Data engineering Archives | Tecton

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Workshop: Bring Your Models to Production with Ray Serve

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 and serving model nodes in a Ray Cluster.

Ray Serve supports inference on CPUs, GPUs (even fractional GPUs!), and other accelerators – using just Python code. In addition to single-node serving, Serve enables seamless multi-model inference pipelines (also known as model composition); autoscaling in Kubernetes, both locally and in the cloud; and integrations between business logic and machine learning model code.

We will also share how to integrate your model serving system with feature stores and operationalize your end-to-end ML application on Ray. … Read More

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, Apache Hudi, and feature stores like Feast and Tecton. … Read More

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. … Read More

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. David and Miles will provide a demo of the Tecton and Snowflake integration along with coding examples. Attendees will learn how to:

– Build new features using Tecton’s declarative framework

– Automate the transformation of batch data directly on Snowflake

– Automate the transformation of real-time data using Snowpark

– Create training datasets from data stored in Snowflake

– Serve data online using DynamoDB or Redis … Read More

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. … Read More

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 and engage with hundreds of millions of products. Real-time session-aware ML systems are best suited to adapt to such changing dynamics and can power intelligent systems to provide 1:1 personalized customer experiences, from finding the product to delivering it to the customer. In this talk we will look at how we leverage session features to power customer preference engines in real-time applications at Walmart scale. … Read More

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 presentation, we’ll review how Google Cloud BigQuery supports regression, classification, forecasting, dimensionality reduction, and collaborative filtering. Feature processing, hyperparameter tuning, and evaluation functions are described as well. The talk concludes with a discussion of good practices for building and serving ML models in Google Cloud BigQuery. … Read More

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 graphs, and TorchArrow, and lightweight front-end for dispatchable data processing, will be introduced with examples. … Read More

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 data. We’ll provide coding examples to showcase how Feast can be used to:

– Curate features in online and offline storage

– Process features in real-time

– Ensure data consistency between training and serving environments

– Serve feature data online for real-time inference

– Quickly create training datasets

– Share and re-use features across models … Read More

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 learning and data-related lessons and challenges we’ve encountered over the last 5 years on Faire’s journey to empowering entrepreneurs to chase their dreams. … Read More

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 resources to process data in real-time. Instead, you can write SQLs to do the processing and analysis on streaming data.

At Gojek, Data Platform team use Dagger for feature engineering on realtime features. Computed features are then ingested to Feast for model training and serving. Dagger powers more than 200 realtime features at Gojek. This talk will about the end to end architecture and how Dagger and Feast work together to provide a cohesive feature engineering workflow. … Read More

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 framework, and Predibase, an enterprise grade solution based on it. … Read More

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