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AWS Machine Learning Specialty Certification Crash Course

Noah Gift

This course will cover the essentials of Machine Learning on AWS and prepare a candidate to sit for and clear the AWS Machine Learning-Specialty (ML-S) Certification exam. There are four main categories that will be covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. The final portion of the course will be to cover real world case studies of Machine Learning problems on AWS.

What you'll learn-and how you can apply it

  • Learn how to perform Data Engineering tasks on AWS
  • Learn how to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
  • Learn how to perform Machine Learning Modeling tasks on the AWS platform
  • Learn how to operationalize Machine Learning models and deploy them to production on the AWS platform
  • Learn how to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome.

This training course is for you because...

  • You are a DevOps Engineer who wants to understand how to operationalize ML workloads.
  • You are a Software Engineer who wants to master Machine Learning terminology, and practices on AWS.
  • You are a Machine Learning Engineer who wants to solidify their your knowledge on AWS Machine Learning practices.
  • You are a Product Manager who needs to understand the AWS Machine Learning lifecycle.
  • You are a Data Scientist who runs Machine Learning workloads on AWS.

Prerequisites

  • 1-2 years of experience with AWS and six months using ML tools. Ideally candidates would have already passed the AWS Cloud Practitioner cert.

Course Set-up

Recommended Preparation

Recommended Follow-up

About your instructor

  • Noah Gift is lecturer and consultant at both UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He is teaching and designing graduate machine learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities including leading a multi-cloud certification initiative for students. He has published close to 100 technical publications including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo.

    Professionally, Noah has approximately 20 years’ experience programming in Python. He is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, Microsoft MTA on Python. He has worked in roles ranging from CTO, General Manager, Consulting CTO and Cloud Architect. This experience has been with a wide variety of companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios and Linden Lab. In the last ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. Currently he is consulting startups and other companies.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Day 1

Part 1: AWS Machine Learning-Specialty (ML-S) Certification Length (90 min)

  • Get an overview of the certification
  • Use exam study resources
  • Review the exam guide
  • Learn the exam strategy
  • Learn the best practices of ML on AWS
  • Learn the techniques to accelerate hands-on practice
  • Understand important ML related services

Q&A (15 min)

Break (15 min)

Part 2: Data Engineering for ML on AWS Length (45 min)

  • Learn data ingestion concepts
  • Using data cleaning and preparation
  • Learn data storage concepts
  • Learn ETL solutions (Extract-Transform-Load)
  • Understand data batch vs data streaming
  • Understand data security
  • Learn data backup and recovery concepts

Q&A (10 min)

Break (5 min)

Part 3: Exploratory Data Analysis on AWS Length (45 min)

  • Understand data visualization: Overview
  • Learn Clustering
  • Use Summary Statistics
  • Implement Heatmap
  • Understand Principal Component Analysis (PCA)
  • Understand data distributions
  • Use data normalization techniques

Q&A (15 min)

Day 2

Part 4: Machine Learning Modeling on AWS & Operationalize Machine Learning on AWS Length (90 min)

  • Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet)
  • Use Feature Engineering
  • Train a Model
  • Evaluate a Model
  • Tune a Model
  • Understand ML Inference
  • Understand Deep Learning on AWS
  • Understand ML operations: Overview
  • Use Containerization with Machine Learning and Deep Learning
  • Implement continuous deployment and delivery for Machine Learning
  • Understand A/B Testing production deployment
  • Troubleshoot production deployment
  • Understand production security
  • Understand cost and efficiency of ML systems

Q&A (15 min)

Break (15 min)

Part 5: Create a Production Machine Learning Application Length (45 min)

  • Create Machine Learning Data Pipeline
  • Perform Exploratory Data Analysis using AWS Sagemaker
  • Create Machine Learning Model using AWS Sagemaker
  • Deploy Machine Learning Model using AWS Sagemaker

Q&A (10 min)

Break (5 min)

Part 6: Case Studies Length (45 min)

  • Sagemaker Features
  • DeepLense Features
  • Kinesis Features
  • AWS Flavored Python
  • Cloud9
  • Key Terminology

Q&A (15 min)