MLOps Tutorial - Building a CI/ CD Machine Learning Pipeline

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Within machine learning, the hardest aspect often becomes deploying to production, until the time comes to address the issue. Applied at scale, this issue can hinder deployment, and at the worst, kill the project entirely.

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In this video, we will learn:
- State of Machine Learning
- Why do ML projects & teams struggle to reach production?
- Working as a single data scientist at a startup
- ML project management
- Demo - Building a CI/ CD ML Pipeline
- Monitoring (tentative)
- Cloud best practices

Deploying AI/ML based applications is far from trivial. On top of the traditional DevOps challenges, you need to foster collaboration between multidisciplinary teams (data-scientists, data/ML engineers, software developers and DevOps), handle model and experiment versioning, data versioning, etc. Most ML/AI deployments involve significant manual work, but this is changing with the introduction of new frameworks that leverage cloud-native paradigms, Git and Kubernetes to automate the process of ML/AI-based application deployment.

#MLOps #Machinelearning #CICD

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I admire you. Doing the data / engineering / Data Science / MLOPs is one job role is really challenging.

jamespaz

I wish this video showed me how to actually set all this stuff up on Fargate etc - I'd love that if possible!

dhruvghulati

amazing tutotial!

a couple of questions from someone not experienced in the cloud:
1) the 50$ that you mentioned for AWS is just to have the pipeline running per month right?
2) would it be possible to have it in streamlit, heroku, ... for free? if yes, can you point in a specific direction to find more about it? for small projects to practice it mlops it would be great
3) how would mlflow play a role in this lifecycle?
thanks!

jaimerv

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