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+        "output": "AION User Guide\n\n(Release 2.8.5)\n\nCopyright Notice\n\nThe information contained in this document is the property of HCL Technologies Limited. Except as specifically authorized in writing by HCL Technologies Limited, the holder of this document shall: (1) keep all information contained herein confidential and shall protect same in whole or in part from disclosure and dissemination to all third parties and, (2) use same for operating and maintenance purposes only. \n\n\u00a9Copyright 2018-2023 HCL Technologies Ltd \u2013 All rights reserved.\n\n\n\n\n\n\nIntroduction\n\n\n\nAION (Artificial Intelligence ON) is an AI life cycle management platform for applying machine learning to real-world problems. AION encompasses the complete pipeline from raw data ingestion to a deployable machine learning model with a less-code/no-code approach. It includes the following sub-processes:\n\nAutoML- Enable business user-generated ML models with no code approach\n\nMLOPs - Model Versioning, Deployment & Drift Monitoring\n\nMLaC \u2013 Machine Learning as Code for automatic pipeline code generation\n\nExplainability- Higher accuracy predictions with supported explanations & model confidence \n\nIn AION, all these processes are automated. When you give input data and the use case that needs to be solved, AION runs through all the stages of data analytics cycle and generates the best deployment model.\n\nAION Engines\n\nAION has a multi-stage pipeline as depicted below in the figure. A brief explanation of each stage is given below:\n\nINGESTOR- Data Ingestion: In this stage, the dataset is uploaded in AION GUI from disparate sources.\n\nEXPLORER \u2013 Exploratory Data Analysis: This shows the details about the nature of data, the relation between features, model statistics, and performance information to derive descriptive insights.\n\nTRANSFORMER- Data Processing: In this stage data-cleaning, data preparation, and outlier detectionares did automatically to improve the quality of data for better model accuracy.",
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+        "output": "SELECTOR- Feature Selection: The statistical analysis takes place to identify the relevant features for model training and remove the unimportant features based on correlation & importance.\n\nLEARNER- Model Training Hyper Parameter Tuning: This stage trains configured models and selects the best parameters based on hyperparameter tuning (using which the models are trained). A broad spectrum of algorithms is supported.\n\nPREDICTOR- Inference service: ML Model serving & inference services.\n\nOBSERVER \u2013 Model Monitoring: It monitors the model for input & output drift of data or predictions. \n\nEXPLAINER- Explainable AI: It explains the model & Uncertainty quantification of the prediction.\n\nCONVERTOR- Model Conversion for Edge Device: The generated models can be converted to any desired format that is suitable for embedded, cloud or edge deployments. \n\nTESTER- Model Testing: Once the models are generated, it can be tested using different testing methodologies.\n\n\n\nCODER- Machine Learning as Code: It creates Python code automatically for ML pipeline components.\n\nLaunching AION\n\nAION Explorer is the application used to launch the AION GUI. Followings steps are followed to lunch the GUI.\n\nDouble click on Explorer  icon on desktop to launches the AION GUI.\n\n AION explorer shell will pop up loading all the requirements.\n\nAfter a while GUI landing page will appear in a browser as shown in the figure below. Users need to reload in case the GUI main page does not appear.\n\n\n\nNote:\n\n Access to Hyperscale like AWS, GCP, Azure and data sources like graviton and NiFi will not be possible on HCL network.\n\nTo enable access user needs to raise service exchange ticket and raise access to this source.\n\nCompatibility with previous version:",
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+        "output": "Compatibility with previous version:\n\nNote: Note: if a previous version of AION is already installed, re-training of use cases of that version is possible in the latest version, and it is backward compatible. But, use cases created in the latest version may not run the previous version, as forward compatibility is not supported in all cases\n\nModel Management\n\nCreate Usecase\n\nTo create a new usecase, follow the steps given below:\n\nIn the left panel, click Home. \n\nClick Add Use Case tab, enter the Use case name or keep it default and its Description. \n\nNote: Use case name does not support any special characters or space. \n\nClick Submit.\n\nAs a result, the usecase is added in the USE CASE LIST. All the usecases listed in the USE CASE LIST can be viewed as shown in the figure below. \n\nCompute Infrastructure\n\nUsers can select following computer infrastructure for training:\n\nLocal\n\nAWS EC2 (AION)\n\nAWS EC2 (LLaMA-7B)\n\nGCP GCE (LLaMA-13B)\n\nClick on the Compute Infrastructure tab as shown below. \n\nAWS EC2 (AION): User can use the Amazon AWS EC2 infrastructure with default settings or can configure the details from settings tab as shown below.\n\n\n\nAWS EC2 (LLaMA-7B) and GCP GCE (LLaMA-13B) for LLM fine tuning.\n\nView Existing Models\n\nTo view the existing models, follow the steps given below:\n\nIn the left panel, click Home.\n\nThe existing use cases and their training status can be seen.\n\nTrained models can be lunched for Retaining, Prediction, Trusted AI parameters and Drift Analysis. \n\nFor multiple versions of use case, click on use case name to view different model versions that are trained by Model Retraining. Eg: By clicking on AI00053_lanquos, different versions under that Use Case can be viewed as shown in the figure below.\n\nPublish Model \n\nTrained model can be published in the form of following packages: \n\nMLaC\n\nFederated Learning\n\nPython Package\n\nHomomorphic Encryption\n\nModel Container\n\nRetrained Model",
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+        "output": "Model Container\n\nRetrained Model\n\nMLaC: Machine Learning as Code generates code automatically based on ML Operations performed during various ML Lifecycle. Using MLaC, expert data scientists can have better control over experimentation, optimization & deployment of ML Models.\n\nMLaC has four unique components. \n\nGenerate Code: Generates the code for each pipeline component of the selected use case i.e., data transformations, feature engineering, model training and prediction. This code can further be used to retrain the model on the latest data and can also be used to create a container for each component. The user will find the code in the local system through path: C:\\Users\\yashaswini.ragi\\AppData\\Local\\HCLT\\AION\\target\\AION_375_1\\publish\\MLaC\n\nGenerate Container (Local):\n\nPre-requisite:\n\n Local Machine should have docker installed.\n\nCreate docker images for each of the components by running the docker file present in each component.\n\n Once the docker images are ready to consume, create the container from the images and run that container sequentially as mentioned below:\n\nModel Monitoring > Data Ingestion > Data Transformation > Feature Engineering > Model Training > Model Registry > Model Serving\n\nUser can see the status of each step through the shell as shown below.\n\nExec Pipeline (Local):\u00a0Another way to run this component without using docker is to run the command run_pipeline.py in aion shell.\n\nThe command will execute each component one by one locally in the user system. \n\n\n\nExec Kubeflow pipeline: Using Kubeflow user can run all the components as a single pipeline, which is managed by Kubeflow internally.\n\nGitHub Upload:\u00a0Upload the Generated Code to the GitHub repository.\n\nMLaC service can be used for prediction through PostMan following the steps given below-\n\nOpen PostMan desktop app.\n\nConfigure a POST request with the predict API of the form http://<localhost:port>//AION//<usecasename>/predict  \n\nEg: http://127.0.0.1:8094//AION/AI0192/predict.\n\nSelect authorization as Basic Authorization",
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+        "output": "Select authorization as Basic Authorization\n\nUsername: USERNAME\n\nPassword: PASSWORD\n\nPut the data in the Body as a raw json format. \n\nClick send to perform prediction.\n\nNote: For more information users can follow the user guide for MLaC by clicking on the icon on the top right corner of the pop-up window.\n\n\n\nFederated Learning (Beta): Federated learning is a way to train AI models without exposing the data, offering a way to unlock information to feed new AI applications. Federated learning (or collaborative learning) is an approach used to train a decentralized machine learning model across multiple partitions. The federated learning participants can be edge devices, data centers, smartphones, medical wearables, IoT devices, vehicles, etc. They collaboratively train a shared model while keeping the training data locally without exchanging it. Each participant downloads the model from the server. They train it on their private data, then summarize and encrypt the model\u2019s new configuration. The model updates are sent back to the cloud, decrypted, averaged, and integrated into the centralized model. Then, this centralized model is once again shared with participants for the next round of federated learning. Iteration after iteration, the collaborative training continues until the model is fully trained. In AION, Federated Learning is supported for the following algorithms: Logistic Regression, Linear Regression, and Simple Neural Network (SNN). \n\nPython Package: Python Package creates a WHL File of a trained model so that the user can further use that file in testing and prediction. If the user wants to consume the trained model for prediction in some system where AION is not there, then either the user can install a python package or use the docker images.\n\nPython package can be consumed by following the steps:\n\nClick on the python package icon from the packages tab and as a result whl file will get download.\n\nOpen the command prompt and type the command: python -m pip install whl_file_path and press Enter.",
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+        "output": "Model container: The model container creates a docker image of the trained model so that user can use that image anywhere, pull the image, and perform testing. If the user wants to consume the trained model for prediction purpose in some system where AION is not there, then either the user can install it as python package or use the docker images. \n\nThere are two cased to create a docker container:\n\nWhen the Docker is installed in the machine user can click on the docker and it gets created. \n\nIn case Docker is not installed, running aion will throw an error. For that user can copy the mentioned path file and put in other machine where the docker should be installed and can build the image there also using docker build command.\n\nThe model container also supports CORS (Cross-Origin Resource Sharing) protocol to mitigate the default security policy, Same-Origin Policy (SOP) used by the browsers to protect resources. The same-origin policy is a critical security mechanism that restricts a documents or script loaded by one origin can interact with a resource from another origin.\n\n\n\nCommand to enable CORS: python aion_service.py ip 0.0.0.0 -p 80 -cors \u201chttp://localhost:5000\u201d\n\nHomomorphic Encryption:  Homomorphic Encryption (HE) combines data encryption with privacy protection. A homomorphic cryptosystem is like other forms of public encryption in that it uses a public key to encrypt data and allows only the individual with the matching private key to access its unencrypted data. However, what sets it apart from other forms of encryption is that it uses an algebraic system to allow a variety of computations on the encrypted data. AION Supports only the XGBoost algorithm.\n\nTo publish the trained model, go to the Packages tab and click on the icons of above-mentioned packages accordingly. \n\nUsers can select any model or any version of that model for publishing so that it can be installed on the system for faster inference.",
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+        "output": "Eg: If an user clicks on Python Package icon from the Packages tab, a pop-up window will appear to confirm the model download. Click Ok and then the model gets downloaded in the form of a WHL file which can be used for further testing and prediction.\n\nModel Re-training\u00a0\n\nAfter the Model got trained go to the Home.\n\nFrom the Train column click on the Model Re-training icon to re-train the model withing Re-Configuring.\n\nFor Re-Configuring and training click on the icon.\n\nClick On EDA if required or click on Next. \n\nSet the Basic and Advance configuration if required.\n\nClick Train Model tab to train the model.\n\n\n\nModel will get Re-trained successfully and user can see the results as shown below.\n\nSystem Setting\n\nTo configure and change system settings, \n\nIn the left panel, click the Settings icon. \n\nUser can view the settings and configure them if required.\n\nConfigurations for the following integration entities are available:\n\nConfiguration Settings: \n\nApache Kafka Configuration: Kafka cluster by default will be running in the local host. Kafka uses key-value pairs in the\u00a0property file format\u00a0for configuration. The default values of Kafka cluster ip and kafka cluster port are localhost and 6000 respectively. The value of Kafka Topic can be supplied by the user as per the Kafka configuration.\n\nOpen AI Configuration: For open AI related task Open API key, Open API Base, Open API Type, and Open API Version need to be given by the user.\n\n\n\nCloud Storage Settings:\n\nAWS S3 Bucket\n\nGCP Bucket\n\nAzure Storage\n\nCredentials need to be given for accessing AWS, GCP and Azure Storage by clinking on add bucket and data hence data can be uploaded from the cloud in GUI.\n\nCloud Infrastructure Settings: The user can configure the details of AWS EC2 compute infrastructure to consume AWS EC2 infrastructure.\n\nModel Training\n\n\n\nThe Model Training tab is used to train the model based on the data input file.\n\nData Upload \n\nData upload tab is used to load the dataset in the GUI to train the model and get insight of it.",
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+        "output": "While uploading the dataset, data size and ram size need to be checked in two cases:\n\nIf file size > ram size, file will not be uploaded for further process. File size should not be more than 80% of ram size.\n\nIf the file size > 50% of ram size than the alert will be shown and proceed for the next step. \n\nStructured Data\n\nTo upload the data file, follow the steps given below:\n\nCreate use case.\n\nClick the Data Upload tab.\n\nFile like csv, tar and zip file can be uploaded from local, URL, NiFi, GCP Storage, Azure Storage, AWS S3 Bucket, and Graviton. Credentials might be required to ingest data form all the cloud storages. \n\nAs we Submit, the file will be uploaded successfully.\n\nGraviton: It enables implementation of the modern data platform using polyglot data technologies, open-source data frameworks and cloud-native services. It facilitates the development and delivery of optimal data products and efficient management of data product lifecycle from development to production.\n\nUnstructured Data\n\nThis option is used to upload a document.\n\nTo upload a file, follow the steps given below:\n\nClick on the Data Upload tab.\n\nClick Unstructure data.\n\nSelect the type of document you want to upload (txt, log, pdf).\n\nPlace the files in a folder and specify the path in the Folder Path field.\n\n\n\nClick Submit.\n\nAs a result, the files in the folder will be uploaded successfully.\n\nCustom Python Script\n\nThe python script needs to be created by the user, which can be converted into a data file.\n\nTo upload a python script, follow the steps given below:\n\nClick the Data Upload tab.\n\nClick Custom Python Script.\n\nSpecify the python script path.\n\nClick Submit.\n\nAs a result, the file will be uploaded successfully.\n\nExternal Data Sources\n\nUsers can also access data from external databases for model training.\n\nFor external systems follow the following steps:\n\nClick the Data Upload tab.\n\nClick External Data Sources.",
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+        "output": "Click External Data Sources.\n\nSelect different sql databases and data warehouses to ingest data. viz.- SQLite, Microsoft SQL, PostgreSQL, MySQL, Oracle, Google BigQuery, Redshift, Snowflake, Actian.\n\n\n\nTo access data from the database, you need to provide essential credentials like Database Name, Username, Host Port, etc.\n\nIf a single table contains all the data, you can simply put the table name and fetch the table as Data Frame. Otherwise, the user can perform join and selection based on the condition to merge more than one table and finally fetch the data.\u00a0\n\nNote: As of now, this option is to upload data from some external databases and some data warehouses like- SQLite, PostgreSQL, etc. Users can insert a single table as a database. Also, some basic SQL operation like join, and selection based on where is also possible on multiple tables.  Please contact ERS Research in case you wish to enable this option.\n\nSKLearn Data Sources\n\nTo upload a standard sklearn data set, follow the steps given below:\n\nClick on the Data Upload tab.\n\nClick SKLearn Data Sources.\n\nSelect a dataset.\n\nClick Submit.\n\nAs a result, the dataset will be uploaded successfully.\n\nAION Labelled Data\n\nHere the trained unsupervised model can be used as a base model to train the supervised model. The two main significance of using a model within model is to simplify a large, complex model and allow for more advanced use of model\u00a0iterators.\n\nTo use the model chain following steps are followed:\n\n\n\nSubmit new use case \uf0e0 Upload dataset\n\nTrain unsupervised model i.e., clustering \u00a0\n\nCreate a new use case \uf0e0 upload the dataset. \n\nTraining a supervised model i.e., classification, regression, etc.\n\nFrom the drop-down list choose the model as the base model to train the new supervised model.\n\nClick next. \n\nEDA\n\nEDA stands for Exploratory Data Analysis where user can analyze and investigate data sets and summarize their main characteristics, employing data visualization methods.\n\nAfter uploading the data file, you can view the dataset of top 10 records.",
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+        "output": "To see the remaining details, click the EDA button.\n\nOn clicking the EDA button, a window will pop up that enables configuring the features and the sample of data that needed to be selected for EDA.\n\nSelect features and data subsampling size for exploratory data analysis. Finally, click Next.\n\n\n\nData Overview: The summary statistics of the features can be viewed as shown in the figure below.\n\nData Distribution: It shows the data distribution type for each feature of a dataset. \n\nFor numerical data, select the numerical feature and click on Show Data Distribution to see the distribution of data is generated as shown below.\n\nFor text data, select the text feature and click on the Show Data Distribution. Hence, word cloud is generated as shown below. \n\n\n\nMore details can be seen by clicking the icon on the right of the Data Distribution tab. \n\nTimeseries Analysis: Check the dataset Stationarity and Seasonality for each of the feature of the ingested dataset. \n\nFeature Importance: The relevant features depending on the variance ratio can be viewed in the bar chart. The higher the variance ratio more relevant the feature is. For example: From the figure below, the sepal_width is the most significant feature for iris data classification.\n\nCorrelation Analysis: The strength of relationships varying from 0 to 1 among various features can be viewed as shown in the figure below. A value closer to 1 shows that the features are highly correlated and a value close to 0 shows that the features are less correlated. Example: The correlation value of feature row record_dateTime and feature column id is 1 which shows that these two features are highly correlated and considering both the feature is irrelevant. \n\n\n\nUnsupervised Clustering: The unsupervised clustering of the data can be viewed as shown in the figure. Example: From the figure, we observed five clusters are being created and the average value of those clusters for feature retransmission is 5.52, 3.98, 5.23, 4.66 & 5.44.",
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+        "output": "Data Deep Dive: It offers an interface to inspect the correlation between data points for all the features of a dataset. Each item in the visualization represents the data point of an ingested dataset. Clicking on an individual item shows the key pairs that represent the features of that record whose values may be strings or numbers.\n\nData deep dive related\u00a0details\u00a0will appear only when the User clicks the Show Data Deep Dive link to see it.\n\n\n\nNote: If the data ingestion is huge, Data Deep Dive may not be responsive, or data may fail to load.\n\nPair Graph: The variation of two selected features can be compared as shown in the figure below. \n\nFairness Metrics: The bias identification of data can be viewed by the fairness metrics. There are three metrics- Statistical Parity Difference, Disparate Impact and Theil Index to check for unwanted bias in the dataset. Both are measures of discrimination (i.e., deviation from fairness).\n\nStatistical Parity measures the difference that the majority and protected classes receive a favorable outcome. This measure must be equal to 0 to be fair.\n\n\n\nDisparate impact compares the proportion of individuals that receive a favorable outcome for two groups, a majority group, and a minority group. This measure must be equal to 1 to be fair.\n\nSelect the feature (Categorical) and target feature and then click Show Metrics to analyze the potential bias between the selected feature and the target variable.\n\nTheil Index measures\u00a0inequality among individuals in the group for a classification problem type. Ideally it should be zero for perfect fairness and biasness increase with increase in value greater than zero.\n\nNote: The acceptable threshold is highlighted in green. A class is considered biased if the metrics score is outside the threshold range. \n\n\n\nBasic Configuration\n\nThis section provides the option to select more than one algorithm based on problem type and also provides the option to discard the unnecessary features from the dataset.\n\nProblem Description\n\nModel features",
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+        "output": "Problem Description\n\nModel features\n\nFilter and Grouping\n\nProblem Description\n\nSelect the Problem Type. The following problem types are supported:\n\nClassification\n\nRegression\n\nTime Series Forecasting\n\nTime Series Anomaly Detection\n\nRecommender System\n\nClustering\n\nAnomaly Detection\n\nSurvival Analysis\n\nTopic Modeling\n\nState Transition\n\nDocument Similarity \n\nText Summarization [beta]\n\nNote: By default, classification is selected.\n\n\n\nFor AWS EC2 (LLaMA 7B) and GCP GCE (LLaMA 13B) computer infrastructure, problem type Large Language Models-Tuning is available.\n\nSelect the Scoring Criteria.\n\nBased on the problem Type, select the Algorithms.\n\nSelect the Online Learning (Beta) or Distributed learning (Beta) check box for Incremental Learning. Only Regression and Classification problem types support Incremental learning. For more details, please refer to the sections later these topics.\n\nFeatures Configuration\n\nThis section provides the option to select features that will be used for training, options to change the data type and transform each selected feature. Note that the target feature is not considered a training feature.\n\n\n\nIn this section, the user can see the list of features with prefilled default values.\n\nType of feature can be selected as date, index, categorical, numerical, or text.\n\nSix fill method is supported with the default value for numerical being Median and for Categorical is Mode.\n\nUser can select different Encoding techniques for different features.\n\nOutlier and Normalization can be selected from the list of given options. \n\nFinally mention the Target Features (for supervised learning). For some of the algorithms like Document similarity and clustering, the target feature is not applicable.\n\nTime Series Settings\n\nTime Series Settings is for time series problem type, that allows to set the parameters like- Interpolation, Rolling Window, Aggregation, Granulatity, etc.\n\nRow Filtering and Grouping\n\nThis section handles the row filtering. If filtering is not required, this section can be skipped.",
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+        "output": "Clicking on the Next button will save the configuration and moves it to Advanced Configuration Tab.\n\nAION Online Learning (Beta)\n\nOnline learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e., to further train the model even after the first fit. In AION, support for incremental learning is provided for regression and classification tasks. Every Online learning use case has two major components, \n\nOnline data profiling - River and Sklearn\n\nOnline model training \u2013 Sklearn\n\n\n\nSince both profiler and ml models must be continuously updated, the models are pickled and saved in the usecase folder for further use. Note that hyperparameter tuning can be done only in the first fit of data and for all the remaining incremental addition of data the selected best hyperparameters from the first fit are used. \n\nInstructions for creating a usecase:\n\nSTEP 1: In the Basic, Config page click on the Online Learning checkbox and select the required options.\n\nSTEP 2: Select the required data profiling options \u2013 \n\nNumerical fill \u2013 The method by which missing values in numerical features are filled\n\nMean \u2013 Running Mean\n\nMin \u2013 Running Minimum\n\nMax \u2013 Running Maximum\n\nDrop \u2013 Drop the missing value records\n\nZero \u2013 Fill with 0\n\nCategorical fill \u2013 The method by which missing values in categorical features are filled\n\nMode \u2013 Running Mode\n\n\n\nDrop \u2013 Drop the missing value records\n\nZero \u2013 Fill with 0\n\nCategory encoding \u2013 The method by which categorical features are to be numerically encoded. Only Target encoding is supported.\n\nTarget encoding params \u2013 The statistics to be used for encoding the target column into a categorical column are:\n\nMean \u2013 Mean of the target group concerning the category is computed\n\nBayesian Mean \u2013 A smoothed mean statistic to prevent data leakage while encoding\n\nOutlier detection \u2013 The method to use to remove outliers from training data are:\n\nOne class SVM with a quantile filter\n\nHalf space trees with quantile filter",
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+        "output": "Half space trees with quantile filter\n\nOther profiling options are the same as in regular AION profiler\n\nSTEP 3:  Train the model\n\nNow a usecase folder will be created in the target folder. All the profiler models and the best ML model are present in the Production folder\n\nPath: C:\\Users\\<username>\\AppData\\Local\\HCLT\\AION\\target\\<usecasename> \\Production\n\nSTEP 4: Incremental addition of new data to the existing model\n\nWhen new data is fed, the older model is archived in archives and the updated model is stored in the production folder.\n\nPath: C:\\Users\\<username>\\AppData\\Local\\HCLT\\AION\\target\\<usecasename> \\archives\n\nThe next fit or incremental fit logs are appended to the main model training logs. Users can check what profiling steps are applied and the model scores at each partial fit in the appended logs.\n\n After training the model with the first dataset, click the online batch learning icon from the Train tab for incremental learning. \n\nPop-up window will appear to enter the incremental data (second or third dataset) location consisting of similar characteristics.\n\n\n\nClick on submit button. \n\nAccuracy results will pop up on the window with previous and current accuracy.\n\nSTEP 5: Prediction of test data from the current production model \n\nClick on Predict icon from the predict tab.\n\nPredictions can be either from a single instance (Predict Instance) or from a file (Predict Batch).\n\nClick Predict or Submit for successful prediction.\n\nLimitations of Online Learning (Beta)\n\nFor the classification task, the user is expected to not add new classes of the target variable in the next batch of data.\n\nEnsure that the next batch of data does not miss out any columns from previous data columns.\n\nText features are not supported.\n\n\n\nOnly Model training and prediction are supported. \n\nDistributed Learning (Beta)",
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+        "output": "Distributed Learning (Beta)\n\nDistributed machine learning refers to multi-node/multi-processor machine learning algorithms and systems that are designed to improve performance, increase accuracy, and scale to larger input data sizes. In AION, distributed learning is supported for classification, and regression with XGBoost and LightGBM. \n\nCharacteristics of Distributed learning:\n\nDistributed data pre-processing and EDA: AION support loading, preprocessing, and analyzing a large dataset\u00a0that exceed the memory limit of host machine.\n\nProvide advanced configuration for the user to easily configure the distributed parameters as well as model hyper-parameters.\n\nDistributed model training: Use the native XGBoost and LightGBM library for training and evaluating the model.\n\nInstructions for creating a use case\n\nSTEP 1: In the Basic, Config page click on the Distributed Learning checkbox and select the algorithm. Distributed learning supports both classification and regression problems for algorithms- XGBoost and LightGBM.\n\nSTEP 2: Select the model features and target features. \n\n\n\nSTEP 3: Click the advance configuration tab to set the advance configuration or else click Next to train the model. \n\nSTEP 4: Click the Train Model tab to train the model successfully.\n\nFastText Support \n\nFor Document Similarity problem if FastText algorithm is selected than the user needs to be installed FastText pretrained model manually. \n\nFollowing are the steps to install the same:\n\nDownload and install the Microsoft C++ Build Tools through link: https://visualstudio.microsoft.com/visual-cpp-build-tools\n\nOpen the AION shell.\n\nInstall fastText by using the command below-\n\nCommand: python -m pip install fastText.\n\nor\n\nWhen it throws an error,\n\nDownload whl package from link: https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext\n\nCommand: python -m pip install (Path of downloaded whl package)\n\nVectorDB Cosine\n\n\n\nFor VectorDB cosine user needs to install the chromadb manually. \n\nFollowing are the steps to install the same:",
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+    },
+    {
+        "output": "Following are the steps to install the same:\n\nDownload and install the Microsoft C++ Build Tools through link: https://visualstudio.microsoft.com/visual-cpp-build-tools\n\nOpen the AION shell.\n\nInstall chromadb by using the command below-\n\nCommand: python -m pip install chromadb\n\nAdvance Configuration\n\nTo update the advanced configuration, follow the steps given below:\n\nFollowed by data uploading and basic configuration click on the Advance Configuration tab. Configuration can be done for Data Filter, Text Transformations, and Feature Engineering, Machine Learning. Click on plus (+) icon for detail configuration. \n\nBy default, the fields are pre-filled. Change the fields if required.\n\n\n\nClick Next and as a result, the advance configuration details are saved.\n\nText embedding\n\nText embedding measures the relatedness of text strings. Also, it has an ability to capture semantic meanings and relationships between words or phrases which enables machines to understand and process human language efficiently. Popular models to perform text embeddings are- TF_IDF, OpenAI, MiniLM-L6-v2 and Mpnet-base-v2.\n\nModel Training\n\nModel training tab gives the information about the Problem type, Selected algorithms, no of rows and Training Features, Training Data, Data balancing and Number of iterations. The data balancing is applicable only for classification problem. The percentage of test data can be changed accordingly by the user. Number of iterations reflects the size of hyper-parameter space selected by the user. \n\nClick Train Model tab to train the model. \n\n\n\nTraining can be aborted if required by clicking Stop Training tab.\n\nModel training can also be performed by using API which is explained in detail in the section 5.1.\n\nTrained Model Details\n\nAfter successful training, tained model details can be viewed. \n\nTrained Model Info",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Trained Model Info\n\nIt gives an information about- Training status, Deployment location of a model, problem type, best model (in case multiple algorithms is selected), Score of a best model, feature used, and the reports can be downloaded by clicking on Download Reports tab.\n\nEvaluated Models\n\n\n\nEvaluated models gives an information about the model performance based on testing score, Confidence Score and Feature Engineering Method. For classification and regression performance of seclected model is compared with the basic model such as logistic regression and linear regression respectively. \n\nEg: When user selects Extreme Gradient Boosting (XGBoost) model for classification, its performance will be compared with Logistic regression.\n\nTraining Data evaluation\n\nIt gives an information about training data as shown in the figure below.\n\nTest data evaluation\n\nIt gives an information about test data as shown in the figure below.\n\n\n\nPerformance\n\nPerformance tab shows the graph of True Positive Rate vs False Positive Rate and Precision vs Recall, giving an idea about the model performance. \n\nPerformance Leaderboard\n\nPerformance leaderboard gives an idea about the best performing algorithm when multiple algorithms are selected based on its best score. Best performing algorithms appear at the first row followed by least performing algorithms. It supports only machine learning algorithms for classification and regression problem types.\n\nTo view the leaderboard, click on the icon  from the right corner of the model training window.\n\n\n\nLeaderboard will appear as shown in the figure below.\n\nModel Prediction\n\nModel prediction predicts the input data based on the pattern of training data.\n\nPredict Instance\n\nTo perform the Single Instance prediction, follow the steps given below:\n\nClick on the Predict icon.\n\nIf necessary, change feature values.\n\nClick Predict to obtain the prediction result.\n\n\n\nPredict Batch\n\nTo perform the prediction from the file, follow the steps given below:\n\nClick on the Predict icon.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click on the Predict icon.\n\nClick Predict Batch tab.\n\nUpload the dataset.\n\nClick Submit to view the prediction result for the file selected.\n\nPrediction can also be performed by using API which is explained in detail in the section 5.2.\n\nGenerate Script\n\nThis will generate the sample python code to call rest API for prediction.\n\nNote: Prediction Service in AION is only for testing purposes and not for performance. For performance, the prediction service must be published.\n\nTrusted AI\n\nTrusted AI provides an integrated approach to evaluate, quantify, and monitor the impact and trustworthiness of a models.\n\nFairness Metrics\n\nFairness metrics are a set of measures that enables the user to detect the presence of bias in model.\n\nTheil Index measures\u00a0inequality among individuals in the group for a classification problem type. Value of Theil Index is always greater than zero. Ideally it should be zero for perfect fairness and biasness increase with increase in value higher than zero.\n\n\n\nEqual Opportunity Difference measure the difference in true positive rate between privileged and unprivileged group of protected features for a classification problem type. Ideally it should be zero to be fair. A value less than zero illustrates a higher benefit for the privileged group and positive value is a sign of higher benefit for the unprivileged group.\n\nStatistical Parity Difference measure the difference in the rate of favorable outcomes between privileged and unprivileged groups. This measure must be equal to 0 to be fair.\n\nDisparate impact ratio measures the ratio of rate of favorable outcomes for the underprivileged group to that of privileged group. This measure must be equal to 0 to be fair.\n\nSteps: \n\nClick on trusted AI icon  under predict tab from the home page.\n\nSelect protected feature from drop down column \u2018Check Bias For\u2019. Target feature gets selected automatically which is selected while training the model.\n\nSelect the graph type.\n\nClick Show Metric tab to view the graph.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click Show Metric tab to view the graph.\n\nNote: The acceptable threshold is highlighted in green. A class is considered biased if metrics-score is outside the threshold range.\n\nPerformance\n\nModel performance is an assessment of the model's ability to perform a task accurately not only with training data but also in real-time with runtime data when the model is deployed.\n\nMatthews correlation coefficient (MCC) is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction.\n\nTransparency\n\nTransparency helps users to understand the input data, algorithm and expected output of an ML model.\n\nBrier Score measures the mean of squared difference between the predicted probability between the model and the ground truth values of a target feature. \n\nExplainability\n\nExplainability tab gives an explanation about Prediction and Model. \n\nXplain Prediction\n\nXplain Prediction explain the details about prediction concerning its Precision, confidence, reason, along with force plot and decision plot.\n\nSteps:\n\nClick on Trusted AI  icon. \n\n\n\nClick Xplain Prediction.\n\nClick Xplain. \n\nDetailed Result will be displayed as shown below.  \n\nXplain Model\n\nXplain Model gives the insights of input data and explain the details of its important features. \n\nSteps:\n\nClick on Trusted AI  icon.\n\n\n\nClick Xplain Model tab.\n\nClick Get Model Details.\n\nThe details of the model explanation will be displayed. Scroll down for more details. \n\n\n\nUser can DOWNLOAD REPORTS if required.\n\nRobustness\n\nRobustness of AI/ML models is analyzed by Sensitivity Analysis examining the extent to which the results are affected by changes in input data.      \t\nWe have two different options to perform sensitivity analysis to understand the influencing factors in more details.\n\nFirst Order Sensitivity Analysis",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "First Order Sensitivity Analysis\n\nFirst Order Sensitivity Analysis quantifies the impact of individual input variables on the output of a model by systematically varying one variable at a time while keeping others constant. It helps to understand the most influential factors of a model.\n\n\n\nTotal Order Sensitivity Analysis\n\nTotal Order Sensitivity Analysis evaluates the influence of individual input variables as well as their interactions on the output of a model. It measures the relative importance of the variable considering both their direct and indirect impact, providing understanding of their influence on the model prediction. It helps user to understand the most critical factors of a model. \n\nModel Monitoring\n\nIt entails monitoring ML models for changes in data distribution ensuring that data is maintaining an acceptable level of variation. Two types of drift- Input drift and Performance drift can be done in AION.\n\nStep: In the Home tab go to the Monitor column and click on Model drift icon.\n\nInput Drift\n\nInput drift gives idea about the data distribution between the new dataset and the old dataset that is used to train the model. In case the data maintains acceptable level of variation no further training is required to re-use the model. However, for unacceptable range of variation in data distribution model need to be retrained with the new dataset.\n\nTo monitor the training data distribution, follow the steps given below:\n\n\n\nClick on Drift icon. \n\nClick Input for input drift analysis.\n\nEnter the current data file path. \n\nClick submit and as a result, the Distribution details will be displayed.\n\nClick on New data distribution and graph will be shown.\n\nClick on Drift details for more details.\n\n\n\nPerformance Drift\n\nPerformance drift compares the data distribution between the output data results and the label of input data which gives an idea about the model performance.\n\nTo monitor the training data distribution, steps like input drift is followed:\n\nClick on Input Drift icon.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click on Input Drift icon.\n\nClick Performance for Performance Drift Analysis.\n\nEnter the current data file path from Local or URL.\n\n\n\nClick on Submit button.\n\nConsumable API \n\nAPI stands for \u201cApplication Programming Interface\u201d. An API is\u00a0a software intermediary that allows AION to communicate with other applications to perform certain task like- model prediction, model drift analysis, etc.\n\nUsers can consume API in the form of Model Training, Model Prediction, Model Drift analysis, and Explanation.\n\nModel Training\n\nModel training can be done by two methods, either with the script or from the command prompt.\n\nScript:\n------\nimport sys\nfrom AION import aion_train_model\nif __name__ == \"__main__\":\n\u00a0\u00a0\u00a0 status = aion_train_model(sys.argv[1])\n\u00a0\u00a0\u00a0 print(status)\n---------------\u00a0\u00a0\u00a0\n\n\n\n\nCommand Prompt:\n---------------\npython bin\\run_aion.py config_file.json\n\nSome changes in the config file that the user need to make for different use cases:\n\nBasic configuration:\n\nThe fields that need to be changed in the basic configuration are:\n\nData location: Specify the path of the data in the \u201cdata location\u201d field.\n\nDeployment location: Specify the path where the model needs to be deployed in the \u201cdeploy location\u201d field.\n\nAnalysis type: Select the problem type by turning it to be \u201cTrue\u201d. \n\nAlgorithms: Similarly, select the algorithms.\n\nScoring criteria: Scoring criteria can be selected depending on the problem type.\n\n\n\nFeature selection: The user can select the date-time feature, index feature, training features, and target features accordingly. \n\nAdvance Configuration\n\nUsers can keep the pre-defined values, or they can be changed accordingly.\n\nModel Prediction\n\nDifferent ways to call the predict.\n\nCommand Line:\n\nClick AION_Shell.bat\n\nGo to the absolute path of the trained model\n\nCall aion_predict.py\n\npython aion_predict.py argument\n\ncan be any one of the following\n\nJSON Data\n\nJSON File\n\nCSV File\n\nOutput: JSON\n\nUsing Wheel Package:\n\nGo to the absolute path of trained model\n\nGo to directory \u201cWHEEL file\u201d",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Go to directory \u201cWHEEL file\u201d\n\nInstall the whl package using the command:\n\npython -m pip install usecasename_version-py3-none-any.whl\n\nAdd the following code:\n\n\n\nfrom {packageName} import aion_predict as p1 p1.predict({json_data})\n\nUsing Rest API\n\nMake Sure the AION_Service.bat is running\n\nClick on the launch button of trained model from AION UI.\n\nDo the prediction using AION UI\n\nNow you can even call the rest API and check the prediction output\n\nCall get request: http://127.0.0.1:8091/AION/AION_38_1\n\nWhere AI00038 is usecase name and 1 is version \n\n\n\nOutput:\n\nFollowing URL:\n\nPrediction: \tURL: /AION/AION_38_1/predict\n\n \t\tRequestType: POST Content-Type=application/json\n\nBody: {\"retransmission\": \"Numeric Value\", \"signalStrength\": \"Numeric Value\", \"bytesReceived\": \"Numeric Value\", \"bytesSent\": \"Numeric Value\", \"errorReceived\": \"Numeric Value\", \"errorSent\": \"Numeric Value\", \"Noise\": \"Numeric Value\"}\n\nOutput: prediction,probability(if Applicable),remarks corresponding to each row.\n\nLocal Explanation: \n\nURL: /AION/AION_38_1/explain \n\nRequestType: POSTContent-Type=application/json\n\nBody: {\"retransmission\": \"Numeric Value\", \"signalStrength\": \"Numeric Value\", \"bytesReceived\": \"Numeric Value\", \"bytesSent\": \"Numeric Value\", \"errorReceived\": \"Numeric Value\", \"errorSent\": \"Numeric Value\", \"Noise\": \"Numeric Value\"}\n\nOutput: anchor (Local Explanation),prediction,forceplot,multidecisionplot. Features\n\nFeatures:\tURL:/AION/AION_38_1/features\n\n RequestType: POSTContent-Type=application/json\n\nOutput: {\"status\": \"SUCCESS\", \"features\": [{\"feature\": \"QoS\", \"Type\": \"Target\",\n\n\"values\": \"\"}, {\"feature\": \"retransmission\", \"Type\": \"Numeric\"}, {\"feature\": \"signalStrength\", \"Type\": \"Numeric\"}, {\"feature\": \"bytesReceived\", \"Type\": \"Numeric\"}, {\"feature\": \"bytesSent\", \"Type\": \"Numeric\"}, {\"feature\": \"errorReceived\",\n\n\"Type\": \"Numeric\"}, {\"feature\": \"errorSent\", \"Type\": \"Numeric\"}, {\"feature\": \"Noise\", \"Type\": \"Numeric\"}]}.\n\nMonitoring: \tURL:/AION/AION_38_1/monitoring\n\n RequestType: POST Content-Type=application/json",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "RequestType: POST Content-Type=application/json\n\nBody:\t{\"trainingDataLocation\":\t\"Reference\tData\tFile\tPath\", \"currentDataLocation\": \"Latest Data File Path\"}\n\nOutput: Affected Columns. HTML File Path.\n\nPerformance: \tURL:/AION/AION_38_1/performance \n\nRequestType: POST Content Type = application/jsonBody : {\"trainingDataLocation\": \"Reference DataFilePath\",\"currentDataLocation\":\"Latest Data File Path\"} Output: HTML File Path.\n\nUsers can call any of the above APIs for respective functionality i.e, predict, explain, monitor, performance, and features.\n\nUtility\n\nIn the left panel click on Utility Tab for Data Labelling, Data Generator, MLLite, MLTest, MLflow Tracking, Summarization, Text Data Labelling, LLM Testing, Code Clone Detection and QnA Generator. \n\nData labelling (Beta)\n\nData labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor.\n\nKey benefits:\n\nTime savings: Over 80% of the time spent in AI projects goes into preparing, cleaning, and Labelling data. Automated labelling allows Data scientists to save precious time. \n\nIncreased Accuracy and Quality of training Data set:\n\nLabelling allows users to incorporate different factors influencing the result, allowing for an increase in accuracy and Quality of training Data set.\n\nBetter Results:\n\nEffective and accurate Labelling Ensures Better Results as AI/ML models can present the most fitting results for different Scenarios.\n\nSTEPS: \n\nIn Data Location upload a file (CSV, JSON, TSV) in these formats and click the Next button.\n\n\n\nIN Define Label assign Condition Label as red or green and weightage between (0-10). Click on the Next button. \n\nNote: It is required to add at least 2 condition Label",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "In Define Label add Rule, condition, and value as shown in the figure below. Click on the Next button.  \n\nNote:  It is required to add at least 3 Rules to label dataset.\n\nIn Labelled Dataset user will get Model Summary. \n\n\n\nAnd user can download fully Labelled Dataset as shown below.\n\nData Generator\n\nData generation tool is used to generating artificial datasets with configurable size and complexity. The tool currently supports classification, regression and timeseries problem type.\u00a0\n\nClick on Data > Data generator > Fill the configuration based on problem type which is given in the section below.\n\nConfiguration of Classification data\n\nnumber_samples: Number of samples (records) in dummy dataset (default is 100)\u00a0\n\nnumber_numerical_features: Number of numerical features in dummy dataset (default is 25)\u00a0\n\nnumber_categorical_features: Number of categorical features in dummy dataset (default is 2)\u00a0\n\nnumber_text_features: Number of text features in dummy dataset (default is 2)\u00a0\n\nmissing_proportion: Create records with missing values based on a predefined proportion. For example: if there are 100 records in the dataset, and missing proportion is 0.1, number of missing records will be 100*0.1=10 (default is 0.1)\u00a0\n\nnumber_informative: Number of informative features. (Default is 20, i.e out of 25 total number features, only 20 features are useful of the model, and the other 5 are uninformative or redundant.)\u00a0\n\nnumber_class: Number of classes for classification problem (default is 2)\u00a0\n\nweights: The proportions of samples assigned to each class. If None, then classes are balanced. (Default is [0.4, 0.6] with number_class=2).\u00a0\n\n\n\nshift: Shift features by the specified value (default is 0.0). This value can be a float, or an array with a size equal to number of numerical features.\u00a0\n\nvalue_range_dict: Set the range of values of different features based on features id (default value is {\u201c0\u201d: [1,2]}, meaning that feature with id 0 has the range between 1 and 2. Users can set with multiple feature ids if needed.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Configuration of Regression data\u00a0\n\nnumber_samples: Number of samples (records) in dummy dataset (default=100)\u00a0\n\nnumber_numerical_features: Number of numerical features in dummy dataset (default=25)\u00a0\n\nnumber_categorical_features: Number of categorical features in dummy dataset (default=2)\u00a0\n\nnumber_text_feature: Number of text features in dummy dataset (default is 2)\u00a0\n\nmissing_proportion: Create records with missing values based on a predefined proportion. (Default is 0.1 meaning that if there are 100 records in the dataset, and the missing proportion is 0.1, the number of records with missing value will be 100*0.1=10.)\u00a0\n\nnumber_informative: Number of informative features. (Default is 20, i.e out of 25 total number features, only 20 features are useful of the model, and the other 5 are uninformative or redundant.)\u00a0\n\nnumber_target: Number of target features\u00a0\u00a0\n\nbias: The bias term (or offset/y-intercept) in the underlying linear model (default is 0.0, meaning that no bias term is added up).\u00a0\n\nnoise: The standard deviation of the gaussian noise applied to the dataset (default is 0.0, meaning that there is no noise applied to the dataset).\u00a0\n\nvalue_range_dict: Set the range of values of different features based on features id (default value is {\u201c0\u201d: [1,2]}, meaning that feature with id 0 has the range between 1 and 2. Users can set up multiple feature ids if needed.\u00a0\n\nConfiguration of Time series data\u00a0\n\nnumber_samples: Number of samples (records) in dummy dataset (default=100)\u00a0\n\nnumber_numerical_features: Number of numerical features in dummy dataset (default=25)\u00a0\n\nnumber_categorical_features: Number of categorical features in dummy dataset (default=2)\u00a0\n\nnumber_text_feature: Number of text features in dummy dataset (default is 2)\u00a0\n\nmissing_proportion: Create records with missing values based on a predefined proportion. (Default is 0.1 meaning that if there are 100 records in the dataset, and the missing proportion is 0.1, the number of records with missing value will be 100*0.1=10.)",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "number_informative: Number of informative features. (Default is 20, i.e out of 25 total number features, only 20 features are useful of the model, and the other 5 are uninformative or redundant.)\u00a0\n\nnumber_target: Number of target features\u00a0\u00a0\n\nbias: The bias term (or offset/y-intercept) in the underlying linear model (default is 0.0, meaning that no bias term is added up).\u00a0\n\nnoise: The standard deviation of the gaussian noise applied to the dataset (default is 0.0, meaning that there is no noise applied to the dataset).\u00a0\n\nvalue_range_dict: Set the range of values of different features based on features id (default value is {\u201c0\u201d: [1,2]}, meaning that feature with id 0 has the range between 1 and 2. Users can set up multiple feature ids if needed.\u00a0\n\nUnivariate: Set to True if the user wants to generate univariate time series data. Otherwise, set to False to generate multivariate time series data.\n\nStart Time: Left bound for generating dates.\n\nEnd Time: Right bound for generating dates.\n\nMLTest\n\n\n\nBaseline Testing\n\nAION supports baselining & testing an existing ML model. \n\nClick on MLTest    \n\nModel path: C:\\Users\\yashaswini.ragi\\AppData\\Local\\HCLT\\AION\\target\\AI00335_1\\model\\AI00335_1.sav\n\nData path: C:\\Users\\yashaswini.ragi\\AppData\\Local\\HCLT\\AION\\target\\AI00335_1\\data\\postprocesseddata.csv\n\nTraining features should select from the log file:\n\nC:\\Users\\yashaswini.ragi\\AppData\\Local\\HCLT\\AION\\target\\AI00335_1\\log\n\nSelect the Target feature.\n\nClick on submit Button.\n\nResult will get displayed as below in fig.\n\nIf needed user can save the data to file.\n\nA/B Testing\n\nA/B Testing capability recommends better performing models using other ML algorithms & their corresponding hyper-parameters. AION supports two forms of A/B testing:\n\nA/B testing using a candidate model that is generated with the same algorithm but different hyperparameters.\n\n\n\nA/B testing using a candidate model that is generated with different algorithms\n\nWhen A/B testing is done successfully result will be shown below.\n\nUncertainty Quantification",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Uncertainty Quantification\n\nThe uncertainty Quantification feature gives the user a confidence score of the model under test and recommends how the uncertainty of predictions can be reduced.\n\nSupported Uncertainty quantification testing (MLTest) algorithms are:\n\nClassification:\n\nLogistic regression (Default)\n\nRandom Forest\n\nDecision Tree\n\nSupport Vector Machine (SVM)\n\nKNN\n\nGradient Boosting\n\nRegression:\n\nLinear Regression (Default)\n\nDecision Tree\n\nLasso\n\nRidge\n\nRandom Forest\n\n\n\nSteps:\n\nUncertainty Quantification test is followed by Baseline and A/B testing. \n\nModel path and Data Path will be updated automatically.\n\nResult will display as shown in the figure below.\n\n\n\nMLLite\n\n\n\nModel Converter\n\nThe model converter converts AION-generated models or externally generated models to formats like- ONNX, TFLite, and Sagemaker. \n\nThe input model formats that can be converted to ONNX are:\n\nKeras\n\nSklearn\n\nTensorFlow\n\nTFLite\n\nLight GBM\n\nXGBoost\n\nLibSVM\n\nCoreML\n\nThe input model formats that can be converted to TFLite are:\n\nKeras\n\nTensorFlow\n\nThe input model formats that can be converted to Sagemaker are:\n\nSklearn\n\nONNX and TFLite Runtime\n\nONNX is designed to accelerate machine learning across an extensive range of frameworks, operating systems, and hardware platforms. It facilitates the advancement of machine learning inferencing across all deployment targets using a single set of\u00a0APIs. It automatically parses through the model to identify optimization opportunities and provides access to the best hardware acceleration available.\n\nTFLite is a Compiled TensorFlow lite, a fraction size of full TensorFlow, and requires a bare minimum code to run the inferences. This small package is preferable when the user wants to use minimum disk space which may not be feasible with a large TensorFlow library.\n\n\n\nONNX and TFLite models can be consumed by the user for further prediction using ONNX runtime or TFLite runtime. Steps to perform runtime prediction: \n\nClick MLLite\n\nClick ONNX runtime or TFLite runtime accordingly",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click ONNX runtime or TFLite runtime accordingly\n\nGive model path and data path\n\nEg: Model path: C:\\Users\\<user_name>\\Model\\irisData.onnx\n\n      Data path: C:\\Users\\<user_name>\\input_data \\iris_data.csv\n\nClick submit. By default, GUI will read the first row of the dataset.\n\nClick Runtime Prediction for a successful prediction.\n\nSagemaker Predictions\n\n\n\nAION supports predictions to be made on Sagemaker endpoints. This utility allows users to access model prediction without going out of the AION environment.\n\nSecure API \n\nAION supports authentication of the user with Username-admin and Password-aion in the post request which can be configured under System settings. \n\nPost request is sent with an authorization header along with content type and body. The validation output is shown below. \n\nModel Help\n\nHelp API\n\nAION support model help API for easier understanding of calling model services.\n\nBy going to the URL: http://127.0.0.1:8091/AION/help the following help information is available below:\n\nPrediction help\n\n\n\nExplanation help\n\nFeature help\n\nMonitoring help\n\nPerformance help\n\nHelp for a specific use case can be got similarly:\n\nE.g., for the use case AION_22_2, the following URL gives the help details: http://127.0.0.1:8091/AION/AION_22_2/help\n\nThis help is specific to the model under consideration and provides all the info related to the model and what it can be called.\n\nThis is seen as an example below: \n\nMLflow Tracking\n\n\n\nMLflow Tracking is used for managing workflows and artifacts across the machine learning lifecycle. It has an UI for reviewing and comparing runs and their results.\n\nText Summarization\n\nText Summarization summarize the longer text into concise and coherent summaries using Large Language Model (LLM). It has an ability to understand and generate human-like text, making well suited for text summarization. \n\nStep:\n\nClick on Utility icon .\n\nClick Summarization.\n\nSelect Document type and file type.\n\nEnter the raw text if Raw Text Data is selected or give the file path if File Path is selected.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click Submit. \n\n\n\nUser can modify Keywords and key Attributes for Summarization if required.\n\nClick Submit for final Summarization.\n\nText Data Labelling\n\nText labelling generate a label from the given text using Large Language Model (LLM). The LLM takes the input text data and generate prediction for corresponding label. The label with highest prediction probability can be assigned to text as the final label.\n\nStep:\n\nClick on Utility icon .\n\nClick Text Data Labelling.\n\nUpload the text data in csv format.\n\n\n\nClick Next.\n\nSelect relevant features and click Next for successful data labelling.\n\nLLM Testing\n\nLLM Testing \u00a0helps users to test model robustness with adversarial examples, out-of-distribution inputs, and linguistic variations, to help developers and researchers identify potential weaknesses and improve the performance of their LLMs and NLP solutions.\n\nUser needs to select the parameters shown in table below and click on the Generate Testing Report tab.\n\n\n\nModel Name: Open AI model is powered by diverse set of models with different capabilities and price point. Default is \u2018text-davinci-003\u2019.\n\nSimilarity Threshold: If the similarity- threshold is above 0.75 model generate perturbation compared to reference generation.\n\nPerturbations:\u00a0This is done with the help of another LLM that paraphrases the original prompt but preserves the semantic meaning. The original prompt along with the perturbations are then passed onto the LLM to be evaluated.\n\nPrompt: Reference question asked to LLM model.\n\nReference Generated Answer: Reference answer expected from LLM model.\n\nPrompt Evaluation result will be shown as below.\n\n\n\nNote: For LLM Testing user need to run the commands below:\n\npython -m pip install notebook==6.4.5\t\n\npython -m pip install fiddler-auditor==0.0.2\n\nCode Clone Detection\n\nCode Clone Detection\u00a0locates exact or similar pieces of\u00a0code, known as clones, within or between software source files. Openai based models- GPT 3.5 Turbo and Text-Datvinci-03 are currently available for code clone detection.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Types of code clones are:\n\nExact clone: Two code fragments like each other with little transformation in comments, layout, or whitespaces.\n\nParameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.\n\nNear-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.\n\nSemantic clone: The code snippets have different syntax but with alike functionality results in this clone.\n\nTo access Code Clone detection, please navigate Utilities > Code Clone Detection as below,\n\nOnce user click on  icon, then navigate to main code clone page as below,\n\n\n\nUsers must specify the Root Directory (python files directory) and Code Clone Detection Mode (user needs to be select the radio button). Then finally Submit.\n\nOnce user submit the required details, then AION will use OpenAI to fetch the clones in the given python files as below,\n\nFinally, user can download the log file related to code clone detection.\n\nQnA Generator\n\nQnA Generator generate a set of questions and answer from the given text content. \n\nSteps:\n\nUpload the document in text format.\n\nQuestion and answer will be generated on the basis on given text context as shown in the figure below.\n\nNote:\n\nUsers need to provide openai credentials such as OpenAI API Key, OpenAI API Type, OpenAI API Version and OpenAI API BASE/UR for Text Summarization, Text Data Labelling, LLM Testing, Code clone Detection, and QnA Generator in AION settings page as below.\n\n\n\nLLM Fine Tuning\n\nFor LLM finetuning two models are available: LLaMA-7B and LLaMA-13B. \n\nLLaMA-7B\n\nLLaMA-7B is a family of large language model from meta-AI which contains 7B parameter version available for fine-tunning in AWS machine. If user wants to finetune the LLaMA-7B model, actual fine tuning occurs in AWS machine and user perform inferencing from the local machine.\n\nLLaMA-13B",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "LLaMA-13B\n\nLLaMA-13B is a family of large language model from meta-AI which contains 13B parameter version available for fine-tunning in GCP GCE machine. If user wants to finetune the LLaMA-13B model, actual fine tuning occurs in GCP GCE machine and user perform inferencing from the local machine.\n\nSteps to finetune LLaMA-7B and LLaMA-13B:\u00a0\n\nUser needs to select computer infrastructure, for LLaMA-7B select AWS EC2 (LLaMA-7B) and for LLaMA-13B select GCP GCE (LLaMA-13B).\n\nFrom settings page user need to configure the details of EC2/GCP.\n\nCreate a Usecase, select dataset containing text (dialogue-summary type).\n\nIn Basic configuration page: \n\nProblem Description: \n\nProblem Type: Large Language Models\n\nScoring Criteria: NA\n\nAlgorithm: LLaMA-7B / LLaMA-13B\n\nTuning: Fine Tuning\n\n\n\nFeatures Configuration: \n\nPrompt: the input feature, in case summarization, Dialogue\n\ncontext: \u00a0NA/user specific\n\nOutput: \u00a0output feature, Summary\n\nFriendly Names: User can provide friendly names to prompt and output if the column names are not self-explanatory.\n\nSkip advance configuration and go to training page.\n\nIn Training page, user can choose to keep running or shutdown the VM after Tuning. It is advisable to keep it running if user wants to do inferencing of the finetuned model.\n\nTrain the model and refer the log for finetuning status.\n\nAfter successful training, the model is ready to do inferencing. Go to Prediction page and perform inferencing.\n\nUse Case\n\nUse cases are some of the practical engineering applications where the AION can be used in Engineering Analytics, Oil and Gas, Chemicals, etc.\n\nTo enable the use case: Settings > select the enable option for UseCase Tab > Submit. \n\n\n\nHence use case tab will appear on the left column. Aion can be used to solve many real-life engineering problems from various sectors which is shown in the figure below.  \n\nUsers can select relevant use cases for training and prediction.",
+        "instruction": "",
+        "input": ""
+    },
+    {
+        "output": "Click on any of those use cases. Example: Click Engineering Analytics to explore various sectors where AION can be used.\n\n\n\nProblem statement:\n\nDefect reports are generated from various testing and development activities in software engineering. \n\nSometimes two reports are submitted that describe the same problem, leading to duplicate reports. \n\nApproach:\n\nDefect reports are generated from various testing and development activities in software engineering. \n\nSometimes two reports are submitted that describe the same problem, leading to duplicate reports. \n\nSolution:\n\nThe model is first trained on the training data (~600 records) and then evaluated on the test data.\u00a0\n\nThe Model is used to find the duplicate defects based on similarity score or Word Movers Distance from historical data.\n\nCosine similarity metric is used to measure duplicates. \n\nResult:",
+        "instruction": "",
+        "input": ""
+    }
+]