Back office ops · Production

ML Kickstarter: open-source local end-to-end ML project template with RAG chatbot and training pipelines

The problem

Setting up an end-to-end ML project for personal use was too time-consuming and either too expensive (relying on cloud providers) or too painful to develop locally.

Workflow diagram · grounded in source
1
Pipeline orchestration via Prefect
trigger
“I landed on using [Prefect](https://docs.prefect.io/latest/) to trigger code runs. As this makes it easy to write almost pure Python methods, but still get observability, logging, alerting, scheduling, and way more.”
2
Incremental data materialization
integration
“Kickstarter comes with a built-in pipeline to incrementally update all data that is out of the defined freshness requirement. This will update all views but also predict for models that do not require any joins.”
3
Model training workflow
ai_action
“the workflow creates train and test datasets, does hyperparameter tuning, trains the model, stores the model, and evaluates the model”
4
Embedding generation via ollama
ai_action
“I landed on using [ollama](https://ollama.com/) to create embeddings locally, and running LLMs if wanted”
5
Vector database storage in lancedb
integration
“I also added a vector database using [lancedb](https://lancedb.github.io/lancedb/). ... This will also update its vector database every 15 minutes to make sure the context is up to date with your changes.”
6
RAG chatbot answers project questions
ai_action
“The Kickstarter also provides a simple chatbot that tries to answer questions about the project. As it chunks the project's Python code and read me files.”
7
Model serving via mlserver
output
“mlflow have a very nice integration for model serving that enables the usage of the [mlserver](https://mlserver.readthedocs.io/en/latest/getting-started/index.html) by seldon”
Reported outcome

The author produced an end-to-end ML project template with minimal build steps, a faster developer feedback loop, and a chatbot that keeps its vector database current with code changes.

Reported metrics
Developer feedback loop speedfeedback loop is faster
Vector database update frequencyevery 15 minutes
Reported stack
Dockerdocker-composewatchfilesPrefectmlflowalignedstreamlitollamalancedbmlserver
Source
https://mlops.community/blog/introducing-the-ml-kickstarter
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The author produced an end-to-end ML project template with minimal build steps, a faster developer feedback loop, and a chatbot that keeps its vector database current with code changes.

What tools did this team use?

Docker, docker-compose, watchfiles, Prefect, mlflow, aligned, streamlit, ollama, lancedb, mlserver.

What results were reported?

Developer feedback loop speed: feedback loop is faster; Vector database update frequency: every 15 minutes (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Pipeline orchestration via Prefect → Incremental data materialization → Model training workflow → Embedding generation via ollama → Vector database storage in lancedb → RAG chatbot answers project questions → Model serving via mlserver.

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