Exam Readiness: AWS Certified Machine Learning – Specialty

Kód kurzu: AWSCMLSPEC

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.

10 400 Kč

12 584 Kč s DPH

Nejbližší termín od Individuální

Výběr termínů

Odborní
certifikovaní lektoři

Mezinárodně
uznávané certifikace

Široká nabídka technických
a soft skills kurzů

Skvělý zákaznický
servis

Přizpůsobení kurzů
přesně na míru

Termíny kurzu

Počáteční datum: Individuální

Forma: Virtuální

Délka kurzu: 1 den

Jazyk: en

Cena bez DPH: 10 400 Kč

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Popis kurzu

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.

Cílová skupina

This course is intended for:

  • Machine learning practitioners preparing to take the AWS Certified Machine Learning – Specialty exam

Struktura kurzu

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

Předpokládané znalosti

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

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Daniel Šťastný

Daniel Šťastný

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