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Getting Started
Metaflow on AWS
Developing with Metaflow
Going to Production with Metaflow
Internals of Metaflow
Why Metaflow
Here is a data scientist.
Data scientists solve important business problems.
A good way to start solving a problem is to prototype a solution in a notebook.
There are great libraries available for machine learning that make prototyping fun.
After the first experiments, it is a good idea to start keeping track of models and data.
It can take a lot of work to get the latest data and keep the models up to date reliably.
Production workflows should run on servers, not on a laptop.
These days, servers execute containers. Workflows need to be packaged for execution.
Where should the results go? Sometimes they are deployed as containers too.
Containerized models can be consumed by business applications.
Stakeholders evaluate the results. They want more models and better models!
Taking care of all this can be hard. Metaflow can help.
Last modified 2yr ago
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