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.
Who should participate in MLOps conference?
- 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.
Who should speak?
- Data engineers and architects who think about tools, frameworks and system landscapes to manage data, in an ever changing businsess landscape.
- MLOps engineers who build and maintain ML workflows and deploy ML models.
- Data engineers building production scale data pipelines, feature stores, model dashboards, and model maintenance.
- Tech leaders/engineers/scientists/product managers of companies who have built tools and products for ML productivity.
- Tech leaders/engineers/scientists/product managers of companies who have built tools, products, processes for Data Ops to support ML.
- Tech leaders/engineers/scientists/product managers who have experience with products that failed to make a mark in the market due to ML failures.
- Investors who are investing in the space of ML productivity tools, frameworks and landscape.
- Privacy/ethics stakeholders involved in model governance and testing for ethics/bias.
Speak about/share your work with MLOps on: https://hasgeek.com/fifthelephant/mlops-conference/sub
Contact information: Join The Fifth Elephant Telegram group on https://t.me/fifthel or follow @fifthel on Twitter. For inquiries, contact The Fifth Elephant on email@example.com or call 7676332020.