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 staffs critical healthcare in real-time with Chalk

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.

Quarterly Product Update: Fall

How Mission Lane manages thousands of ML features in production using Chalk. Demo recap covering feature reusability, live decisioning, and batch workflows. 

Mission Lane demo: Scaling ML with thousands of features

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

Chalk named to Fast Company's Next Big Things in Tech 2025 for helping AI teams get fresh data to their models in real-time.

Chalk Named to Fast Company's Next Big Things in Tech 2025

Chalk runs inside your VPC on Kubernetes so sensitive data stays resident, latency drops, and ML teams move faster. Hear the full story on the podcast.

Deploy where your data lives: Why Chalk runs in your cloud

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

Chalk for Data Scientists

MoneyLion delivers AI- powered personal finance products with Chalk

CEO Marc on The Information TV unpacks why winning at AI isn't about bigger models but smarter infrastructure that keeps data and compute in your cloud.

CEO Marc on The Information TV

Our CEO Marc Freed-Finnegan joins This Week in Startups (TWiST) to explain why AI needs fresh data fast. Learn how Chalk eliminates the speed vs. freshness tradeoff.

Chalk on TWiST500

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

Chalk for Data Engineers

How Chalk fuels Whatnot’s live shopping platform

The Chalk team went to SciPy 2025 in Tacoma and came back inspired. Here's our recap of all the advances we saw in Python's data ecosystem.

SciPy 2025 Recap

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

Quarterly Product Update: Summer

Verisoul shows how to catch sophisticated fraudsters by using Chalk to combine ML features with LLM intelligence. See how they rapidly iterated through three different techniques in under an hour.

Verisoul Demo Recap

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.

Feature store vs. Feature engine

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

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

Chalk for AI Engineers

What is MLOps? Explore how data scientists, data engineers, AI engineers, and MLOps teams use Chalk to eliminate handoffs,prevent feature rewrites, and ship ML models faster.

What is MLOps?

Insights from Data Council 2025 on the shift toward real-time inference, declarative data architectures, Python-first interfaces, and simplified data stacks—plus a look at the community shaping the future of data.

What Data Council 2025 taught us about the future of data

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

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

Chalk's team recaps their April 2025 tour across major AI conferences, sharing what they learnt on real-time ML infrastructure and LLM integration.

Chalk on the Move: April Events Recap

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

Quarterly Product Update: Spring

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

How Apartment List uses Chalk to power personalized apartment recommendations

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

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

Quarterly Product Update: Winter

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.

Chalk and Elliot Marx won Best Technology at The GenAI Collective's Demo Day by showing how Chalk's feature store can be used to unify traditional machine learning features with generative AI for parsing unstructured data.

Chalk wins Best Technology at The GenAI Collective's Demo Day

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.

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.

Feature Store at Work

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

Introducing Chalk NY

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

Today we're proud to announce CB Insights recognized Chalk alongside Databricks in its 2024 AI 100 as one of the leading ML / AI Development Platforms globally.

Chalk and Databricks selected as leading ML / AI Platforms in 2024 CB Insights AI 100

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

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

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