Commercialization education sessions give Vector researchers and FastLane SMEs a chance to connect with and hear from experienced industry specialists on a range of topics, including venture capital, contract law, intellectual property and AI, patent eligibility, and entrepreneurship.

Productionizing machine learning is difficult. The machine learning lifecycle consists of many complex components such as data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and much more. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering. Naturally, it requires stringent operational rigor to keep all these processes synchronous and working in tandem.

Join this session to learn more about MLOps from Stella Wu, Shakudo’s Co-founder and head of product.  We’ll cover:

• What is MLOps and why do we need to think about it now?
• What is considered as good MLOps practice and how to set them up with minimum effort to avoid distracting the core product development?

Open to SMEs that have signed up for Vector’s FastLane Program. Open to current and former Vector researchers, Vector Scholarship in Artificial Intelligence recipients, students and alumni from Vector-recognized master’s programs, students of Vector Faculty Affiliates, Vector Interns, and Vector Postgraduate Affiliates. Your registration will be cancelled if you do not meet this criterion.