10 Trending Virtual MLOps Talks Coming to ODSC Europe 2022
A model that never makes it into production is one that is incapable of producing value for a business or organization. Unfortunately, the percentage of models that make it out of development is still low. However, the field of MLOps is focused on this very problem and has come up with a number of solutions, best practices, and tools to help organizations effectively integrate machine learning and AI models into their business practices. These MLOps trends will be helpful beyond just 2022.
To help you learn the tools and skills you need to implement MLOps in your organization, ODSC Europe 2022 this June 15th-16th will feature talks, workshops, and training sessions led by some of the best and brightest minds in the field. Check out some of the topics, tools, and use cases that will be discussed below. These MLOps trends go beyond the conference, and show what’s trending in 2022, who to pay attention to, what companies are excelling with MLOps, and emerging tools.
Editor’s note: Abstracts are abbreviated. Please check our schedule for full abstracts.
MLOps Sessions:
Human-Friendly, Production-Ready Data Science with Metaflow
Ville Tuulos | Co-Founder | Outerbounds
There is a pressing need for tools and workflows that meet data scientists where they are. This is also a serious business need: How to enable an organization of data scientists, who are not software engineers by training, to build and deploy end-to-end machine learning workflows and applications independently. In this talk, we discuss the problem space and the approach we took to solving it with Metaflow, the open-source framework we developed at Netflix, which now powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics and drones to real estate.
Full-stack Machine Learning for Data Scientists
Hugo Bowne-Anderson, PhD | Head of Data Science Evangelism and Marketing | Outerbounds
One of the key questions in modern data science and machine learning, for businesses and practitioners alike, is how do you move machine learning projects from prototype and experiment to production as a repeatable process. In this workshop, we present an introduction to the landscape of production-grade tools, techniques, and workflows that bridge the gap between laptop data science and production ML workflows.
Dan Sullivan, PhD | Principal Data Architect | 4 Mile Analytics
This presentation argues for the large-scale adoption of data mesh principles to advance data science. Specifically, there is a need for domain-specific data standards including well-defined data structures for key entities in the domain and metadata to support particular use cases. Examples will demonstrate how bioinformaticians create data pipelines that draw from data sources about gene (GenBank) and protein (UniProt) sequences, protein structures (Protein Data Bank), gene expression (Expression Atlas), bioactive molecules (ChemBL), and metabolic and signaling pathways (KEGG Pathway Database).
Scaling Machine Learning with Data Mesh
Shawn Kyzer | Principal Data Engineer | Thoughtworks
With the quick rise in popularity of Data Mesh we now approach new frontiers in the Data Mesh space to solve for more complex scenarios such as model training at scale. This talk will discuss how to architect your Data Mesh platform to create scalable self service Machine Learning Data Products. Thereby allowing both Data Scientists and Machine Learning Engineers to easily provision and deploy infrastructure reducing time to market while also gaining all the benefits of Data Mesh.
A Systematic Approach for Building Full-Spectrum Model Monitoring
Mihir Mathur | Product Manager | Lyft
In this talk we’ll discuss a systematic framework to build and roll-out full-spectrum Model Monitoring for identifying and preventing problems with models. We’ll do a deep dive into Lyft’s model monitoring architecture (which includes real-time feature validation, performance drift detection, anomaly detection, and model score monitoring), how we leveraged open source, and the cultural change needed to get data scientists to effectively monitor their models.
Aoife Cahill, PhD | Director, AI Research | Dataminr
In this talk, Aoife will describe the technical challenges in processing vast amounts of heterogeneous, noisy data in real-time from the web and other sources, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in humanitarian and emergency response. She will give specific examples and discuss relevant future research directions in several AI fields.
Run Azure Machine Learning Anywhere in Multi-cloud or on Premises
Doris Zhong | Product Manager | Microsoft
In this session, you will learn how to run machine learning workloads with seamless Azure Machine Learning experience anywhere, including on-premises, in multi-cloud environments, and at the edge. Use any Kubernetes cluster and extend machine learning to run MLOps, model training, real-time inference, or batch inference. You can manage all the resources through a single pane with management, consistency, and reliability.
The Hidden Layers of Tech Behind Successful Data Labeling
Glen Ford | VP of Product | iMerit
Failure or delays in creating training data and deploying data ops can suffocate good deep learning models, a chance data scientists can’t bet on. In this session, attendees will learn how iMerit is solving the problem of scaling data pipelines with accuracy using unique technology. Join iMerit’s VP of Product, Glen Ford, as he uncovers the invisible technology building successful data labeling workflows and discovering anomalous and novel classes for customers using iMerit’s Edge Case technology.
The Rapid Evolution of the Canonical Stack for Machine Learning
Lee Baker | General Secretary | AI Infrastructure Alliance
Just a few years ago every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon, rolled their own AI/ML tech stack from scratch. Fast forward to today and we have a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? In order for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast.
What’s new in Apache Airflow 2.3?
Kaxil Naik | Director of Airflow Engineering | Astronomer
This session will talk about the awesome new features the community has built that were recently released in Apache Airflow 2.3. This includes: Dynamic Task Mapping, First-class support for DB Downgrades, Pruning old DB records (No need of using Maintenance DAGs anymore), Building Connections using JSON, and UI Improvements.
How to see these virtual MLOps talks
By registering for an ODSC Europe 2022 virtual ticket, you will be able to see all of these MLOps sessions and more. Tickets start at £99 or you can pick up a free Demo Talks pass to see a limited number of sessions for free.
