In this workshop, we will walk through a step-by-step guide on how to deploy an ML application with Ray Serve. Compared to building your own model servers with Flask and FastAPI, Ray Serve facilitates seamless building and scaling to multiple models …
Streamlining NLP Model Creation and Inference
At Primer we deliver applications with cutting-edge NLP models to surface actionable information from vast stores of unstructured text. The size of these models and our applications’ latency requirements create an operational challenge of deploying …
Accelerating Model Deployment Velocity
All ML teams need to be able to translate offline gains to online performance. Deploying ML models to production is hard. Making sure that those models stay fresh and performant can be even harder. In this talk, we will cover the value of regularly …
Building a Best-in-Class Customer Experience Platform – The Hux Journey – Deloitte Digital
New technologies have been advancing rapidly across the areas of frictionless data ingestion, customer data management, identity resolution, feature stores, MLOps and customer interaction orchestration. Over the same period many large enterprises …
Reusability in Machine Learning
In this session we will explore modern techniques and tooling which empower reusability in data and analytics solutions. Creating and leveraging reusable machine-learning code has many similarities with traditional software engineering but is also …
Towards Reproducible Machine Learning
We live in a time of both feast and famine in machine learning. Large organizations are publishing state-of-the-art models at an ever-increasing rate but the average data scientist face daunting challenges to reproduce the results themselves. Even in …
Third Generation Production ML Architectures: Lessons from History, Experiences with Ray
Production ML architectures (deployed at scale in production) are evolving at a rapid pace. We suggest there have been two generations so far: the first generation were very much fixed function pipelines with predetermined stages, the second …
Machine Learning is Going Real-Time
This talk covers different levels of real-time machine learning, their use cases, challenges, and adoption.
Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth
As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and …
