The MLOps community started in March 2020 as a place for engineers and practitioners to get together and share their knowledge about operationalizing ML. Since its inception, it has morphed into a community of more than 3k members with an array of …
Financially Responsible Feature Engineering
Anyone who has tried doing machine learning at scale knows it can get expensive. The costs associated with training models using on-demand compute and storing features in low latency databases can quickly get out of hand, and we’re often forced to …
The Only Truly Hard Problem in MLOps
MLOps solutions are often presented as addressing particularly challenging problems. This is mostly untrue. The majority of the problems solved by MLOps solutions have their origins in pre-ML data processing systems and are well addressed by the …
Towards a Unified Real-Time ML Data Pipeline, from Training to Serving
On a global marketplace like Etsy where buyers come to buy unique, varied items from sellers from around the globe, the inventory of items is constantly changing. User preferences also change in real time as they discover the latest selection being …
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 …
Best Practices for Productionalizing Data & ML Projects
This talk will briefly explore the development lifecycle for data engineering & ML projects before delving into some of the friction points most common when productionalizing those projects. We’ll provide an overview of how large companies like …
Bringing Feast 0.10 to AWS
In this lightning talk, we will cover our vision, the current state, and the roadmap for bringing Feast 0.10 to AWS. We will take a deep dive into the core composable API, and into Amazon DynamoDB and Amazon S3 connectors for online and offline …
Scaling a Machine Learning Social Feed with Feature Pipelines
In 2018 we launched an experiment to add machine learning to the ranking algorithms on the social feed of the Cookpad application. The results of this experiment were plausible for our users, however the architecture we built for this experiment did …
Programmatic Supervision for Software 2.0
One of major bottlenecks in the development and deployment of AI applications is the need for the massive labeled training datasets that drive modern ML approaches today. These training datasets traditionally are often labeled by hand at great time …
Building a Feature Store to Reduce the Time to Production of ML Models
Within the world of software development and especially in the area of Machine Learning, times assigned to analysis, development and deployment are key to the success of an organization. Experts’ work in the Feature Engineering process is one of …
Feature Stores at Spotify: Building & Scaling a Centralized Platform
Over 345 million Spotify users rely on Spotify’s great recommendations and personalized features in 170 different markets around the globe (with 85 of these markets launching in the first part of 2021). Some users even claim Spotify knows their …
Tackling Fraud with Tecton
Feature stores enable companies to make the difficult leap from research to production machine learning. At their best, feature stores allow you to code up your features once, then use them for training and production, and share them between models. …
