If your organization is serious about successfully bringing machine learning models from development to production, a strong understanding of MLOps is a must. At ODSC West, we will have several talks, workshops, and training sessions on this essential field. Check out some of our confirmed sessions and speakers below.
Deep Dive into Flyte
Ketan Umare | Creator and Chair | Flyte
During this talk, you’ll discover if Flyte, a Kubernetes native, workflow automation platform for machine learning and data science workflows is right for your organization and how best to utilize it to minimize the need for a central team to manage the organization’s infrastructure.
Machine Learning | Digits
During his many years of experience, Hannes Hapke has solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He has also been recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: “Building Machine Learning Pipeline” by O’Reilly Media and “NLP in Action” by Manning Publications.
Unifying Development and Production Environments for Machine Learning Projects
Chip Huyen Adjunct Lecturer | Founder Stanford University | Startup on real-time ML
Learn about the challenges that arise during different phases of bringing machine learning models into production, as well as various solutions for addressing the gap between the production and development environments. You’ll also get hands-on experience using Metaflow.
Strategy Engineer, PMC Apache Airflow Project | Astronomer.io
Daniel Imberman’s passion is to help build the next generation of ML tooling. When he isn’t working toward that goal, he is a PMC of the Apache Airflow project, core contributor of the Kubernetes Executor, and Strategy Engineer at Astronomer.io.
Using Reproducible Experiments To Create Better Machine Learning Model
Milecia McGregor | Senior Software Engineer | Iterative
Learn how you can better track the changes in your model’s hyperparameter values, thereby increasing the reproducibility, using this open-source tool DVC. This live demo will take you through the process of setting up and running random search and grid search experiments.
Senior Machine Learning Developer, Developer Advocate | Pachyderm, Inc.
Jimmy Whitaker’s work is focused on sustainable and applied practices for instituting the machine learning life cycle. He is also the co-author of the textbook, Deep Learning for NLP and Speech Recognition.
MLOps… From Model to Production
Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
Learn how MLOps can support your data science models through deployment to maintenance. This session will focus on performance degradation, establishing continuous evaluation metrics, and tuning model performance in training pipelines and deployed serving pipelines.
CEO & Co-Founder | Noteable
Michelle’s company, noteable.io, is focused on building the first enterprise-grade platform for Jupyter notebooks. Before co-founding Noteable, she handled analytics tooling and platform innovation at Netflix.
Operationalization of Models Developed and Deployed in Heterogeneous Platforms
Sourav Mazumder Data Scientist |Thought Leader, AI & ML Operationalization Leader | IBM
Learn how your organization can develop and deploy models customized to meet your governance requirements while also meeting the needs of different groups and stakeholders using open-source and IBM technologies.
Don’t miss this opportunity to learn how to bring your machine learning models from development to production from some of the leading experts in the field. Register for ODSC West, the leading applied data science training conference, today to save 30% on your pass.