MLOps takes center stage at Spark + AI Summit

As companies ramp up machine learning, the growth in the number of models they have under development begins to impact their set of tools, processes and infrastructure. Machine learning involves data and data pipelines, model training and tuning (i.e., experiments), governance, and specialized tools for deployment, monitoring and observability. About three years ago, we started to first hear of  “machine learning engineer” as a role emerging in the San Francisco Bay Area. Today, machine learning engineers are more common, and companies are beginning to think through systems and strategies for MLOps — a set of new practices for productionizing machine learning.
 
MLOps-focused trainings, tutorials, keynotes, and sessions featured at the 2020 Virtual Spark + AI Summit

Featured MLOps-focused Programs and Topics

The growing interest in Devops for machine learning or  MLOps is something we’ve been tracking closely and, for the upcoming virtual Spark + AI Summit, we have training, tutorials, keynotes and sessions on topics relevant to MLOps. We provided a sneak peek during a recent virtual conference focused on ML platforms, and we have much more in store at the conference in June. Some of the topics that will be covered in the virtual Spark + AI Summit include the following:

  • Model development, tuning and governance: There will be hands-on training focused MLflow and case studies from many companies, including Atlassian, Halliburton, Zynga, Outreach and Facebook.
  • Feature stores: In 2017, Uber introduced the concept of a central place to store curated features within an organization. Since the creation and discovery of relevant features is a central part of the ML development process, teams need data management systems (feature stores) where they can store, share and discover features. At this year’s summit, a set of companies, including Tecton, Logical Clocks, Accenture and iFoods, will describe their approach to building feature stores.
  • Large-scale model inference and prediction services: Several speakers will describe how they deploy ML models to production, including sessions that detail best practices for designing large-scale prediction services. We will have speakers from Facebook, LinkedIn, Microsoft, IBM, Stanford, ExxonMobil, Condé Nast and more.
  • Monitoring and managing models in production: Model monitoring and observability are capabilities that many companies are just beginning to build. At this year’s virtual summit we are fortunate to have presentations from companies and speakers who are building these services. Some of the companies presenting and teaching on these topics include speakers from Intuit, Databricks, Iguazio and AWS.
  • MLOps: Continuous integration (CI) and continuous deployment (CD) are well-known software engineering practices that are beginning to influence how companies develop, deploy and manage machine learning models. This year’s summit will have presentations on CI/CD for ML from leading companies, including Intel, Outreach, Databricks and others.

Machine learning and AI are impacting a wider variety of domains and industries. At the same time, most companies are just beginning to build, manage and deploy machine learning models to production. The upcoming virtual Spark + AI Summit will highlight best practices, tools and case studies from companies and speakers at the forefront of making machine learning work in real-world applications.

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