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MLOps: All-in-One Platform vs Piecemeal Tools

Should you choose an all-in-one MLOps platform or cobble together a solution from piecemeal tools?

 ·  โ˜• 2 min read
Putting together stuff is fun as long as it is not all consuming.
Putting together stuff is fun as long as it is not all consuming.

Photo by Shane Aldendorff on Unsplash

MLOps Tools Universe

I have been trying to grasp all aspects of MLOps for quite a while. There are โ€œall-in-oneโ€ platforms from 3 major clouds:

Then there are a number of companies and ecosystems either offering somewhat all-in-one platforms or piecemeal tools: MLFlow, Metaflow, Dataiku,, Valohai, Weights & Biases, Neptune, Comet ML, ClearML, OctoML, BentoML, ZenML, and many many more.

The number of tools and companies is overwhelming. Probably there are more companies than customers! Here is the relevant part of Matt Turckโ€™s 2021 Machine Learning, AI, and Data (MAD) Landscape:

Data amd ML tool landscape
Data amd ML tool landscape

Image Source: Matt Turck

MLOps โ€” The Dust is Settling

I think the dust is beginning to settle. I seek simplicity, and this landscape is anything but simple. I will default to choosing the all-in-one offering from the cloud providers for my project or company. Seeing the push from the big 3 to enhance and simplify their MLOps offerings, this decision will become more common.

Apart from the big 3, there is space for 2โ€“3 more all-in-one-offerings. Consolidation of these tools and companies is inevitable. There will be a bunch of open-source tools that will become the foundation of all this, much like how it happened in Big Data storage and processing.

Key Piecemeal Functionalities

MLOps itself looks pretty complex, but there are 7 key functionalities:

  • Data Versioning
  • Feature Store
  • Pipeline Orchestration
  • Experiment Tracking
  • Model Versioning
  • Model Serving
  • Model Monitoring

If you do not want to go with the big 3, pick the least number of the most popular open-source source offerings that cover the maximum of these 7 functionalities. Look for a coalition of 2โ€“3 tools that work well together out of the box and give you almost all of the 7 functionalities.

May the force be with you!

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Satish Chandra Gupta
Satish Chandra Gupta
Data/ML Practitioner