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Capitalize on Machine Learning at Scale

“MLOps is a must-have capability to operationalize AI at scale.” – Forrester Machine learning (ML) and artificial intelligence (AI) have the potential to create new insights and new opportunities for your agency. To maximize on the potential of ML and AI, you need to develop processes that allow you to repeatably and reliably train, validate, deploy, and […]

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June 01, 2021

“MLOps is a must-have capability to operationalize AI at scale.” – Forrester

Machine learning (ML) and artificial intelligence (AI) have the potential to create new insights and new opportunities for your agency. To maximize on the potential of ML and AI, you need to develop processes that allow you to repeatably and reliably train, validate, deploy, and maintain your ML algorithms. With MLOps, your organization can democratize ML, empower a community of data practitioners, and maximize business impact. MLOps makes ML efforts more efficient and effective, allowing them to scale to meet the largest challenges.

Objectives:

  • Understand the importance of business needs before introducing AI
  • Introduce the 5 areas essential for MLOps
    • MLOps for data governance and ingestion
    • Model Development
    • Model Operationalization
    • Monitoring
    • Security
  • Learn how to get started with an example agency use-case

Speakers:

Claire Walsh, Vice President of Engineering and Services, Excella

Henry Jia, Data Science Lead, Excella

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