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The Machine Learning Pipeline on AWS Training


What The Machine Learning Pipeline on AWS training is all about?

The Machine Learning Pipeline on AWS Training demonstrates the methodologies to utilize machine learning pipeline to rectify real business issues in a project-based learning environment. This technical course helps professionals gain comprehensive knowledge on each phase of the pipeline from the demonstrations and preparations and utilizing that information to finish a project. It trains individuals to build, evaluate, train, and launch an ML model with the help of Amazon SageMaker that rectifies their major business issue.

This course teaches you to apply machine learning to a real-time issue and use the ML pipeline to overcome a specific business problem. You will demonstrate some of the leading practices for creating scalable, cost-effective, and protection ML pipelines in AWS. It enables you to choose and justify the suitable ML approach for a stated business problem. This course is ideal for developers, solution engineers, or anyone who wants to gain more information related to ML pipeline using Amazon SageMaker.

Schedule

Contact us to customize this class with your preferred dates, times and location. You can call us on 1-800-961-0337 or Chat with our representative.

What are the course objectives for The Machine Learning Pipeline on AWS training?
  • Justifying and selecting appropriate ML approaches for particular business difficulties.
  • Utilizing ML pipeline to solve the specified business problems.
  • Utilizing the ML pipeline to resolve an exact business troubles.
  • Deploying, Training, evaluating, and tuning a ML model in Amazon SageMaker.
  • Describing few most excellent training so as to secure ML pipelines, cost-optimized, designing scalable in AWS.
  • Applying ML to real-life business difficulties after the competition of the course.
Who should attend The Machine Learning Pipeline on AWS training?

The Machine Learning Pipeline On AWS Training is highly advantageous for Data Engineers, Solutions Architects and Developers have little experience with Machine learning fundamentals and ML pipelines. Therefore, professionals who are utilizing Amazon SageMaker and have brief knowledge about SageMaker can enroll in this training.

What are the prerequisites for The Machine Learning Pipeline on AWS training?
Recommended
AWS Cloud Practitioner Essentials

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

What is the course outline for The Machine Learning Pipeline on AWS training?
  • 1. Introduction to Machine Learning and the ML Pipeline
  • a). Overview of machine learning, including use cases, types of machine learning, and key concepts
  • b). Overview of the ML pipeline
  • c). Introduction to course projects and approach
  • 2. Introduction to Amazon SageMaker
  • a). Introduction to Amazon SageMaker
  • b). Demo: Amazon SageMaker and Jupyter notebooks
  • c). Lab: Introduction to Amazon SageMaker
  • 3. Problem Formulation
  • a). Overview of problem formulation and deciding if ML is the right solution
  • b). Converting a business problem into an ML problem
  • c). Demo: Amazon SageMaker Ground Truth
  • d). Hands-on: Amazon SageMaker Ground Truth
  • e). Problem Formulation Exercise and Review
  • f). Project work for Problem Formulation
  • 4. Preprocessing
  • a). Overview of data collection and integration, and techniques for data preprocessing and visualization
  • b). Lab: Data Preprocessing (including project work)
  • 5. Model Training
  • a). Choosing the right algorithm
  • b). Formatting and splitting your data for training
  • c). Loss functions and gradient descent for improving your model
  • d). Demo: Create a training job in Amazon SageMaker
  • 6. Model Training
  • a). How to evaluate classification models
  • b). How to evaluate regression models
  • c). Practice model training and evaluation
  • d). Train and evaluate project models
  • e). Lab: Model Training and Evaluation (including project work)
  • f). Project Share-Out 1
  • 7. Feature Engineering and Model Tuning
  • a). Feature extraction, selection, creation, and transformation
  • b). Hyperparameter tuning
  • c). Demo: SageMaker hyperparameter optimization
  • d). Lab: Feature Engineering (including project work)
  • 8. Module Deployment
  • a). How to deploy, inference, and monitor your model on Amazon SageMaker
  • b). Deploying ML at the edge
  • 9. Course Wrap-Up
  • a). Project Share-Out 2
  • b). Post-Assessment
  • c). Wrap-up