You changed one thing. Why is your whole AI pipeline rebuilding again?
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The developer of "aimake" introduces it as an incremental build system for AI/ML pipelines, akin to "make" for AI applications. It addresses the issue of entire AI pipelines rebuilding after a single change. With "aimake," users can see what would rebuild using "aimake plan," run only stale steps with "aimake build," and understand why a step needs rebuilding via "aimake explain." The developer seeks GitHub stars if the idea is deemed useful.
I built aimake — an incremental build system for AI/ML pipelines, basically make for AI apps.
GitHub: https://github.com/arjun988/aimake
The idea is simple:
Dataset → Preprocess → Embeddings → Index → Prompt → Eval → Report
aimake builds a dependency graph, fingerprints inputs by content rather than timestamps, and only rebuilds steps whose inputs actually changed.
So if you change your prompt:
Before: Dataset ✓ Preprocess ✓ Embeddings ✓ Index ✓ Prompt ✗ changed Eval ✗ Report ✗ After: 2 rebuilt · 5 reused aimake plan # see what would rebuild aimake build # only stale steps run aimake explain # see why a step needs rebuilding
It's not Airflow (orchestration) and it's not DVC (data versioning alone).
It's make for AI pipelines: dependency graph + content fingerprints + incremental builds + caching.
Shipped so far
- Content-hash fingerprints instead of mtime-based caching
- Incremental + parallel builds
- plan / build / explain CLI
- Experiment comparison + hyperparameter search
- S3 cache + Hugging Face / DVC / Docker / Ollama / W&B plugins
You can try it with:
pip install aimake
The main reason I built this is that AI pipelines are expensive to rerun. Changing a prompt shouldn't mean recomputing your dataset, embeddings, vector index, etc.
If you build RAG , evaluation or any AI pipelines, I'd love feedback: what's the most painful step you wish was cached?
And if you think the idea is useful, a ⭐ on GitHub would really help.