MLE5 : ML Operations Demystified ~ What Is It & Why It Matters

Sunday. May 14, 2023 - 1 min

MLOps is all about a set of processes and framework that sets up reproducible machine learning pipelines to bring models into production at scale. MLOps lifecycle involves from data preparation, model training to monitoring and deployment.

While the early versions of open-source AutoML solutions originated from academia,they were limited in support for workflow steps that extended to feature extraction and engineering, model evaluation, deployment and monitoring. The recent wave of enterprise AutoML solutions are developed mostly by the major cloud providers are more comprehensive while offering scalability, flexibility and explanability capabilities. Unlike a mainstream system design where tasks are run in a monolith fashion, these models abstract easch part of the ML workflow into an independent service as it scales.

I present a comparative study on some AutoML solutions from both cloud vendors and third-party solutions as they have attempted to abstract this complexity through managed machine learning services by focusing on key aspects of the core layers in terms of their functionalities and perfomance on experiments involving high dimensional dataset and multi-clas problems.

• Feature Stores
  
  
• Google’s AutoML
  
  
• Databricks AutoML
  
  
• Alteryx
  
  
• Dataiku
  
  
• H2O
  
  

Feature Stores are an important aspect in operationalizing the machine laerning pipeline. They function as a decision vault which is potentianally used to justify historical decisions and used to comply with regulations. for monitoring and auditing data.