The primary responsibility of Data Scientists involves extracting value out of data by building and operationalizing Machine Learning Models. As businesses are embracing data science to improve business strategy, Data Scientists are struggling to manage the growing number of  Machine Learning models.

Fintech, Healthcare, and Retail industries are earmarking their Machine Learning budget for 2020 to increase by 25% raising the scale and complexity of Machine Learning models. With the growing complexities, Data Scientists are finding it laborious to manage the rising number of Machine Learning models in production. Based on the budget allotted for ML projects, enterprises either have separate teams or data scientists responsible for engineering data and building and managing ML models at scale.

There is a dire need for a seamless end to end Machine Learning platform to experiment ML models with proper version management and to deploy at scale with reproducible deployment pipelines. Cloud Vendors have taken notice and hopped on to the bandwagon to build ML platform for managing Machine Learning projects end-to-end.

ML Platform Varieties

Google Machine Learning platform offers necessary tools and libraries like TensorFlow Extended (TFX) and KubeFlow Pipelines (KFP) to build and orchestrate the deployment of Machine Learning models.

Google ML Platform

Amazon’s SageMaker Notebooks, SageMaker Experiments, and SageMaker Tuning are used to build and train models while SageMaker Neo and Model Monitor deploy and monitor ML models.

 Amazon ML Platform

Microsoft handles the build and deployment of Machine Learning Models using Azure Notebook, Azure Pipeline, and Azure Monitor.

 Microsoft Azure ML Platform

AirFlow and MLFlow are yet another set of tools used to manage Machine Learning projects end-to-end. It is very time consuming and in most cases impractical to evaluate this variety of ML platforms and tools.



Learn More from End to End ML Platform ! Are we there yet ? (Part 2)