Executive Briefing: What it takes to use machine learning in fast data pipelines

Dean Wampler helps you develop a conceptual understanding of the challenges faced by your teams as they develop and deploy machine learning and artificial intelligence services integrated with fast data (streaming) pipelines. While combining these technologies is challenging, the benefits include timely delivery of innovative services to your customers.

Dean begins by briefly discuss machine learning use cases that are best delivered as streaming data applications. He then explores the main challenges faced when deploying these technologies together and outline solutions to these challenges, including criteria to use when evaluating choices. Along the way, he explains the tools your teams are already talking about and the role they play.

Topics include:

• Bridging the gap between data science tools and methods versus data engineering tools and methods needed for robust production delivery
• How fast data pipelines are forcing changes to data architectures, in order to meet higher demands for reliability, resiliency, dynamic scalability, etc.
• Performance implications of different AI/ML and fast data tools and techniques
• Deploying updates to ML/AI capabilities into running pipelines without forcing restarts