8 "Non-MLOps" sessions to attend at MLOps World 2022, and why we think they're important! ??

If you're attending MLOps World, here are some 'non-technical' sessions we think are very important!

Talk: Understanding Foundation Models: a New Paradigm for Building and Productizing AI Systems, Hagay Lupesko, Director of Engineering, Meta AI

Commentary: The advent of GPT-3 and other Foundational Model took the world by storm and left the AI community re-envisioning what the future holds. This is a great opportunity to learn from the head of Engineering at Meta, who will share how your company might benefit from FM's, and how you can put these models into production!

Panel: Exploring Model Governance and Analysis at Industrial Scale, Andrea Olgiati, Chief Engineer, AWS SageMaker | Arthur Berill is Head of Location, RBC | Kathryn Humne is VP Digital Investments Technology, RBC

Commentary: We used to treat our models as "pet's" carefully caring for them, and working closely on their individual performance. Now, it's not uncommon to have 250k-1MM models in production at large companies. But what does this mean for model governance? How can companies ensure they're addressing bias and mitigating risk? This will be a great, candid discussion with 3 true visionaries in the field sharing their hands-on experience.

Talk: What Every Product Manager Delivering AI Solutions Should Know - Nahla Salem, Senior Product Manager, Yelp | Phillip Gornicki, Product Manager, Kinaxis |Anneya Golob, Staff Data Scientist, Shopify

Commentary: Effective AI product managers are hard to find! It's not often you get an honest discussion around what effective practices look like, with valuable experiences that others can adopt!

Panel: What Do Engineers Not Get About Working with Data Scientists? Demetrios Brinkmann Founder, MLOps COmmunnity & Panel

Commentary: Now, more than ever, data scientists and engineers have to work collaboratively as a single team. While this requires coordination on both sides, and while this is an relatively new and novel practice with very little in the way of best practices established - it often get's rocky! This session will share core takeaways from data scientists who will give their canndid insights on how data engineers can support this transition, and more effectively work with data science teams.

Talk: Top 5 Lessons Learned in Helping Organizations Adopt MLOps Practices, Shelbee Eigenbrode Principal AI/ML Specialist & Solutions Architect AWS, Shelbee Eigenbrode

Commentary: When setting up an MLOps practice, there are many things not yet in alignment; from people to processes, to the underlining technology. These shortcomings might not be immediately evident but quickly become barriers and you'll learn how to mitigate these risks. Shelbee has a world of experience and this talk will shed a light on her top lessons from working with hundreds of companies.

Talk: Transformative Power of ML in the Real World - Tomi Poutanen, Co-Founder & CEO, Signal 1 AI

Commentary: One of Canada's top leaders in Canadian AI development, and commercialization is using AI to save lives, and there's a lot of evidence to suggest Signal 1 will make a big impact. Based on the work of Michael’s Hospital that has resulted in a more than 15-per-cent drop in mortality rate, and a core piece of proprietary technology licensed from TD Bank this use case is incredibly inspiring and not to miss!

Talk: How MLOps Tools Will Need to Adapt to Responsible and Ethical AI: Stay Ahead of the Curve, Patricia Thaine Co-Founder & CEO, Private AI

Commentary: Patricia is a bright mind that has established herself as a leading voice around privacy and explainability. This is a great opportunity to learn about how legislators are thinking about regulating AI and how requirements around privacy and explainability will fit into MLOps! You'll get a head start and some insights that will allow you to get ahead of the curve.

Talk: Don't Fear Compliance Requirements & Audits: Implementing SecMLOps at Every Stage of the Pipeline, Ganesh Nagarathnam, Head of Machine Learning Engineering & Analytics, S&P Global

Commentary: As regulatory bodies key into the many applications of ML/AI across industries, this will increasingly be a point of focus for your organization. This is a great chance to hear from a very senior leader who is proposing a solution that will help integrate your team into the development cycle to reduce the risks of audit requirements from GDPR, DPIA etc. As regulatory bodies key into the many applications of ML/AI across industries, this will increasingly be a point of focus for your organization