Alkemy
Agents write the code. Alkemy makes sure they don't skip the science.
Alkemy is a framework for building reliable machine learning (ML) projects with AI coding agents. It integrates with the coding agent you already use and gives every ML project the same structure: a standard layout and a fixed sequence of steps covering datasets, experimentation, validation, deployment, and ongoing operations.
ALKEMY
How it works
Alkemy provides the path to ensure the agent follows all the right steps, in the right order. This same structure allows agents to execute machine learning operations (MLOps) reliably: connecting the model to live data, executing experiments, and tracking versions so you always know which model produced which result. Here's how we do it:
SET UP
PROJECTS
Establish your environment for model development.
BUILD
DATASETS
Create any number of training datasets to test new model features.
RUN
EXPERIMENTS
Build a variety of models to test performance, including benchmark models to beat.
MANAGE
VERSIONS
Keep the history and progression of your code, configurations, and model artifacts.
DEPLOY AND
OPERATIONALIZE
Create a deployment for your best model so that it's ready to score live data.
Video Transcript
[00:00] Introduction Fabricks supports teams at every stage of their data journey. If you need a foundation, Foundry sets it up fast. If you already have one, Alkemy plugs into it. [00:12] Streamlined Machine Learning Workflows Machine learning projects slow down when the workflow is full of friction. Alkemy gives you streamlined workflows at every stage: datasets, experiments, and deployments. Use any library and stay focused on driving value while Alkemy handles the rest. [00:29] Accelerating Machine Learning Projects Let's take a look at how Alkemy accelerates machine learning projects. Alkemy datasets are structured and versioned. You define it once and reuse it across experiments. Experiments encode a familiar workflow that encourages best practices. At every step, outputs are saved automatically, so results are reproducible. With Alkemy's built-in reporting, it's easy to compare experiments, see what works, and plan your next move. [01:03] Deployment and Versioning When you're ready to deploy, Alkemy packages your model into a portable file. You get a production-ready API and scalable batch scoring out of the box. Adjust your dataset, run more experiments, update your deployment, and you're ready to ship a new version. Alkemy removes friction so your machine learning projects deliver real results.
BENEFITS
Why Alkemy?
When AI can produce so much so quickly, how do you make sure quality keeps up?
Building an ML project with only an AI coding agent can be fast, but it also introduces risk. Agents tend to optimize for the fastest path to a plausible answer, and in machine learning, plausible can still be misleading. A result may look promising in testing, while a skipped step, weak assumption, or shortcut creates costly problems in production.
Alkemy brings structure and rigor to AI-assisted ML, making it easier to build models that are accessible, reproducible, and ready to evolve. If you have a mountain of data but still feel like you’re guessing at the answers, Alkemy can help you turn that data into systems that learn over time and make data-driven decision-making a standard part of how your business operates.
Here are the key benefits of using Alkemy:
STRUCTURED
Don’t let your agents skip important steps or settle for plausible guesses. Alkemy keeps agents on the right path as they build models, so you can trust the outputs at the end.
ACCESSIBILE
Whether you’re new to ML or an experienced modeler, Alkemy provides a clear path to high-quality models that help you get more value from your existing data.
REPRODUCIBILE
Alkemy’s structure simplifies MLOps. Code and data are versioned, artifacts are organized, and agents journal their progress so experiments can be understood and reproduced.
FLEXIBILITY
Move quickly from idea to production, then adapt as conditions change. Alkemy supports fast experimentation and iteration without sacrificing structure or control.
PRICING
Choose the level of support that fits your team.
Simple, predictable pricing from self-service to hands-on support.
PROFESSIONAL PLAN
$100
/ month
SINGLE-USER LICENSE
For experienced tech folks who have implemented ML before and seek an independent experience
TEAM PLAN
$2000-$5000
/ month
BASED ON SERVICE LEVELS
For small teams who are implementing or revisiting their ML practice, comes with support and onboarding sessions from Team d²l
CUSTOM PLAN
For those with advanced needs or who would like more hands-on help with their first few models