Build Your MLOps Tool Stack

“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

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:

Want to learn how other teams built their tool stacks?

Jump on the call with an ML Teams Advocate right away.

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:

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 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.

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.

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

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

This Is Our MLOps Tool Stack: Continuum Industries

MLOps at GreenSteam: Shipping Machine Learning [Case Study]

How These 8 Companies Implement MLOps: In-Depth Guide

Model Deployment Challenges: 6 Lessons From 6 ML Engineers

4 Ways Machine Learning Teams Use CI/CD in Production

ML Model Testing: 4 Teams Share How They Test Their Models

MLOps at a Reasonable Scale [The Ultimate Guide]

Real-World MLOps Examples: Model Development in Hypefactors

Tips for MLOps Setup—Things We Learned From 7 ML Experts

Building MLOps Pipeline for NLP: Machine Translation Task [Tutorial]

Building MLOps Pipeline for Time Series Prediction [Tutorial]

How to Solve the Model Serving Component of the MLOps Stack