MLOps World is taking place this year in Canada and virtually, from June 7 to 10, 2022. Packed with more than 80 workshops and case study talks, MLOps World is an important global gathering for anyone working with ML or AI.
With so many incredible sessions, it can be hard to choose which ones to attend. To help, we compiled our top recommended ones. We chose them based on the use cases they cover, their comprehensiveness of productizing ML and the practical tools they offer.
We will also be there, more details below.
Here are our top recommended eight sessions, in the order of their appearance:
June 7th, 10am - 12pm
Machine learning monitoring is essential for detecting problems in ML stacks. But how should data scientists and engineers get started? In this session, Lina Weichbrodt from DKB Bank will explain about the four golden signals, how to prioritize a service response and how to monitor with the existing tools you already have!
June 7, 10am - 12:30 pm
June 7, 1pm - 1:40pm
How does Google implement production applications in ML pipeline architectures while ensuring they are production-ready? In this talk, Robert Crowe from Google will explain the difference between production ML and research/academic ML, go through methods and architectures for creating and adapting MLOps infrastructure and show how Google uses TensorFlow Extended (TFX) and additional tools for rigorous analysis of model performance and sensitivity.
June 7, 2pm - 3pm
Most data science teams start with building AI models and only think about operationalization later. But taking a production-first approach and automating components is the key to generating measurable ROI for the business. In this talk, Iguazio’s co-founder and CTO, Yaron Haviv, explains how to simplify and automate your production pipeline to bring data science to production faster and more efficiently. He will show real live use cases while going through all the different steps in the process.
June 8, 4pm - 4:40pm
When your ML models make millions of high stake decisions every day, including pricing, physical safety classification and fraud, it’s critical that they don’t get degraded. In this talk, Mihir Mathur from Lyft will discuss approaches for model monitoring in real-time, challenges, how Lyft’s model monitoring architecture operated and the need for a cultural change.
6. Don't Fear Compliance Requirements & Audits: Implementing SecMLOps at Every Stage of the Pipeline
June 9, 11:30am - 12:15pm
As MLOps tools proliferate, applying security at every stage becomes challenging. Instead, Ganesh Nagarathnam from S&P Global proposes an extension framework - SecMLOps. In his talk, he will teach Product Managers, Program Managers and Application Security Managers how to integrate security early into the process with this framework, making compliance and security simple.
June 9, 3pm - 3:40pm
A practical session from Shelbee Eigenbrode from AWS, covering top five lessons and pitfalls to avoid for implementing MLOps practices at scale.
June 10, 11:30am - 12:15pm
Detecting drift and retraining models on time are important for model serving. In this talk, Diego Klabjan and Yegna Jambunath from Northwestern University, explain practical challenges in model serving for deep learning, provide possible algorithmic and modeling solutions and introduce their open-source project, a Kubernetes native POC solution, which incorporates these aspects.
Meet Iguazio at MLOps World
Let’s meet at MLOps World! Come see Iguazio at booth #25 and let’s discuss MLOps pipelines.
But the conversation doesn’t have to be over after MLOps. Join our MLOps live community on Slack for more discussions, ideas and interesting resources.
See you at MLOps World!