Chalk

Recently, an ML leader at one of our partners, Vital.io, summed up his Chalk experience as simply "doing more, with less code." This Thanksgiving, like every year, we couldn't be more thankful for our amazing customers we get to build alongside with everyday. But it's really the moments like these that keep us working tirelessly to simplify ML for developers everywhere. From everyone at Team Chalk, have a great and restful holiday! 🦃 🍁

The atmospheric river flooded San Francisco, so we flooded our codebase with a downpour of new updates. 🌊 ⛈️ 🌊 ⛈️ 🌊 ⛈️   💨 Underscore expressions now support more  chalk.functions for working with arrays and Dataframes, mathematical operations, encoding, formatting datetime, and strings.  😶🌫️ You can now choose whether to cache nulls or default values in the online store with the cache_nulls and cache_defaults parameters. Customers with Redis or DynamoDB online stores can also select to evict null/default feature values for any null/default feature value that would have existed in the online store.  🗺️ You can now define Chalk features as map types, for example user_preferences: dict[str, bool]  🎣 In addition, you can now retrieve Map document types from DynamoDB data sources as either dicts or strings. As always, more detail and much more linked in the full changelog in comments. From the Chalk team, have a wonderful holiday! 🦃

What's the best dashboard you've ever used? We're rebuilding ours and like to learn from the best. Some of our favorites: 1/ Attio / Alexander Christie and team have THE best global search 2/ Chalk / Elliot Marx and co have the best views for the 500+ features we have 3/ Linear / command center + key shortcuts - shoutout Tuomas Artman 4/ New Relic / most customizable and interactive charts 5/ CelerData / it's simple and powerful - no nonsense

In honor of National Apple Cider Day, National Vichyssoise Day, and National Princess Day, pour yourself some hot apple cider 🍎 and a hot bowl of potato soup 🥣, put on your tiaras 👑, and check out what we've been up to at Chalk! ⬛   👶 You can now view the Kubernetes pods created by each deployment in the dashboard along with additional details like the pod states and resources requested by each pod!  🍡 We've added the array_agg function chalk.functions to help you resolve list features with underscore expressions! To see all of the chalk.functions that you can use in underscore expressions for fast feature computation in statically compiled C++, check out our API docs!  🧾 Users can now use the chalk usage commands to view usage information for their projects and environments through the Chalk CLI. As always, the full changelog is linked below or ping us directly if you have any questions 🕶️

While everyone's excited about generative AI (everyone...everywhere..) there are two major, less sexy, challenges that often don't get discussed: Cost at Scale For high-volume applications like recommendation systems (think 300k+ predictions/second), using something like OpenAI's API would cost thousands per second. That's orders of magnitude too expensive for most use cases for most companies. Latency Issues Many applications need responses in ms. Current GenAI APIs take seconds to respond - achingly too slow for many real-time applications like detecting fraud or routing an ambulance. There's real reasons to get excited about GenAI and its application in complex and real-time predictions. The reality however? Traditional ML models still dominate production systems for good reason.

I recently had the chance to meet with Ben Shipley, Elliot Marx, and Marc Freed-Finnegan from Chalk. 🙌 They walked me through a demo of their modern data platform, and the possibilities blew me away! Their work is truly next-level, from streamlining MLOps workflows to sparking fresh ideas. They’re building something special, and I can’t wait to see how Chalk continues to innovate! If you’re exploring MLOps solutions, I highly recommend checking them out! Thanks again, Ben, Elliot, and Marc—it was a pleasure!

This week was 11/11! For the believers in the room, that means at 11:11 we made a wish. In case your wish was more updates from the Chalk team--we're here to deliver! ✨   👯 We now provide an idempotency-key parameter for triggering resolver runs so that you can ensure only one job will be kicked off for each key provided.   ✅ The ChalkClient now has a check function that you can use with pytest to run a query and validate the query outputs for integration testing.   🌐 We've added even more functions to chalk.functions to be used in underscore expressions! You can now use the mathematical functions floor, ceil, and abs, the logical functions when, then, otherwise, and is_null, and use haversine to compute the distance between two points on Earth given their latitude and longitude!   ⚙️ Users can now view the P50, P75, P95, and P99 latencies for resolvers in the table under the Resolvers tab of the menu bar by clicking on the gear icon in the top left corner of the table and selecting which columns to view. We've also added a SQL explorer in the dashboard for examining resolver output in queries that are run with the parameter store_plan_stages=True.   🩻 You can now use the chalk healthcheck command in the CLI to view information on the health of Chalk's API server and its services. The healthcheck provides information for the API server based on the active environment and project.

Team Chalk NY keeps growing! 🗽 Today we welcome Jared Gaynes as the newest member of Chalk's GTM team. Jared's started his journey to Chalk from a long history of selling complex data products at early-stage co's, including building out his last company's GTM from the ground up. Welcome aboard Jared, super stoked to have you!

Happy Halloween from the ghouls and gals of Chalk! 🎃 👻 Our dev team have some real easter egg 🥚 costumes in there, can you name them?

If there's something strange 👻 in your neighborhood 😯 Who you gonna call? ☎️ Chalk? ⬛ We're here to answer the call with some more updates on what we've shipped this week!  🧠 We've added several new functions to chalk.functions , including sagemaker_predict which allows you to run predictions against a SageMaker endpoint. The new functions expand your ability to perform encoding, decoding, datetime manipulation, string manipulation, and other mathematical operations in underscore expressions. To read more about all available functions and how to use them, check out our API docs! 🪺 You can now reference other windowed aggregations in the expressions for your windowed aggregation feature definitions! This enables you to define more complex windowed aggregation features using nested references and the _.chalk_window operator!  🎛️ We’ve updated the Usage Dashboard with a new view under the Pod Resources tab that allows you to view CPU and storage requests by pod as grouped by cluster, environment, namespace, and service! If you have any questions about the usage dashboard, please reach out to the Chalk team! See everything the team has 🚢 🚢 🚢 this week at our full changelog linked in comments!