How Good Models Go Bad in Production

December 10, 2024

Ultimately you want your model to make better predictions — which means improving your model accuracy. Model accuracy measures how often a model’s predictions align with the actual values. How well can it reflect the current state of the real …

Feature PlatformsOperational & Real-Time Machine LearningThought Leadership

Why AI Applications Struggle Getting to Production

December 4, 2024

Learn why most AI projects fail to reach production, exploring the critical data engineering challenges that block deployment and how to overcome them.

Feature PlatformsThought Leadership

Why RAG Isn’t Enough Without the Full Data Context

September 20, 2024

Oh no! Your flight is delayed! You’re going to miss your connection and you need to change flights ASAP. Ever the savvy traveler, you start chatting with the new AI-powered customer support chatbot in the airline’s app. But does the chatbot know …

GenAIThought Leadership

Hidden Data Engineering Problems in ML and How to Solve Them

June 10, 2024

Machine learning teams are under constant pressure to embed AI into every customer interaction and experience. The demand for smarter features, personalized recommendations, and real-time decisions at every touchpoint is soaring. Yet, the intricate …

Thought LeadershipFeature Platforms

Productionizing Embeddings: Challenges and a Path Forward

April 30, 2024

Embeddings – condensed, rich representations of unstructured data – have emerged as a transformative technique for unlocking the full potential of both predictive and generative AI. In business-critical predictive AI applications like fraud …

Product UpdatesThought Leadership

How Tecton Helps ML Teams Build Smarter Models, Faster

April 5, 2024

In the race to infuse intelligence into every product and application, the speed at which machine learning (ML) teams can innovate is not just a metric of efficiency. It’s what sets industry leaders apart, empowering them to constantly improve …

Thought LeadershipFeature Platforms

Production ML: 6 Key Challenges & Insights—an MLOps Roundtable Discussion

January 24, 2024

Navigating the journey from a promising ML concept to a robust, production-ready application is filled with challenges. Teams need to establish efficient data pipelines, understand and attribute their costs, and design organizational processes that …

Thought Leadership

Why You Don’t Want to Use Your Data Warehouse as a Feature Store

November 2, 2023

Using a data warehouse as a feature store may seem like a good idea—but there are a lot of pitfalls, which we detail in this post.

Thought Leadership

Create Amazing Customer Experiences With LLMs & Real-Time ML Features

September 13, 2023

Did you know connecting large language models (LLMs) to a centralized feature platform can provide powerful, real-time insights from customer events? This post explains the benefits and how you can fit LLMs into production machine learning pipelines.

Thought LeadershipOperational & Real-Time Machine Learning

5 Ways a Feature Platform Enables Responsible AI

August 1, 2023

Generative and Responsible AI are hot topics. This blog post dives into 5 Responsible AI principles and how a feature platform helps enable them.

Thought LeadershipFeature Platforms

Machine Learning: The Past, Present, and Future

September 14, 2022

In this post, we take a look at the early days of getting ML into production, where we are today, and some predictions of what it will be like to build ML applications in the future.

Thought Leadership

Why Building Real-Time Data Pipelines Is So Hard

August 16, 2022

The hardest part of real-time machine learning is building real-time data pipelines. Learn how you can avoid common challenges in this post.

Operational & Real-Time Machine LearningThought Leadership