Automated Machine Learning for Data Analysts & Business Users
By: Raheel Ahmad
Tags: Automl, Mltrons, Predictive Analytics, Deep Learning, Machine Learning

Augmented AI — Machine Learning

Automated Machine Learning for Data Analysts & Business Users

Automated Machine Learning for Business Users & Data Analysts

Automated Machine Learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science.

Machine Learning is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning has provided us with some significant breakthroughs in various industries. Areas like financial services, retail, healthcare, banking, and more have been using machine learning systems in one way or another, and the results have been very promising.

Machine Learning today is not just limited to R&D but has permeated into the enterprise domain. However, the traditional machine learning process is heavily human-dependent, and not all businesses have the resources to invest in experienced data science team. Even where the companies possess the resources, data scientists & engineers, these professionals have to spend hundreds of hours per month to building and maintaining these machine learning systems.

Our research has also shown that most of the current pool of data scientists lack domain expertise and therefore, need to work with professionals from different departments to solve a specific problem, e.g., predicting which customers are more likely to buy a product. These department reps are experts with deeper business knowledge and analytical skills but lack predictive analytics skills, machine learning in specific.

Automated ML democratizes the machine learning model development process, and empowers its users, no matter what their data science expertise is, to identify an end-to-end machine learning pipeline for any problem

Automated Machine Learning is giving rise to the Citizen Data Scientist by making it easier to build and use machine learning models in the real-world without writing code. Automated Machine Learning incorporates the best machine learning practices from top-ranked data scientists, state-of-the-art open-source libraries to make machine learning and data science more accessible across the organization.

Here is the traditional model building process:

Figure 1:

Traditional Machine Learning Model Building Process

As can be seen from Figure 1 above, developing a model with the traditional process is extremely time-consuming, repetitive and tedious. Automated Machine Learning application automatically performs the model building tasks that usually require a skilled data scientist. Instead of taking weeks or months, the automated machine learning system is fast, and usually requires days for business users/data analysts to build 100s of models, make predictions and generate insights. The machine learning automated for data analysts allows organizations to achieve more in less.

AutoML is making it possible for businesses in industries like healthcare, FinTech, banking and more — to leverage advanced machine learning and AI technology that was previously limited to organizations with large resources at their disposal. By automating most of the machine learning modelling tasks, AutoML is enabling business users and data analysts to implement machine learning solutions with ease and focus more on solving complex business problems.

Data Analysts & Business Users across industries can use Automated Machine Learning to:

1. Implement ML-powered solutions without extensive programming knowledge 2. Save time and resources 3. Leverage data science best practices 4. Agile problem-solving

How Automated Machine Learning works?

There are many tools out there and each has its own internal functionalities but we’ll simply focus on mltrons dp2 — a novel application with in-built data preparation and AutoML libraries.

Users can build new projects and run ML experiments with five simple steps by using a very intuitive Web User Interface.

1. Identify the use-case: predict demand, customer churn or credit card fraud 2. Identify the ML problem type: Regression, Classification or Time-Serices 3. Upload your data or connect your data source 3. Choose your target variable (the variable you want to predict) 4. Train & evaluation 100s of machine learning algorithms* 5. Deploy the best performing algorithm and make predictions**

*mltrons Automated Machine Learning curates state-of-the-art libraries like AutoKeras, PyTorch, TensorFlow, H2O, TPOT, Caffe, SageMaker, and AlphaD3M to build the best fit model for the data. Mltrons has an in-built GPUs to support deep learning.

Available libraries within the mltrons framework

** mltrons AutoML for data analysts allows them to use “what-if” scenario simulator to create strategies for multiple outcomes. This fills the gap between the data analyst/scientist & executive decision-maker.

It’s all about starting today!

It’s about taking on as many small projects as you can handle in order to generate value quickly. With automated machine learning, data analysts can start building machine learning models and take their skill-sets to the next level. On the other hand, businesses can get multiple wins under the belt and complete multiple use-cases within their organizations in less time that will build up momentum and make it possible for to iterate and expand the monetization of data.

About The Author

Raheel Ahmad is the CEO and Co-Founder of mltrons. You can connect with him on Twitter, Facebook, and LinkedIn.