A researcher at a large company wants to run an AI workload against three different PyTorch versions to see which one performs best, or test a change to an internal library before anyone trusts it in production. Either way, they need an execution environment: a recipe for what goes in it, a place to execute that recipe, and a way to keep the result current as dependencies change. In production AI work, that environment is a container image.
You probably already build these images, whether with BuildKit wired into continuous integration (CI) or your platform team’s own tooling. Those builds can take tens of minutes or longer, so teams build a handful of execution environments carefully, then guard them instead of rebuilding on demand.
Getting the native library layer right alone can eat a day, matching a CUDA toolkit build to the right driver plus whatever compiled extensions the workload needs, on top of missing libraries and version conflicts that only show up once the image boots. A six-month-old image with three common vulnerabilities and exposures (CVEs) disclosed against its dependencies stays in production, because nobody wants to touch it and break something. So the researcher who wants to try a different PyTorch version hits the same wall everyone else does: building a new environment costs enough, in time and coordination, that people avoid doing it.
Conda environments gave Anaconda packages a place to run. Fast Bakery does the same for production AI, where the environment is a container image, and works with conda and PyPI packages alike.
What Fast Bakery does
Fast Bakery handles all three pieces: the recipe, the execution, and the lifecycle. An environment becomes something you declare, not something you hand-build and then protect. Nobody writes a Dockerfile by hand, and nobody has to remember to rebuild one. You do the machine learning. We do the infrastructure.
Request an environment on demand, and Fast Bakery resolves it and bakes it in seconds. In benchmarks, it built every test image, up to 6.7 GB, in 40 seconds or less, while a standard GitHub Actions pipeline took over 5 minutes for the largest. Each recipe resolves against whatever package channels your organization has already approved, packages your perimeter policy excludes, such as those with known CVEs or disallowed licenses, are kept out before the image gets built. The result is tagged and tracked for as long as it’s in use. An organization can run as many distinct environments as its workloads need, under the same governance.
Fast Bakery doesn’t work alone. Fast Registry stores what it builds, at a fraction of the cost of a general-purpose registry. Fast Container Runtime gets an image running on a compute node as fast as the hardware allows, cutting task startup latency by 4 to 10x in benchmarks. Together, the three cover the full lifecycle: building an environment, storing it, and running it.
Patching a vulnerable dependency
Say a CVE lands against a dependency your workloads use. Because every environment is declared rather than hand-assembled, finding which ones are pinned to the affected version means searching through code. Fixing it is a one-line version bump and a rerun. Fast Bakery resolves and bakes the patched environment in seconds and keeps both versions around, so you can compare them directly, run against run, instead of trusting that the new one behaves the same way. What used to mean coordinating a remediation effort across teams becomes something closer to routine maintenance.
Where it lives
Fast Bakery has built multi-gigabyte ML/AI images in under a minute where comparable CI pipelines took five or more, and Fast Container Runtime has cut task startup latency by 4 to 10x. See Fast, Automatic Containerization of ML and AI Projects with Fast Bakery and Faster Cloud Compute for the full picture.
Fast Bakery is standalone infrastructure, part of AI orchestration in Anaconda Platform. Point an existing CI pipeline, a notebook, or a deployment process at it, and every image it bakes carries the same channel policy and tagging, whatever generated the request.
If you want to see Fast Bakery against your own workloads, request a demo.