AWS Certified Machine Learning – Specialty Training

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Course Highlights

  • Instructor-led Online Training
  • Project Based Learning
  • Certified & Experienced Trainers
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support

AWS Certified Machine Learning – Specialty Training Course Overview

The AWS Certified Machine Learning – Specialty course by Multisoft Systems provides a comprehensive pathway for professionals looking to advance their expertise in machine learning on the AWS platform. Designed for individuals with foundational knowledge in ML and AWS, this training program covers a broad spectrum of essential skills, from data engineering and model building to deployment and optimization within the AWS ecosystem. Participants will delve into topics like data preprocessing, feature engineering, and model tuning, leveraging AWS's machine learning services such as Amazon SageMaker, AWS Glue, and Amazon Rekognition. The course emphasizes hands-on experience, allowing learners to build, train, and deploy scalable ML models directly on AWS. Key aspects such as model evaluation, troubleshooting, and performance optimization are also included to ensure participants gain a holistic understanding of machine learning workflows on AWS.

This program is ideal for data scientists, developers, and machine learning engineers aiming to become certified in AWS Machine Learning Specialty, equipping them with practical skills to solve real-world business challenges. By the end of the course, participants will be well-prepared for the AWS Certified Machine Learning – Specialty exam, ready to apply AWS’s powerful ML capabilities to innovate and drive success within their organizations.

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AWS Certified Machine Learning – Specialty Training Course curriculum

Curriculum Designed by Experts

The AWS Certified Machine Learning – Specialty course by Multisoft Systems provides a comprehensive pathway for professionals looking to advance their expertise in machine learning on the AWS platform. Designed for individuals with foundational knowledge in ML and AWS, this training program covers a broad spectrum of essential skills, from data engineering and model building to deployment and optimization within the AWS ecosystem. Participants will delve into topics like data preprocessing, feature engineering, and model tuning, leveraging AWS's machine learning services such as Amazon SageMaker, AWS Glue, and Amazon Rekognition. The course emphasizes hands-on experience, allowing learners to build, train, and deploy scalable ML models directly on AWS. Key aspects such as model evaluation, troubleshooting, and performance optimization are also included to ensure participants gain a holistic understanding of machine learning workflows on AWS.

This program is ideal for data scientists, developers, and machine learning engineers aiming to become certified in AWS Machine Learning Specialty, equipping them with practical skills to solve real-world business challenges. By the end of the course, participants will be well-prepared for the AWS Certified Machine Learning – Specialty exam, ready to apply AWS’s powerful ML capabilities to innovate and drive success within their organizations.

  • Understand foundational machine learning concepts and how they apply to AWS services.
  • Learn to prepare, clean, and process datasets for training on AWS.
  • Develop skills in feature engineering to improve model performance.
  • Gain hands-on experience with Amazon SageMaker for model building, training, and deployment.
  • Master AWS tools for data engineering, such as AWS Glue and Amazon Athena.
  • Learn model tuning techniques, including hyperparameter optimization, to enhance accuracy.
  • Understand model evaluation metrics and performance assessment on AWS.
  • Troubleshoot common issues in machine learning workflows on AWS.

Course Prerequisite

  • Basic understanding of machine learning concepts and algorithms
  • Familiarity with Python programming

Course Target Audience

  • Data Scientists
  • Machine Learning Engineers
  • AI Developers
  • Data Engineers
  • Solutions Architects
  • IT Professionals with ML knowledge
  • AWS Practitioners focusing on ML
  • Business Analysts interested in ML on AWS

Course Content

  • The AWS Certified Machine Learning Specialty Exam
  • Study Guide Features
  • AWS Certified Machine Learning Specialty Exam Objectives

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  • Amazon Rekognition
  • Amazon Textract
  • Amazon Transcribe
  • Amazon Translate
  • Amazon Polly
  • Amazon Lex
  • Amazon Kendra
  • Amazon Personalize
  • Amazon Forecast
  • Amazon Comprehend
  • Amazon CodeGuru
  • Amazon Augmented AI
  • Amazon SageMaker
  • AWS Machine Learning Devices
  • Summary
  • Exam Essentials

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  • Storage
  • Amazon VPC
  • AWS Lambda
  • AWS Step Functions
  • AWS RoboMaker
  • Summary
  • Exam Essentials

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  • Phases of ML Workloads
  • Business Problem Identification
  • Summary
  • Exam Essentials

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  • ML Problem Framing
  • Recommended Practices
  • Summary
  • Exam Essentials

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  • Basic Data Concepts
  • Data Repositories
  • Data Migration to AWS
  • Summary
  • Exam Essentials

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  • Data Preparation Tools
  • Summary
  • Exam Essentials

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  • Feature Engineering Concepts
  • Feature Engineering Tools on AWS
  • Summary
  • Exam Essentials

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  • Common ML Algorithms
  • Local Training and Testing
  • Remote Training
  • Distributed Training
  • Monitoring Training Jobs
  • Debugging Training Jobs
  • Hyperparameter Optimization
  • Summary
  • Exam Essentials

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  • Experiment Management
  • Metrics and Visualization
  • Summary
  • Exam Essentials

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  • Deployment for AI Services
  • Deployment for Amazon SageMaker
  • Advanced Deployment Topics
  • Summary
  • Exam Essentials

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  • Integration with On-Premises Systems
  • Integration with Cloud Systems
  • Integration with Front-End Systems
  • Summary
  • Exam Essentials

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  • Operational Excellence on AWS
  • Summary
  • Exam Essentials

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  • Security and AWS
  • Secure SageMaker Environments
  • AI Services Security
  • Summary
  • Exam Essentials

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  • Reliability on AWS
  • Change Management for ML
  • Failure Management for ML
  • Summary
  • Exam Essentials

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  • Performance Efficiency for ML on AWS
  • Summary
  • Exam Essentials

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  • Common Design Principles
  • Cost Optimization for ML Workloads
  • Summary
  • Exam Essentials

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  • New Services and Features Related to AI Services
  • New Features Related to Amazon SageMaker
  • Summary
  • Exam Essentials

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AWS Certified Machine Learning – Specialty Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

  • User-friendly interface for easy navigation
  • Secure login and authentication measures to protect data
  • Automated scoring and grading to save time
  • Time limits and countdown timers to manage duration.
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AWS Certified Machine Learning – Specialty Corporate Training

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Multisoft Systems is the “one-top learning platform” for everyone. Get trained with certified industry experts and receive a globally-recognized training certificate. Some Multisoft Training Certificate Features :

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  • Course ID & Course Name
  • Certificate with Date of Issuance
  • Name and Digital Signature of the Awardee
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AWS Certified Machine Learning – Specialty Training FAQ's

This certification validates advanced expertise in building, training, and deploying machine learning models on AWS, focusing on practical, scalable solutions.

This course is ideal for data scientists, machine learning engineers, data engineers, AI developers, and AWS practitioners interested in applying ML on AWS.

Yes, foundational knowledge of machine learning, experience with Python, and familiarity with AWS basics are recommended.

Key services include Amazon SageMaker, AWS Glue, Amazon Rekognition, and Amazon S3, among others.

To contact Multisoft Systems you can mail us on info@multisoftsystems.com or can call for course enquiry on this number +91 9810306956

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