Iterative Tools

Studio

Track experiments and share insights from ML projects

DVC

Open-source version control system for ML projects

CML

Open-source CI/CD for ML projects

MLEM

Open-source model registry and deployment tool for ML projects
Achieve your goals

Learn to become a better ML Practitioner

New to Iterative Tools?  Start learning how to use our tools to be more effective at productionizing your machine learning projects.     
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Iterative TOols School

Develop your professional skills

Learn how to break the wall between data science and software development with Iterative Tools.

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Benefits of our courses

We're designing a Community to get you where you need to go.

Practical approach

Our training is designed to provide the skills in a practical approach. Learning is broken down into digestible modules with additional practice lessons.  Our students' success is our best asset in showing the quality of our tools.  Join our Community to connect with others grappling with the same challenges and grow your skills.

Open Source +

Our open source tools are free for the world to use.  Our Studio tool is built to drive extended visibility and collaboration for your team on top of our open source tools. Students can build their own customized platform without the heavy lifting of a homegrown solution by leveraging shared strategies shared and knowledge learned in this course.

For your career

Wherever you are in the Data Science ecosystem, learning our tools will provide you the knowledge and capability to deliver reproducible, composable, efficient and collaborative machine learning projects for your organization in your domain. No need to reinvent the wheel.  Our tools bring best practices of software engineering to ML projects.

Get Closer To Your Goals

As a Data Scientist you know everything that goes into building a successful model for your proof of concept.  But turning that proof of concept into a working software application is another set of complex tasks.  Input data maybe continuously changing or updating. You may even be chaining together multiple POCs to reach your desired outcome.  The complexity grows, and keeping track of all of it can be challenging at best and chaos at worst. Ethics and increasing regulatory requirements call for the ability to reproduce your work.
In this course you will learn the best practices from software engineering and apply them to machine learning projects.  This is now achievable with Iterative tools that fill the gap where traditional software development tools fall short.  The large file requirements of your data and models are tracked with DVC, while Git takes care of the metafiles that sync all the versions of your process together.

CML helps you automate routine tasks in the process for more efficiency, and Studio brings it all together in a collaboration tool for your team. Together Iterative tools provide you the building blocks to flexibly create an efficient pipeline to machine learning to development in whatever domain you are operating in. 

So buckle up!  You're about to take your data science skills to the next level! 🚀

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Our tools with your words

#modularity | #composability | #easy collaboration | #auto trigger new training run | #hella cool

🦉 Am very much enjoying the modularity of @DVCorg's open-source products (particularly CML and DVC). The tools feel so much more composable than other MLOps systems...
Paige bailey
Interesting blog post from @DVCorg with their philosophy for managing workflows with ML.
I've enjoyed using DVC for the last year - for the first time I've had a process that allows for easy collaboration on data analysis with colleagues
Liam Brannigan
This looks extremely useful. Full CI/CD for ML projects, baked directly into GitHub using DVC. Any PR that changes training code auto-triggers a new training run, and new metrics / image samples are auto-commented on the PR. Very cool!
Russel Kaplan