MLOps tool stack

Build Your MLOps Tool Stack

avatar lazyload
We started like everyone else. We were having endless discussions, creating new notebooks, re-training, comparing the results. It worked for building a prototype, but we needed to organize it better. (…) We knew our ML operations needed to grow with the company, and we needed a setup that could handle it. (…)

Sometimes, the best way to move forward is to take a step back, which is exactly what we did. We’ve decided to start from scratch and rethink our entire machine learning infrastructure and operations.
Tymoteusz Wołodźko (Ex) Machine Learning Engineer at GreenSteam

Setting up an ML infrastructure is a challenging task. These resources should help you do it right.

You’ll find here everything you need to know about building your MLOps tool stack:

What is MLOps?

MLOps (Machine Learning Operations or Machine Learning Model Operationalization Management) is a set of practices for collaboration and communication between data scientists and operations professionals. 

Applying these practices:

  • increases the quality
  • simplifies the management process
  • and automates the deployment

of Machine Learning and Deep Learning models in large-scale production environments. It’s easier to align models with business needs, as well as regulatory requirements.

MLOps cycle

MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle – data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.

Read more about MLOps here:

MLOps tool stack

The range of MLOps tools is very wide. There are tools specific for only some parts of the MLOps pipeline and those that can help you manage the whole process.

MLOps tools landscape

Here are a few articles that can help you build your MLOps tool stack. 

The Best MLOps Tools and How to Evaluate Them

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The Best Open-Source MLOps Tools You Should Know

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15 Best Tools for Tracking Machine Learning Experiments

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Best Tools to Do ML Model Monitoring

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Best 7 Data Version Control Tools

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The Best Tools for Machine Learning Model Visualization

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The Best Tools to Monitor Machine Learning Experiment Runs

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Best Metadata Store Solutions

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Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning

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Best MLOps Platforms to Manage Machine Learning Lifecycle

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Examples of MLOps implementation

If you want to read about how different ML practitioners and industry teams built their MLOps tool stacks, check these case studies and examples. 

Looking for the experiment tracking and model registry component for your MLOps stack?

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