Schedule: Managing and Deploying Machine Learning sessions

Most machine learning algorithms are designed to work on stationary data, but real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Emre Velipasaoglu reviews monitoring methods, focusing on their applicability in fast data and streaming applications. Read more.

Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries. Read more.

Ted Dunning offers an overview of the rendezvous architecture, which is geared to deal with much of the complexity involved in deploying models to production, thus allowing more time to be spent thinking and doing real data science. Ted covers the ideas behind the architecture, practical scenarios, and advantages and disadvantages of the architecture. Read more.

Most uses of deep learning involve models trained with large datasets. Ramesh Sridharan explains how Captricity uses deep learning with tiny datasets at scale, training thousands of models using tens to hundreds of examples each. These models are dynamically trained using an automatic deployment framework, and carefully chosen metrics further exploit error properties of the resulting models. Read more.

Having trouble coordinating development of your production ML system between a team of developers? Microservices drifting and causing problems debugging? Kinnary Jangla explains how Pinterest dockerized the services powering its home feed and how it impacted the engineering productivity of its ML teams while increasing uptime and ease of deployment. Read more.

In the past, you needed a high-end proprietary stack for advanced machine learning, but today, you can use open source machine learning and deep learning algorithms available with distributed computing technologies like Apache Spark and GPUs. Nanda Vijaydev and Thomas Phelan demonstrate how to deploy a TensorFlow and Spark with NVIDIA CUDA stack on Docker containers in a multitenant environment. Read more.

Moty Fania explains how Intel implemented an AI inference platform to enable internal visual inspection use cases and shares lessons learned along the way. The platform is based on open source technologies and was designed for real-time streaming and online actuation. Read more.

Guillaume Salou shares OVH's approach to continuous deployment of machine learning models, which involved building a full stack of automated machine learning. Automated machine learning allows the company to rebuild models efficiently and keep models up to date with fresh data brought by its data convergence tool. Read more.

A machine learning platform is not just the sum of its parts; the key is how it supports the model lifecycle end to end. Hope Wang explains how to manage various artifacts and their associations, automate deployment to support the lifecycle of a model, and build a cohesive machine learning platform. Read more.

The promises of AI are great, but taking the steps to implement AI within an enterprise is challenging. The secret behind enterprise AI success often traces back to the underlying platform that accelerates AI development at scale. Based on years of experience helping executives establish AI product strategies, Simon Chan helps you discover the AI platform journey that is right for your business. Read more.