LIVE TRAINING
SAVE THE DATE: October 21th, 2021 @12pm EST
Noah, a Python Software Foundation Fellow works extensively with AWS and is an AWS Machine Learning Hero, and he is the author of numerous books including Practical MLOps, Pragmatic A.I.: An introduction to Cloud-Based Machine Learning and Cloud Computing for Data Analysis.
LIVE TRAINING: Introduction to PYTHON for Programming
October 12th @12 PM EST
Price: $147
Regular price $210, discounted 10%
4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion
30% Discount Ends in:
Subscribe and get an additional 10% to 35% off ALL live training session
Pricing: $189 $139
Regular price $210, discounted 30%
*Use Voucher code for additional $50 off: october2021
4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion
LAST CHANCE 10% Discount Ends in:
Subscribe and get an additional 10% to 35% off ALL live training session
Meet Your Instructor
Noah Gift
Noah Gift is the founder of Pragmatic A.I. Labs. 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.
Course Overview
What’s the plan?
By the end of this live, hands-on, online course, you’ll understand: What MLOps is, how to get started using MLOps and best practices for MLOps. You’ll be able to: 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.
This training is for you because you’re a data scientist, software engineer, or Python programmer, you work with machine learning, data or software and you want to become an MLOps practitioner.
Course Outline
What’s the plan?
Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.
Course Overview
Python is a powerful tools that XXX
Join the live session with Noah Gift
SAVE 10%Course Outline
Module 1:
Getting Started with MLOps
- Getting Started with MLOps (55 minutes)
- Poll: Experience Level With MLOps?
- Poll: Experience Level With Cloud Computing?
- Presentation: What is MLOps and how to get started
- Presentation: Why Cloud-based development environments for MLOps?
- Exercise: Setup AWS Cloud9 Environment
- Exercise: Setup Github and Git
- Presentation: Why Cloud Based continuous integration
- Exercise: Setup AWS Code Build
Module 2:
Building Containerized MLOps Command-Line Tool
- Building containerized MLOps command-line tool (55 minutes)
- Poll: Experience level with containers?
- Poll: Experience level with command-line tools?
- Presentation: Docker Overview
- Presentation: 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
- Presentation: Common Issues Running a Docker Container
Module 3:
Build Containerized ML Web Microservice Applications
- Build Containerized ML Web Microservice Applications (55 minutes)
- Poll: Experience level with running containers?
- Poll: Experience level with container registries?
- Presentation: 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.
Module 4:
Continuous Delivery Containerized App
- Continuous Delivery Containerized App (60 minutes)
- 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
Lists
Slicing
Dictionaries
Comprehensions
Exercise
Key Details
DATE
TIME:
DURATION:
LEVEL:
OCTOBER 21ST, 2021
TIME: 12 PM EST, 9 AM PST
4 HOURS
BEGINNER
Prerequisites
- Basic Python, Linux and Cloud knowledge.
Upcoming Live Training
October 28th
Gradient Boosting for Prediction and Inference
By the end of this live, hands-on, online course, you’ll understand in detail how Gradient Boosting models are fit as an ensemble of decision trees and apply that understanding to the feature engineering process, the various parameters of Gradient Boosting and their relative importance and how to appropriately choose them and gain familiarity with the various Gradient Boosting packages and the capabilities, strengths, and weaknesses of each. You will also learn how to interpret, understand, and evaluate a model: both qualitatively and quantitatively.
Upcoming Live Training
XXth
XXX
XXX.