Any organisation involved with deploying machine learning models to production knows it comes with its share of business and technical challenges and will typically look to solve ‘some’ of those challenges by using a Machine Learning Platform complemented with some MLOps processes to increase maturity and governance in your team.

For organisations running multiple models in production and looking to adopt an ML platform they’ll typically either build an end-to-end ML platform in-house (Uber, Airbnb, Facebook Learner, Google TFX etc), or buy. In this article I am going to compare some ML Platforms which you can buy.

When do you need an end-end ML Platform?

You should always answer another question first. “What problems are you trying to solve?”. If you can’t think of any existing problems, then there is no point in implementing new technology. However, if you are running into issues like, tracking what models or what versions are in production, increasing governance with your ML experiments, sharing notebooks as your data science team is growing or proactively monitoring data drift and/or feature drift, then chances are you going down or about to go down the path of implementing components of a ML platform.

To assist with this process I have performed a high-level comparison of what I’d like to think are key components of a ML platform.


The following comparison is considering out of the box functionality. E.g. not deploying an open-source solution to close a gap in functionality or building in-house.

  enter image description here enter image description here enter image description here enter image description here
Features AWS GCP Azure Databricks
Data pipeline Data Pipeline Dataflow Data Factory Spark
Feature Store
Model Monitoring Model Monitor Azure Monitor
Experiment Management SageMaker Experiments Azure Machine Learning SDK MLFlow Tracking
Model versioning Production Variants Versions Model registration MLflow Model Registry
A/B Testing Sagemaker Controlled Rollout
Model Serving Sagemaker AI Platform Azure Machine Learning MLFlow Model Serving
AutoML Autopilot Cloud AutoML AutomatedML
Notebooks Sagemaker Notebooks AI Platform Notebooks Microsoft Azure Notebooks Notebooks

Feature Store?

You might have noticed, not a single solution offers a feature store. Feature Stores are a fairly new component in the landscape and I suspect more organisations will be adding this to their ML platforms in the near future. Their are a few open-source solutions available, and even more research material on this amazing website. Just remember to focus on what problems you are aiming to solve, before investing time and money in a nonproblem.

“What problems are you trying to solve?”


To conclude this short article, I wanted to highlight some other products worth mentioning, but were not included in this comparison. DataRobot, IBM Watson Studio,, Data Bricks & Cloudera. I am going to endeavor to try keep this article up to date in the coming months as the machine learning landscape is changing rapidly. You don’t have to listen to me, just check out all the announcement’s on MLflow blog from Databricks at the Spark + AI Summit this year!

If you want your product added to this comparison please reach out to me on my LinkedIn.