Implementing End-to-End Machine Learning Lifecycle Workflows on Azure

From pre-processing to learning to deployment - machine learning goes through many phases.  In this course, you will learn how developers and data scientists at all skill levels can use Azure Machine Learning for MLOps, Interoperability and Responsible ML in their projects.

We will use demos to illustrate how the audience can build, train, extract insights, deploy, and monitor models at scale. In addition, we will give an overview of how they can accelerate their end-to-end ML process to manage and manage collaborative ML workspaces to achieve versioning, tracking, traceability, and monitoring.

In this webinar, you will learn:

  • Access data for data wrangling at scale and link datasets during data preparation
  • Build & train models with your favorite language or frameworks using No Code/Low Code model training
  • Debug and analyze your model for explainability while working towards responsible AI scoring for reliable and unbiased quality
  • Optimize, package and deploy your model at scale while monitoring your model to avoid data-drifts and maintaining audit trails

[Webinar ID# 5305]

Earn 1 CEU. Credits are self-reported to the industry certifying bodies. Check their respective websites for details/qualifications.

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Ruth Yakubu

Ruth Yakubu is a Principal Cloud Advocate at Microsoft. She specializes in Java, Advanced Analytics, Data Platforms, and Artificial Intelligence (AI). Ruth is also a highly sought-after tech speaker for several conferences, like Microsoft Ignite, O'Reilly Velocity, Devoxx UK, Grace Hopper Dublin, TechSummit, Websummit, and numerous other developer conferences. Prior to Microsoft, she had done amazing work with UNISYS, ACCENTURE, and DIRECTV, where she gained a lot of experience with software architectural design and programming. Ruth was also awarded's Most Valued Blogger.

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