Train and Deploy an ML REST API to predict crypto prices, in 10 steps
MIT License
Predicting crypto price movements is extremely hard. But it is also a great field to show what Machine Learning has to offer.
In this tutorial you won't build an ML system that will make you rich. But you will master the MLOps frameworks and tools you need to build ML systems that, together with tons of experimentation, can take you there.
With this hands-on tutorial, I want to help you grow as an ML engineer and go beyond notebooks.
You will learn to
Without further ado, let's get to work!
We will use a 100% Serverless stack, so you don't need to set up and maintain infrastructure
If you only have 5 minutes and want to see the whole system in action, follow these steps:
Create a Python virtual environment with all project dependencies with
$ make init
Set your API keys for Comet ML and Cerebrium in set_environment_variables_template.sh
, rename the file as set_environment_variables.sh
and run it
$ . ./set_environment_variables.sh
Download historical data from Coinbase and save it locally to disk
$ make data
Train ML model
$ make train
Deploy the model
$ make deploy
Take the endpoint URL you get from Cerebrium in the previous step, and set the CEREBRIUM_ENDPOINT_URL
variable in set_environment_variables.sh
. Then re-run
$ . ./set_environment_variables.sh
Test the endpoint works
$ make test-endpoint
In this first lecture you will
In this lecture you won't train an ML model that will make you rich. But you will master the framework and skillset you need if you want to build ML models that, together with tons of experimentation, can take you there.
Ready to get your hands-dirty? Start training models 👩💻👨🏽💻 🏋️
In this second lecture you will
deploy the best ML model you found in lecture 1 as a REST API using Cerebrium
test the endpoint works.
Ready to deploy? Start deploying models 👩💻👨🏽💻 🚀
ML models often need to be re-trained to keep them performant. Hence, automating safe deployments is a must.
In this third and final lecture you will
Ready for the final round? Start automating safe deployments 👩💻👨🏽💻 🪝
Wanna learn to build a complete ML system that
using MLOps best practices?
I am preparing a new hands-on tutorial where you will learn all this.
Subscribe to The Real-World ML Newsletter to be notified when the tutorial is out.