Live training with Noah Gift starts on October 21st at 12 PM (EST)

Training duration: 4 hours (Hands-on)

Price with 30% discount

Regular Price: $210.00

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Instructor Bio:

MLOps Expert | Solopreneur | Author | Duke & Northwestern & UC Davis Adjunct Professor | CTO | Pragmatic AI Labs

Noah Gift

Author of Practical MLOps, Pragmatic AI, and Python for DevOps. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI.

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Learning Objectives

  • Understand what MLOps is , how to get started and best practices using MLOps

  • Perform Continuous Integration for Python ML Projects

  • Use the AWS Cloud for MLOps development

  • Create Containerized workflows for MLOps

  • Create Flask and CLI Services for Python ML Projects

DIFFICULTY LEVEL: BEGINNER

Course Outline

1. Getting Started with MLOps 


Poll: Experience Level With MLOps?

Poll: Experience Level With Cloud Computing?

- What is MLOps and how to get started

- Why Cloud-based development environments for MLOps?

Exercise: Setup AWS Cloud9 Environment

Exercise: Setup Github and Git

- Why Cloud Based continuous integration

Exercise: Setup AWS Code Build

Q&A



2. Building containerized MLOps command-line tool 


Poll: Experience level with containers?

Poll: Experience level with command-line tools?

-  Docker Overview

- Why Docker Containers vs Virtual Machines?

Exercise:  Use a Docker Container from Docker Hub

Exercise:  Extend a Docker Container

Exercise:  Build a Python click command-line tool in a container

- Common Issues Running a Docker Container

Q&A



3. Build Containerized ML Web Microservice Applications 


Poll: Experience level with running containers?

Poll: Experience level with container registries?

- Flask Microservice Overview

Exercise:  Build a Flask Docker sklearn prediction container in AWS Cloud9

Exercise:  Run a Flask Docker sklearn prediction container in AWS Cloud9

Exercise:  Verify inference response from Flask application using utilities you build yourself.

Q&A



4. Continuous Delivery Containerized App 


Poll: Experience level with building containers automatically?

Exercise:  Deploy a Docker sklearn prediction container to Docker Hub

Exercise:  Deploy a Docker sklearn prediction container to Amazon Container Registry

Exercise:  Deploy Flask ML microservice container via AWS App Runner in a CaaS (Container as a Service) Workflow

Q&A

Course Abstract

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This training takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This training gives you a head start.

What background knowledge you should have?

  • Basic Python

  • Linux and Cloud knowledge

What is included in your ticket?

  • Access to live training and QA session with the Instructor

  • Access to the on-demand recording

  • Certificate of completion

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