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 …
Lakehouse: A New Class of Platforms for Data and AI Workloads
In this talk, Matei will present the role of the Lakehouse as an open data platform for operational ML use cases. He’ll discuss the ecosystem of data tooling that is commonly used to support ML use cases on the Lakehouse, including Delta Lake, …
DIY Feature Store: A Minimalist’s Guide
A feature store can solve many problems, with various degrees of complexity. In this talk I’ll go over our process to keep it simple, and the solutions we came up with.
Workshop: Operationalizing ML Features on Snowflake with Tecton
Many organizations have standardized on Snowflake as their cloud data platform. Tecton integrates with Snowflake and enables data teams to process ML features and serve them in production quickly and reliably, without building custom data pipelines. …
The dbt Semantic Layer
In this talk, Drew will discuss the dbt Semantic Layer and explore some of the ways that Semantic Layers and Feature Stores can be leveraged together to power consistent and precise analytics and machine learning applications.
Intelligent Customer Preference engine with real-time ML systems
In an omni-commerce space such as Walmart, Personalization is the key to enable customer journeys tailored to their individual needs, preferences and routines. Moreover, in e-commerce, customers’ needs and intent evolve with time as they navigate …
Machine Learning, Meet SQL: When ML Comes to the Database
SQL has evolved beyond its relational origins to support non-relational abstractions like arrays, JSON, and geospatial data types so it shouldn’t surprise us that SQL is now being used to build and serve machine learning models. In this …
PyTorch’s Next Generation of Data Tooling
An overview and lookahead of our data efforts within PyTorch, including our new API extension points to support state-of-the-art ML data processing in both research and production. TorchData, an extensible library for constructing data loading …
Workshop: Building Real-Time ML Features with Feast, Spark, Redis, and Kafka
This workshop will focus on the core concepts underlying Feast, the open source feature store. We’ll explain how Feast integrates with underlying data infrastructure including Spark, Redis, and Kafka, to provide an interface between models and …
Empowering Small Businesses with the Power of Tech, Data, and Machine Learning
Data and machine learning shape Faire’s marketplace – and as a company that serves small business owners, our primary goal is to increase sales for both brands and retailers using our platform. During this session, we’ll discuss the machine …
Feature Engineering at Scale with Dagger and Feast
Dagger or Data Aggregator is an easy-to-use, configuration over code, cloud-native framework built on top of Apache Flink for stateful processing of real-time streaming data. With Dagger, you don’t need to write custom applications or manage …
Declarative Machine Learning Systems: Ludwig & Predibase
Declarative Machine Learning Systems are a new trend that marries the flexibility of DIY machine learning infrastructure and the simplicity of AutoML solutions. In this talk we will discuss about Ludwig, the open source declarative deep learning …
