HARTMANN software group

AWS Certified Machine Learning - Specialty Crash Training in Redwood City

Enroll in or hire us to teach our AWS Certified Machine Learning - Specialty Crash class in Redwood City, California by calling us @303.377.6176. Like all HSG classes, AWS Certified Machine Learning - Specialty Crash may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, AWS Certified Machine Learning - Specialty Crash may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  
This live virtual training course covers the essentials of machine learning on AWS and prepares a candidate to sit for and clear the AWS Machine Learning-Specialty (ML-S) certification exam. Instructor Noah Gift covers the four core areas of the certification: Data Engineering, EDA (Exploratory Data Analysis), Machine Learning Modeling, and ML Operations. The final portion of the course covers real-world case studies of Machine Learning problems on AWS.----------------------------- This Live Virtual Training is for: - AWS Certified Machine Learning - Specialty certification candidates. - DevOps engineers who want to understand how to operationalize ML workloads. - Software engineers who want to master machine learning terminology and practice on AWS. - Machine learning engineers who want to solidify knowledge on AWS Machine Learning practices.----------------- You will learn: - How to perform Data Engineering tasks and Machine Learning Modeling tasks on the AWS platform - How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS 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
Course Length: 2 Days
Course Tuition: $970 (US)

Prerequisites
1-2 years of experience with AWS and 6 months using ML tools. - Ideally, candidates would have already passed the AWS Cloud Practitioner certification.

Course Outline

  
Part 1: AWS Machine Learning-Specialty (ML-S) Certification (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
 
QA (15 min)
Break (15 min)
 
Part 2: Data Engineering for ML on AWS (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
 
QA (10 min)
Break (5 min)
 
Part 3:  Exploratory Data Analysis on AWS (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
 
QA (15 min)
  
Part 4: Machine Learning Modeling on AWS & Operationalize Machine Learning on AWS (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
 
QA (15 min)
Break (15 min)
 
Part 5: Create a Production Machine Learning Application (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
 
QA (10 min)
Break (5 min)
 
Part 6:  Case Studies (45 min)
-      SageMaker Features
-      DeepLense Features
-      Kinesis Features
-      AWS Flavored Python
-      Cloud9
 
QA (15 min)

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