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24 alternativesHands-on Train and Deploy ML
This tutorial walks through training and deploying a machine learning REST API that predicts cryptocurrency prices in ten steps.
About Hands-on Train and Deploy ML
Hands-on Train and Deploy ML is an open-source repository with a tutorial for training and deploying a machine learning REST API that predicts cryptocurrency price movements. It covers fetching historical data from Coinbase, creating features and targets, building a baseline model, experimenting with ML models using Python scripts and CometML, and deploying the model with Cerebrium.
It also covers automating redeployment with GitHub Actions and the Comet ML Model Registry. The author states the tutorial does not produce a profitable trading system and requires API keys for CometML and Cerebrium.
Key features
- Fetch Coinbase historical data and generate training features
- Build baseline model before testing ML models
- Track ML experiments with CometML
- Deploy selected model as serverless REST API via Cerebrium
- Test deployed endpoint with provided command
- Automate redeployment with GitHub Actions and registry webhook
Best for: People learning MLOps who want practice training models outside notebooks and deploying them as an API.
Good for
- Open source under MIT license
- Uses a serverless stack without maintaining infrastructure
- Includes Makefile commands for data, training, deployment and endpoint testing
Watch out for
- Author states the resulting model will not make users rich
- Requires API keys for CometML and Cerebrium to follow the steps
Hands-on Train and Deploy ML at a glance
- Category
- Education & Learning › Learn to Code
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hands-on Train and Deploy ML FAQ
What tools does the tutorial use?
CometML for experiment tracking and model registry, Cerebrium for deployment, and GitHub Actions for automation.
What do I need to run the full example?
A Python virtual environment with project dependencies, API keys for Comet ML and Cerebrium, and historical data from Coinbase downloaded with the provided commands.
Will this tutorial produce a profitable crypto model?
No. The repository states it teaches MLOps workflows and does not build a system that will make you rich.
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