We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”
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.
Schedule a callWhat 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 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.
Here are a few articles that can help you build your MLOps tool stack.
The Best MLOps Tools and How to Evaluate Them
The Best Open-Source MLOps Tools You Should Know
15 Best Tools for Tracking Machine Learning Experiments
Read nowBest Tools to Do ML Model Monitoring
Best 7 Data Version Control Tools
Read nowThe Best Tools for Machine Learning Model Visualization
Read nowThe Best Tools to Monitor Machine Learning Experiment Runs
Read nowBest Metadata Store Solutions
Read nowContinuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning
Best MLOps Platforms to Manage Machine Learning Lifecycle
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.
