AWS Certified Machine Learning - Specialty Crash Training in Austin
Enroll in or hire us to teach our AWS Certified Machine Learning - Specialty Crash class in Austin, Texas 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 |
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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 |
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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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