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Big Data

2-for-1: Deploying Machine Learning Models – Serverless and Container Design Patterns/The Future of Data Pipelines

Dan Zaratsian    John Hammink   

Dan Zaratsian – Deploying Machine Learning Models – Serverless and Container Design Patterns (Intermediate)

While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.

As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud. Both serverless and containerized approaches will be covered as well as the advantages and tradeoffs. The presentation will involve demos and sample code for the the deployment design patterns.

John Hammink – The Future of Data Pipelines (Introductory)

With mobile devices and emerging IoT connected infrastructure and devices, we’re seeing the amount of generated data explode, while continuing to transform in form and function. With 16.1 zettabytes of data generated in 2016 expected to grow tenfold by 2025, we’ll look at what we believe data pipelines and data-pipeline components will need to be able to achieve in terms of functionality, design, compliance, usability, performance, and scalability to handle this growth.