Production Use Case Archives | Tecton

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Enabling rapid model deployment in the healthcare setting

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

More ethical machine learning using model cards at Wikimedia

First proposed by Mitchell et al. in 2018, model cards are a form of transparent reporting of machine learning models, their uses, and performance for public audiences. As part of a broader effort to strengthen our ethical approaches to machine learning at Wikimedia, we started implementing model cards for every model hosted by the Foundation. This talk is a description of our process, motivation, and lessons learned along the way. … 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

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

Training Large-Scale Recommendation Models with TPUs

At Snap, we train a large number of deep learning models every day to continuously improve the ad recommendation quality to Snapchatters and provide more value to the advertisers. These ad ranking models have hundreds of millions of parameters and are trained on billions of examples. Training an ad ranking model is a computation-intensive and memory-lookup-heavy task. It requires a state-of-the-art distributed system and performant hardware to complete the training reliably and in a timely manner. This session will describe how we leveraged Google’s Tensor Processing Units (TPU) for fast and efficient training. … Read More

Machine Learning in Production: What I learned from monitoring 30+ models

It’s a software monitoring best practice to alert on symptoms, not on causes. “Customer Order Rate dropped to 0” is a great alert: it alerts directly on a bad outcome. For machine learning stacks, this means we should focus monitoring on the output of our models. Data monitoring is also helpful, but should come later in your maturity cycle. In this talk, I will provide practical strategies for prioritizing your monitoring efforts. … Read More

Wild Wild Tests: Monitoring Recommender Systems in the Wild

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is undoubtedly nuanced, and case-specific tests must be employed to ensure the desired quality. We introduce RecList, a behavioral-based testing methodology and open source package for RecSys, designed to scale up testing through sensible defaults, extensible abstractions and wrappers for popular datasets. … 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

Weaver: CashApp’s Real Time ML Ranking System

In this session, we will talk about one of the core infrastructure systems to personalize the experience on the CashApp, Weaver, and the work we did to scale it. Weaver is our real-time ML ranking system to rank items for search and recommendation use cases. We provide plug-and-play feature store and model hosting backends to meet various needs. We will share our experience on optimizing our service. … 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

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 a model as a service. Furthermore, creation/customization of these models for our customers is difficult as model training requires the procurement, setup, and use of specialized hardware and software. Primer’s ML Platform team solved both of these problems, model training and serving, by creating Kubernetes operators. In this talk we will discuss why we chose the Kubernetes operator pattern to solve these problems and how the operators are designed. … Read More

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