Building blocks for success

You will learn how to use Iterative tools to create a flexible and effective pipeline to get your machine learning projects into production efficiently and fully reproducible.

Collaborate

Break down the wall between data science and engineering teams.  You will be able to work together more transparently and efficiently, significantly speeding your time to market for your projects.

Best Practices

You will learn to apply the best practices from software development to machine learning projects. Iterative tools enable  version control of data, models and experiments as well as CI/CD for machine learning projects.

Has over 6 years of hands-on experience in Machine Learning & Data Science projects as data scientist and team lead. Leads projects and helps teams to implement good tools and engineering practices. Author of online courses on ML Experiments automation and MLOps with DVC. Helped ML teams from banking, telecom, retail and other industries to set up MLOps tools and processes.

Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. In her free time, she enjoys learning random things, like how to ride a unicycle, and playing with her dog.

Tapa is our Senior Product Engineer for Studio from Nepal. She previously worked as a Principal Engineer at a tech start up in India and has worked in various capacities in her career from engineering to project management and communications. Her interests include learning languages and breaking gender stereotypes.

Paweł is a software engineer, based in Warsaw, Poland. His interests revolve around software engineering and deep learning. Currently, he is developing DVC. In his free time he likes to hike and swing kettlebells.