The Machine Learning Pipeline on AWS The Machine Learning Pipeline on AWS

The Machine Learning Pipeline on AWS

The Machine Learning Pipeline on AWS

Course Provider

The Machine Learning Pipeline on AWS

Course Highlights:

  • End-to-end ML on AWS
  • Data Prep
  • Model Training
  • Deployment
  • AWS services: SageMaker, Lambda, S3.
  • Real-world applications.
  • Certificate course on the Machine Learning Pipeline on AWS explores to solve a real business problem in a project-based learning environment.
  • Students will learn about each phase of the Machine Learning Pipeline on AWS Course Certification Online from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays.
  • By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
  • Job Roles that one I apply for post course completion – Developers, Solutions architects, Data engineers.

What will you learn in this The Machine Learning Pipeline on AWS course?

  • Data Preparation: Learn how to collect, clean, and preprocess data, ensuring it's ready for machine learning tasks.
  • Model Development: Explore various machine learning algorithms and options to build accurate predictive models.
  • Model Training: Understand how to use AWS services like SageMaker to efficiently train and fine-tune your models.
  • Model Deployment: Deploy machine learning models to production using AWS infrastructure, making them accessible to users and applications.

The Machine Learning Pipeline on AWS

  • skill-type-icon Skill Type
    Emerging Tech
  • domain-icon Domain
    Big Data Analytics
  • course-category-icon Course Category
    Deepskilling Course
  • certified-earned-icon Certificate Earned Joint Co-Branded Participation Certificate & Partner Completion Certificate
  • ssc-assesment-icon Nasscom assessment Available
  • course-covered-icon Course Covered under GoI Incentive
    Yes
    • cost-white-icon Course Price
      INR 34,900
      + 18% GST
    • duration-white-icon Course Duration
      32 Hours
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Why should you take The Machine Learning Pipeline on AWS course?

  • Hands-On Experience: Machine Learning Pipeline on AWS Course online provides practical, hands-on experience in building and deploying machine learning models on AWS, which is a highly sought-after skill in the tech industry.
  • End-to-End Knowledge: You'll gain a holistic understanding of the entire machine learning pipeline, from data preparation to model deployment, giving you the ability to handle real-world machine learning projects.
  • Machine Learning Pipeline on AWS Course Certification can prepare you for an official certification exam, boosting your credibility in the job market.

Who should take this The Machine Learning Pipeline on AWS course?

  • Data Scientists and Analysts
  • IT Professionals (responsible for deploying and managing the ML Infra)
  • Managers and Decision-Makers: seeking an understanding of ML Pipeline on AWS Course can benefit their organizations.
  • Anyone Interested in Machine Learning: If you have a keen interest in machine learning and cloud computing, this course provides a solid introduction to both.

Curriculum

Mod 0: Introduction

  • Exercise: Pre-assessment

Mod 1: Intro to ML and the ML Pipeline

Mod 2: Introduction to Amazon SageMaker

  • Lab 1: Introduction to Amazon SageMaker

Mod 3: Problem formulation

  • Exercise: Formulate your project’s business problem

Mod 4: Data preprocessing

  • Lab 2: Data preprocessing

Mod 5: Model training

Mod 6: Model evaluation

  • Lab 3: Model Training and Evaluation
  • Exercise: Project presentations

Mod 7: Feature Engineering and Model Tuning

Mod 8: Model deployment

Tools you will learn in The Machine Learning Pipeline on AWS course-

  • Amazon SageMaker: This is a fully managed service that provides the ability to build, train, and deploy machine learning models quickly.
  • Amazon SageMaker Notebook
  • Amazon SageMaker GroundTruth
  • Amazon SageMaker Training Job
  • Amazon SageMaker Hyperparameter Tuning
  • Amazon SageMaker Inference
  • S3
  • Jupyter Notebook

FAQs

  • Operating system - 64-bit windows, mac-os or Linux
  • Storage - 1GB available space
  • Graphics - Integrated or dedicated GPU running at 1920*1080 or less.
  • Browser - Firefox, Edge or chrome (mobile browser are not supported)
  • Network - Broadband Internet connection
  • Input - keyboard and mouse are required to play.

  • Basic knowledge of Python programming language.
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment.
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