Build and Deploy Machine Learning Models | MLOps Overview

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=nQSDHODEFy8


Duration: 2:17
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see more details and purchase the workshop here: https://ai-nlp-mlops.eventbrite.ca/?aff=yt

Machine Learning Operations (ML-Ops) is the process of packaging and deploying ML models into production so that applications can be built on top of those.

This workshop will cover:
Environment setup (AWS, Conda, Github, Docker)
Overview of MLOps (Packaging, Serving, Infrastructure, Operational Considerations)
Cortex.dev demo
Model packaging - High-level overview
Introduction to Pickle, MLflow and ONNX
Docker - High-level overview
Creating Docker images and containers
Introduction to API’s (REST)
Introduction to Flask
Serve a model through Flask
Containerize model / API
Host on EC2 instance
Host on Fargate
Model training pipelines



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Tags:
deep learning
machine learning
natural language processing
NLP
MLOps
ML-Ops
Deployment
mlops
kubernetes
tf serving
sagemaker
pytorch
tensorflow
onnx
model deployment
workshop