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Making Machine Learning Accessible for Enterprises: Part 1

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Making Machine Learning Accessible for Enterprises: Part 1

In this article, we will focus on the key considerations for enterprises to enable their teams in their journey towards AI-led enterprise software and solutions.

· AI Zone ·
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What is Machine Learning? Why is Machine Learning more relevant now than ever before, and why will it be a disruptive force for all businesses? We have heard and read enough! In this article, we will focus on the key considerations for enterprises to enable their teams in their journey towards AI-led enterprise software and solutions.

In the context of enterprise, below are some key areas that enterprises need to consider in evaluating a platform for their teams to deliver successful outcomes using Machine Learning.

  • Speed and Scale for Data Transformation and Modeling
  • Automation of Data Science
  • Model Explainability
  • Model Governance (Traceability, Deployment & Monitoring)

Speed and Scale for Data Transformation and Modeling

Typically, large enterprise data sets cannot be handled within a computer but need to be processed in a parallel and distributed manner. For example, sensor data from Chillers/Industrial machines/IoT devices are captured today for every 30 secs and this data collected over years produces Big Data. The data captured from sensors, line of business applications, or data warehouses have to be transformed into a form suitable for Machine Learning modeling. Data Engineers who work on the projects should be able to carry out required transformations like data imputation, categorical data transformations, etc. through a distributed computing framework like Apache Spark.

The need for speed and scalability holds a greater good, not only for transforming datasets but also for model creation. Machine Learning models typically provide better results with more experience, which means more data. While models in R or Scikit-learn in Python work well on smaller datasets published in Kaggle, they are at times unsuitable for enterprise data volumes. Models getting created in hours instead of days help data scientists iterate with multiple hypotheses, which is key for getting better model accuracy. Therefore, ML models that are implemented for parallel computation on partitions of data residing on multiple machines are desired.

Automation of Data Science

Shortage of "AI-talent" (data scientists, machine learning experts, etc.) is still a key challenge impeding AI adoption across enterprises. What better way would enterprises have to handle this talent crunch than to enable their engineers and business analysts who are close to the data with tools to solve these problems. There are platforms available today that democratize Data Science through automatic model selection, hyper-parameter tuning, and feature engineering capabilities. For example, choosing appropriate values for number of trees, depth, and learning rate for a tree-based ML algorithm like Gradient Boosting Trees is not trivial and needs extensive experience. These platforms iterate through different Machine Learning models, tune each of the models for their hyper-parameters, and choose the best model as per business metrics. Data Science best practices like cross-validation, handling imbalanced data sets, handling high dimensional categorical features are also automated by these platforms. These enable business owners, analysts, and developers with almost no to limited experience in data science to achieve expert-level ML results. They also enable enterprises to solve varied types of business problems across the organization using Machine Learning.

Figure 1: Screenshot shows the auto-model tuning of Infosys Nia Machine Learning platform

In the next part, we will discuss other critical requirements for enterprises to roll-out Machine Learning-based solutions for solving challenging business problems. 

Your machine learning project needs enormous amounts of training data to get to a production-ready confidence level. Get a checklist approach to assembling the combination of technology, workforce and project management skills you’ll need to prepare your own training data.

machine learning ,automation ,big data ,data science ,machine learning algorithms ,digital transformation ,artificial intelligence

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