AWS Certified Machine Learning-Specialty (ML-S)

Video description

More Than 7 Hours of Video Instruction

Overview

This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.
  • Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.
  • Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
  • Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
  • Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.

The supporting code for this LiveLesson is located at http://www.informit.com/store/aws-certified-machine-learning-specialty-ml-s-complete-9780135556511.

About the Instructor

Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He teaches and designs graduate machine learning, AI, data science courses, and consulting on machine learning and cloud architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Academy accredited instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps.

Noah 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. Currently he consults for startups and other companies on machine learning, cloud architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent publications are Pragmatic AI: An introduction to Cloud-Based Machine Learning(Pearson, 2018) and Essential Machine Learning and AI with Python and Jupyter Notebook LiveLessons (Video Training).

Skill Level

Intermediate

What You Will Learn
  • How to perform data engineering tasks on AWS
  • How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
  • How to perform machine learning modeling tasks on the AWS platform
  • How to operationalize machine learning models and deploy them to production on the AWS platform
  • How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome
Who Should Take This Course
  • DevOps engineers who want to understand how to operationalize ML workloads
  • Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS
  • Machine learning engineers who want to solidify their knowledge about AWS machine learning practices
  • Product managers who need to understand the AWS machine learning lifecycle
  • Data scientists who run machine learning workloads on AWS
Course Requirements

One to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud Practitioner certification.

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Table of contents

  1. Introduction
    1. AWS Certified Machine Learning-Specialty (ML-S): Introduction
  2. Lesson 1: AWS Machine Learning-Specialty (ML-S) Certification
    1. Learning objectives
    2. 1.1 Get an overview of the certification
    3. 1.2 Use exam study resources
    4. 1.3 Review the exam guide
    5. 1.4 Learn the exam strategy
    6. 1.5 Learn the best practices of ML on AWS
    7. 1.6 Learn the techniques to accelerate hands-on practice
    8. 1.7 Understand important ML related services
  3. Lesson 2: Data Engineering for ML on AWS
    1. Learning objectives
    2. 2.1 Learn data ingestion concepts
    3. 2.2 Using data cleaning and preparation
    4. 2.3 Learn data storage concepts
    5. 2.4 Learn ETL solutions (Extract-Transform-Load)
    6. 2.5 Understand data batch vs data streaming
    7. 2.6 Understand data security
    8. 2.7 Learn data backup and recovery concepts
  4. Lesson 3: Exploratory Data Analysis on AWS
    1. Learning objectives
    2. 3.1 Understand data visualization: Overview
    3. 3.2 Learn Clustering
    4. 3.3 Use Summary Statistics
    5. 3.4 Implement Heatmap
    6. 3.5 Understand Principal Component Analysis (PCA)
    7. 3.6 Understand data distributions
    8. 3.7 Use data normalization techniques
  5. Lesson 4: Machine Learning Modeling on AWS
    1. Learning objectives
    2. 4.1 Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet)
    3. 4.2 Use Feature Engineering
    4. 4.3 Train a Model
    5. 4.4 Evaluate a Model
    6. 4.5 Tune a Model
    7. 4.6 Understand ML Inference
    8. 4.7 Understand Deep Learning on AWS
  6. Lesson 5: Operationalize Machine Learning on AWS
    1. Learning objectives
    2. 5.1 Understand ML operations: Overview
    3. 5.2 Use Containerization with Machine Learning and Deep Learning
    4. 5.3 Implement continuous deployment and delivery for Machine Learning
    5. 5.4 Understand A/B Testing production deployment
    6. 5.5 Troubleshoot production deployment
    7. 5.6 Understand production security
    8. 5.7 Understand cost and efficiency of ML systems
  7. Lesson 6: Create a Production Machine Learning Application
    1. Learning objectives
    2. 6.1 Create Machine Learning Data Pipeline
    3. 6.2 Perform Exploratory Data Analysis using AWS Sagemaker
    4. 6.3 Create Machine Learning Model using AWS Sagemaker
    5. 6.4 Deploy Machine Learning Model using AWS Sagemaker
  8. Lesson 7: Case Studies
    1. Learning objectives
    2. 7.1 Sagemaker Features
    3. 7.2 DeepLense Features
    4. 7.3 Kinesis Features
    5. 7.4 AWS Flavored Python
    6. 7.5 Cloud9
  9. Summary
    1. AWS Certified Machine Learning-Specialty (ML-S): Summary

Product information

  • Title: AWS Certified Machine Learning-Specialty (ML-S)
  • Author(s): Noah Gift
  • Release date: February 2019
  • Publisher(s): Pearson IT Certification
  • ISBN: 0135556597