Rishi Kundargi

What Is Data Drift in Machine Learning (and How to Detect It)

Learn what data drift is in machine learning, how it differs from concept and model drift, and how teams detect and manage drift in production ML systems.

Dani Lang

Linda Zhou

Feature Store Freshness 

Most feature stores are built around batch pipelines and materialized storage, which creates an inherent freshness ceiling. Tightening staleness settings cannot overcome architectural limits. This article explains why traditional feature stores struggle with real-time freshness and how a compute-first approach enables on-demand feature computation, lower latency, and improved correctness for time-sensitive ML use cases like fraud detection and underwriting.

Why Your Feature Store Has a Freshness Ceiling

Turo Customer Story

How Turo Built a Self-Serve ML Feature Platform for Search and Pricing With Chalk

Quarterly Product Update: Winter

A roundup of Winter 2025’s latest Chalk product updates, covering performance improvements, new integrations, and infrastructure enhancements for running fast, reliable real time data pipelines in production.

7 Common MLOps Challenges

MLOps enables teams to train and run models at scale. Learn why production ML is challenging, from feature inconsistency to fragmented data and how to solve it.

7 Common MLOps Challenges (and How to Solve Them)

Medely Customer Story

Medely staffs critical healthcare in real-time with Chalk

Quarterly Product Update: Fall

This quarter, we expanded Chalk across three core dimensions: flexibility, visibility, and efficiency. Teams can now define and serve models alongside features, trace query behavior for diagnosis, and adapt feature logic dynamically without redeploying.

iwoca Customer Story

Powering Reliable, Explainable Data for Credit Decisioning at iwoca with Chalk

Elvis Kahoro

Chalk for Data Scientists

Your notebook code is production code with Chalk. Deploy directly from notebooks and experiment safely with branches.

Moneylion Customer Story

MoneyLion delivers AI- powered personal finance products with Chalk

Chalk for Data Engineers

 Data engineers waste weeks managing ML pipelines. Learn how to escape backfill nightmares and tool sprawl with declarative features on Chalk.

Whatnot Customer Story

How Chalk fuels Whatnot’s live shopping platform

Quarterly Product Update: Summer

This quarter, we delivered platform-level improvements that extend Chalk’s ability to support complex, production-grade ML systems.

Feature store vs. Feature engine

Discover the critical differences between feature stores and feature engines. Learn when to use each approach and how feature engines enable real-time computation and rapid experimentation for ML teams.

Mission Lane Customer Story

Mission Lane’s Source of Truth for Credit Decisioning with Chalk

Chalk for AI Engineers

Learn how AI engineers use Chalk to eliminate stale context, reduce tooling complexity, and ship AI features that work consistently in production.

What is MLOps?

What is MLOps? Learn the definition, meaning, and benefits of MLOps, plus key components, challenges, and best practices for deploying and maintaining ML models.

What is MLOps? Definition, Challenges, and Best Practices

Sai Atmakuri

Which LLM Wins at Nolan Trivia?

We put Chalk’s native prompt evaluation framework to the test by benchmarking top models on a custom Christopher Nolan trivia dataset—showcasing how teams can evaluate and deploy LLM workflows with production-grade rigor.

Which LLM Wins at Nolan Trivia? Chalk’s Prompt Evaluation in Production

Marc Freed-Finnegan

$50M Series A to Power AI Inference

I'm incredibly excited to share that Chalk has raised a $50 million Series A at a $500 million valuation, led by Felicis, with participation from Triatomic Capital and our existing investors General Catalyst, Unusual Ventures, and Xfund. This funding marks a huge milestone and a powerful validation of our vision for real-time AI infrastructure.

Chalk Raises $50M Series A to Power AI Inference

Quarterly Product Update: Spring

This quarter’s updates help ML teams move faster in production with better performance, observability, and flexibility.

Nathan Fenner

Symbolic Python Interpreter

To eliminate Python’s performance bottlenecks in real-time ML, Chalk built a Symbolic Python Interpreter that converts flexible Python resolver code into optimized Velox-native expressions. This post dives into the technical journey and impact of enabling fast, parallel execution without sacrificing developer ergonomics.

Accelerating Real-Time ML with Symbolic Python Interpreter

Apartment List Customer Story

How Apartment List uses Chalk to power personalized apartment recommendations

Verisoul Customer Story

Verisoul stops fake accounts with Chalk’s real‑time feature platform

Melanie Chen

A roundup of Chalk's latest product releases, feature updates, and new documentation for the end of the year 2024.

Alexandra Kane

Vital Customer Story

Vital predicts hospital wait times with Chalk’s feature platform

A roundup of Fall 2024’s latest product releases, feature updates, and new documentation and tutorials.

Bill Qin

Building PartiQL Support at Chalk

A deep dive into how we built support for PartiQL and DynamoDB on Chalk by using DuckDB's SQL parser.

Deep Dive: Building PartiQL Support at Chalk

New product releases and changelog updates from Chalk for summer 2024.

Feature Store at Work

A Tutorial on Fraud and Risk

Machine learning has long been deeply embedded in the field of fraud detection. A deep dive into how feature stores empower ML teams to build fraud models.

Introducing Chalk NY

Today we’re thrilled to announce Chalk's expansion with the opening of a New York City office.

Samuel Mignot

What is a Feature Store?

An Architectural Deep Dive

A feature store is more than a tool – it is a secret weapon that empowers teams to innovate faster, collaborate better, and deliver production grade machine learning.

What is a Feature Store? A Complete Guide for ML Teams

$10M Seed Round to Accelerate ML

We’re proud to announce we’ve raised $10 million in seed funding led by General Catalyst, Unusual Ventures, and Xfund to build the data platform for machine learning and generative AI.

Announcing our $10M seed to accelerate machine learning

Andrew Moreland

Chalk Completes SOC 2 Type 1 Report

Chalk is proud to announce the availability of our SOC 2 Type 1 Report.

The Chalkboard

Product updates, insights, and behind-the-scenes explorations from the team — all on the Chalk blog.

Chalk Raises $50M Series A to Power AI Inference

What Is Data Drift in Machine Learning (and How to Detect It)

Feature Store Freshness

Turo Customer Story

Quarterly Product Update: Winter

7 Common MLOps Challenges

Medely Customer Story

Quarterly Product Update: Fall

iwoca Customer Story

Chalk for Data Scientists

Moneylion Customer Story

Chalk for Data Engineers

Whatnot Customer Story

Quarterly Product Update: Summer

Feature store vs. Feature engine

Mission Lane Customer Story

Chalk for AI Engineers

What is MLOps?

Which LLM Wins at Nolan Trivia?

Quarterly Product Update: Spring

Symbolic Python Interpreter

Apartment List Customer Story

Verisoul Customer Story

Quarterly Product Update: Winter

Vital Customer Story

Quarterly Product Update: Fall

Building PartiQL Support at Chalk

Quarterly Product Update: Summer

Feature Store at Work

Introducing Chalk NY

What is a Feature Store?

$10M Seed Round to Accelerate ML

Chalk Completes SOC 2 Type 1 Report

Build ML Features faster with Chalk

The Chalkboard

Product updates, insights, and behind-the-scenes explorations from the team — all on the Chalk blog.

Chalk Raises $50M Series A to Power AI Inference

What Is Data Drift in Machine Learning (and How to Detect It)

Feature Store Freshness

Turo Customer Story

Quarterly Product Update: Winter

7 Common MLOps Challenges

Medely Customer Story

Want to stay up-to-date with Chalk?

Quarterly Product Update: Fall

iwoca Customer Story

Chalk for Data Scientists

Moneylion Customer Story

Chalk for Data Engineers

Whatnot Customer Story

Quarterly Product Update: Summer

Feature store vs. Feature engine

Mission Lane Customer Story

Chalk for AI Engineers

What is MLOps?

Which LLM Wins at Nolan Trivia?

Quarterly Product Update: Spring

Symbolic Python Interpreter

Apartment List Customer Story

Verisoul Customer Story

Quarterly Product Update: Winter

Vital Customer Story

Quarterly Product Update: Fall

Building PartiQL Support at Chalk

Quarterly Product Update: Summer

Feature Store at Work

Introducing Chalk NY

What is a Feature Store?

$10M Seed Round to Accelerate ML

Chalk Completes SOC 2 Type 1 Report

Build ML Features faster with Chalk