Machine Learning (ML) is at the helm of products. As products evolve with time, so is the necessity for ML to evolve. In 2010s, we saw DevOps culture take the forefront for engineering teams. 2020s will be all about MLOps.
MLOps stands for Machine Learning Operations. MLOps mainly focuses on workflows, thought processes and tools that are used in creating ML models, and their evolution over time. The workflows for ML at organizations are different as the problem space, maturity of teams and experience with ML tools are widely different.
MLOps relies on DataOps. DataOps is about Data operations, and helps define data and SLOs for data - how they are stored, managed and mutate over time - thereby providing the foundations for sound ML. The success and failure of ML models depends heavily on DataOps, where data is well-managed and brought into the system in a well thought out manner. ML and data processes have to evolve to provide insights into the reasons as to why certain models are not behaving as before.
Productionizing ML models is a challenge, but so is running experiments at scale. MLOps caters not only to scaling ML models in production, but also helps in providing guidelines and thought processes to support rapid prototyping and research for ML teams.
The 2021 edition is curated by Nischal HP, Director of Data at Scoutbee.
The conference covers the following themes:
- Machine Learning Operations
- Machine Learning in Production
- Privacy and Security in Machine Learning
- Tooling and frameworks in Machine Learning
- Economies of Machine Learning
Speakers from Doordash, Twilio, Scribble Data, Microsoft Research Labs India, Freshworks, Aampe, Myntra, Farfetch and other organizations will share their experiences and insights on the above topics.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require ML or building developer productivity products for ML.
- Product managers, who are seeking to learn about the process of building ML products.
- Directors, VPs and senior tech leadership who are building ML teams.
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