Why use AWS SageMaker?
Tags: Machine Learning, AWS, Information Technology, Sagemaker, Technology

AWS SageMaker

Firstly let us see what is AWS SageMaker?

The most market shared cloud provider Amazon web service (AWS) provides lots of tools to explore Machine Learning, creating models with a high accuracy rate. This article will make you a little familiar with one of the services on AWS i.e Amazon Sagemaker which helps in developing efficient and more accuracy rate ML models and the few more benefits are that you can use different AWS services in your model like S3 bucket, amazon Lambda for monitoring the performance of our ML model we can also use AWS Cloudwatch which is a monitoring/analyzing tool provided by AWS.

What is the need for a platform for developing Machine Learning solutions?

A common workflow for a machine learning project is visualized below.

Machine Learning project workflow

Machine Learning workflows basically categorise three main building points:

Build: In this phase, we define the problem, collect data from a big pool of data which is also known as “raw data” and analyze it and clean it, and transform the data according to use. Train: In this phase, we train our model with a set of the dataset which is known as training data. This phase also includes testing of the model with the different dataset which is known as test data. This phase also includes data visualization which means, visual representation of the result processed out by the trained model. Deploy: After the model is trained and process out the maximum possible accuracy then the next step is to deploy the trained model.

Consider a complex ML hurdle that we have to deal with in an organization. Say, you have a very large dataset with millions of records to process and would need at least a few hundred iterations Each group (Build, Train, Deploy) requires a different infrastructure, a different approach towards the execution of the problem and a distinct set of disciplines to accomplish. This helps in understanding how each of these groups within an ML project workflow, fork out as an independent branch with diverse requirements of skills and infrastructure to execute. Building phase requires roles of Data Engineer and Data Analyst. Train phase requires Machine Learning skills, Deploy phase requires primarily Software Engineering and Software Development skills and a little ML skills. Each of these phases would require engineers with a completely different skill.

Usually, the execution of a big-scale ML project requires more from the team.

To solve this problem, most organizations hire multiple individuals with their respective skills in a project to fulfil the demands in each phase. A Data Scientist is the person who analyzes the data and creates machine learning models for the problem statement. Finally, a Machine Learning Engineer who takes the model developed by the scientist into a production line.

It is a hard work for a Data Scientist deploy a machine learning solution (that he researched and prototyped) into a full-scale web service (an API that can be integrated into a software ecosystem). Data scientists lack the required software engineering skills to take a research model and offline machine learning model into a large cumulative model (service) that can inference thousands of API calls in real-time.

However, the less understanding of machine learning and math skills brings in many errors while the implementation. Developing a full-scale ML service does require the engineer to have a deep understanding of the methods and process.

Most organization follow the same practice of hiring data scientists to research, experiment, prototype and develop a PoC that can validate an ML business use-case.

What’s the need for a cloud platform for ML/AI projects?

The cloud’s pay as use model is good for AI or machine learning projects. The cloud makes it easy for organizations to try with machine learning capabilities and scale up as projects go into production and demand increases. The cloud makes it easily possible to begin ML production without advanced skills in artificial intelligence or data science. AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning products that doesn’t require deep knowledge of AI/ML theory, or a team of data scientists. How could a platform solve this problem?

We can solve the problems mentioned above if we have a platform that requires remainder skills in each of these phases with resultants while be still being highly effective and flexible for a data scientist to deliver results.

Thus, there is a requirement of a platform where the data scientist will be able to scaleup his existing skills to study data, train and test ML models and finally deploy the model as a web service by using the required hardware, arranging the entire flow and transition for execution with simple.

extraction and provide a robust solution that can scale and meet demands elastically.

With the question asked and answered, we can now come to the final question.

What platform works best for me?

Here, we can say, AWS SageMaker fits best for us. It provides IDE’s such as Jupyter NoteBooks with running R/Python kernels with a compute instance that we can choose as per our data engineering requirements on demand. We can do visualization, process, clean and transform the data into our required forms using the traditional methods we use. And the entire process of provisioning hardware instances, running high capacity data jobs, orchestrating the entire flow with simple commands while abstracting the mammoth complexities and finally enabling serverless elastic deployment works with a few lines of code and further, it is more cost-effective. Sagemaker is a fair solution for the enterprise.

Features of AWS SageMaker

12 common machine learning algorithms TensorFlow and MXNet pre-installed Can use other ML frameworks

Some scenarios where Sagemaker might not be suitable.

The problems that we discussed earlier though visualizes a big majority of ML projects, there are in cases a few scenarios where this is not a positive option. SageMaker requires its users to code their data engineering and analysis needs in their choice of language. The entire orchestration of infrastructure and transition from one phase to another though happens with few lines code; definitely needs a small amount of code. For experienced professionals, who require easy drag and drop (which requires no code) solutions for the data engineering and ML model development phase, AWS Sagemaker is great platforms for developing ML solutions.

Concluding thoughts

AWS Sagemaker has been a great option for more number of data scientists who would want to full-stack an ML project. It takes care of processing out a ton of software development skills necessary to finish the task while still being effective, flexible and cost-effective as well. Most important, it helps to focus on the core ML experiments and supports the end to end of the ML project.