GB
/
GBP
/
EN_GB

Shaping the future of IT skills

Maximising IT performance through learning

Exam Readiness: AWS Certified Machine Learning - Specialty

WGAC-AWS-ERMLS

Amazon Web Services
Open

Exam Readiness: AWS Certified Machine Learning - Specialty

26 Aug 2022 - 1 day

English

GMT UTC+00:00

£815

Open

Exam Readiness: AWS Certified Machine Learning - Specialty

16 Sep 2022 - 1 day

Italian

CET UTC+01:00

£510

Open

Exam Readiness: AWS Certified Machine Learning - Specialty

11 Nov 2022 - 1 day

Italian

CET UTC+01:00

£510

Open

Exam Readiness: AWS Certified Machine Learning - Specialty

01 Dec 2022 - 1 day

English

GMT UTC+00:00

£815

Open

Exam Readiness: AWS Certified Machine Learning - Specialty

23 Dec 2022 - 1 day

Italian

CET UTC+01:00

£510

Description

Show Tabs
Introduction

Day One

Module 0: Course Introduction

Module 1: Exam Overview and Test-taking Strategies

  • Exam overview, logistics, scoring, and user interface
  • Question mechanics and design
  • Test-taking strategies

Module 2: Domain 1: Data Engineering

  • Domain 1.1: Data Repositories for machine learning
  • Domain 1.2: Identify and implement a data-ingestion solution
  • Domain 1.3: Identify and implement a data-transformation solution
  • Walkthrough of study questions
  • Domain 1 quiz

Module 3: Domain 2: Exploratory Data Analysis

  • Domain 2.1: Sanitize and prepare data for modeling
  • Domain 2.2: Perform featuring engineering
  • Domain 2.3: Analyze and visualize data for ML
  • Walkthrough of study questions
  • Domain 2 quiz

Module 4: Domain 3: Modeling

  • Domain 3.1: Frame business problems as machine learning (ML) problems
  • Domain 3.2: Select the appropriate model(s) for a given ML problem
  • Domain 3.3: Train ML models
  • Domain 3.4 Perform hyperparameter optimization
  • Domain 3.5 Evaluate ML models
  • Walkthrough of study questions
  • Domain 3 quiz

Module 5: ML Implementation and Operations

  • Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
  • Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem
  • Domain 4.3: Apply basic AWS security practices to ML solutions
  • Domain 4.4: Deploy and operationalize ML solutions
  • Walkthrough of study questions
  • Domain 4 quiz

Module 6: Comprehensive study questions

Module 7: Study Material

Module 8: Wrap-up

Prerequisites & Audience

We recommend that attendees of this course to have:

  • One or two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS cloud.
  • Proficiency at expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization
  • Understanding of complete ML pipeline and its components
  • Experience with ML and deep learning frameworks
  • Understanding and applying model training, deployment and operational best practices
Course Benefits

This course is designed to teach you how to:

  • Identify their strengths and weaknesses in each of the exam domains.
  • Create a subsequent study plan to prepare for the exam.
  • Describe the technical topics and concepts making up each of the exam domains.
  • Summarize the logistics and mechanics of the certification exam and its questions.
  • Identify effective test taking strategies that can be used to answer exam questions.
Course Topics

The AWS Certified Machine Learning – Specialty exam validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) or deep learning (DL) solutions for given business problems.

People with one to two years of experience developing, architecting, or running ML/DL workloads on the AWS cloud should join this workshop to learn how to prepare to successfully pass the exam.

The workshop explores the exam’s topic areas, shows how they relate to machine learning on AWS, and also maps them to machine learning and deep learning foundational areas for future self-study. It includes sample exam questions from each domain and discussions of concepts being tested to help test-takers better eliminate incorrect responses.

Topics in the course will address each of the exam’s four subject domains. 1. Data Engineering 2. Exploratory Data Analysis 3. Modeling 4. Machine Learning Implementation and Operations

Amazon Web Services courses


Building Data Analytics Solutions Using Amazon Redshift
CODE: WGAC-AWS-BDASAR
Architecting on AWS Accelerator
CODE: WGAC-AWS-ARCH-AX
Architecting on AWS
CODE: WGAC-AWS-AWSA
AWS Well-Architected Best Practices
CODE: WGAC-AWS-WABP
Video Streaming Essentials for AWS Media Services
CODE: WGAC-AWS-VSEAMS
Building Data Lakes on AWS
CODE: WGAC-AWS-BDLA
Migrating to AWS
CODE: WGAC-AWS-AWSM
AWS Security Governance at Scale
CODE: WGAC-AWS-SGS
AWS Cloud for Finance Professionals
CODE: WGAC-AWS-CFP
Exam Readiness: AWS Certified Database – Specialty
CODE: WGAC-AWS-ACDS-EX
Advanced AWS Well-Architected Best Practices
CODE: WGAC-AWS-AWABP
AWS Cloud Essentials for Business Leaders
CODE: WGAC-AWS-CEBL
Building Batch Data Analytics Solutions on AWS
CODE: WGAC-AWS-BBDAS
AWS Cloud Ready Hackathon: Coding and Testing on Linux - AWSHCTL
CODE: WGAC-AWS-AWSHCTL
Security Engineering on AWS
CODE: WGAC-AWS-AWSSO
Planning and Designing Databases on AWS
CODE: WGAC-AWS-PD-DB
Deep Learning on AWS
CODE: WGAC-AWS-AWSDL
AWS Technical Essentials
CODE: WGAC-AWS-AWSE
Exam Readiness: AWS Certified Solutions Architect – Professional
CODE: WGAC-AWS-ACSAP-EX
Data Warehousing on AWS
CODE: WGAC-AWS-DWAWS
AWS Security Best Practices
CODE: WGAC-AWS-SBP
AWS Discovery Day (3 hours) - AWSDD3H
CODE: WGAC-AWS-AWSDD3H
Machine Learning Pipeline on AWS
CODE: WGAC-AWS-ML-PIPE
Exam Readiness Intensive Workshop: AWS Certified Solutions Architect – Associate
CODE: WGAC-AWS-ACSAA-EXIW
AWS Security Essentials
CODE: WGAC-AWS-SEC-ESS
Exam Readiness: AWS Certified Advanced Networking - Specialty
CODE: WGAC-AWS-ACANS-EX
AWS Discovery Day - AWSDD
CODE: WGAC-AWS-AWSDD
Exam Readiness: AWS Certified Security - Specialty
CODE: WGAC-AWS-ACSS-EX
DevOps Engineering on AWS
CODE: WGAC-AWS-AWSDEVOPS
Running Containers on Amazon Elastic Kubernetes Service
CODE: WGAC-AWS-RCAEKS
MLOps Engineering on AWS
CODE: WGAC-AWS-MLOE
AWS Cloud Ready Hackathon: Containers, Kubernetes CI & CD - AWSHCKC
CODE: WGAC-AWS-AWSHCKC
Big Data on AWS
CODE: WGAC-AWS-BDAWS
Systems Operations on AWS
CODE: WGAC-AWS-AWSSYS
Developing on AWS
CODE: WGAC-AWS-AWSD
AWS Cloud Practitioner Essentials
CODE: WGAC-AWS-CP-ESS
Advanced Architecting on AWS - AAAWS
CODE: WGAC-AWS-AAAWS
Exam Readiness: AWS Certified Developer – Associate
CODE: WGAC-AWS-ACDA-EX
Exam Readiness: AWS Certified Machine Learning - Specialty
CODE: WGAC-AWS-ERMLS
Exam Readiness: AWS Certified Data Analytics – Specialty
CODE: WGAC-AWS-ACDAS-EX
Exam Readiness: AWS Certified Solutions Architect – Associate
CODE: WGAC-AWS-ACSAA-EX
AWS Cloud Ready Hackathon: Running Cloud Workloads with Kubernetes - AWSHRWK
CODE: WGAC-AWS-AWSHRWK
AWS Cloud Essentials for Business Leaders – Financial Services
CODE: WGAC-AWS-CEBL-FS
Advanced Architecting on AWS
CODE: WGAC-AWS-AWSAA
Deep Learning on AWS - AWSDL
CODE: WGAC-CSC-AWSDL
Exam Readiness: AWS Certified DevOps Engineer – Professional
CODE: WGAC-AWS-ACDOEP-EX
Practical Data Science with Amazon SageMaker
CODE: WGAC-AWS-PDSASM
Advanced Developing on AWS
CODE: WGAC-AWS-ADV-DEV
We use cookies to understand how you use our site and to improve your experience. To learn more, click here. Read our revised Privacy Policy and Terms and Conditions.