The Machine Learning Pipeline on AWS Training

Leverage the Machine Learning Pipeline to solve business problems

  • Understand the three types of business problems and how to solve them using ML
  • Build, train, and deploy ML models using Amazon SageMaker
  • Clear the exam and become AWS Certified Machine Learning Specialist 
  • 350,000 + Professionals trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Reduce Risks with Machine Learning Pipelines

The Machine Learning Pipeline on AWS course helps you understand how to use the Machine Learning Pipeline to solve real business problems in a project-based environment. Understand the three major business problems – Fraud Detection, Recommendation Engines or Flight Delays and learn about the various phases of the pipeline to minimize problems and risks.

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Highlights

  • 4 Days of Live, Instructor-Led Training

  • Sessions by Amazon Certified Trainers 

  • Case-study based discussion and labs 

  • Understand best practices for Cloud Adoption 

  • Prep for AWS Certified Machine Learning Exam 

  • Latest, up-to-date curriculum developed by AWS experts.

Accredited by

The KnowledgeHut Edge

Learn from Industry Experts

Interact with certified instructors who are also industry experts. Listen, Learn, Explore, and apply!  

Advanced Curriculum

Acquire the latest skills and understand all concepts easily with the latest and the most updated curriculum.  

Hands-On Training

Learn with the help of theory-backed practical case studies, hands-on exercises, and guided coding practice. 

Exam Support

Get a comprehensive support to help you clear the AWS Certified Machine Learning – Specialty exam. 

Advance from the Basics

Build a strong foundation learning concepts from scratch through step-by-step guidance.

Job-Ready Skills

Maximize your readiness for a rewarding career as you acquire skills that make you in-demand.

Prerequisites

Prerequisites for Machine Learning Pipeline on AWS

Prerequisites for the ML Pipeline on AWS 

  • Basic knowledge of Python programming language 
  • Basic understanding of AWS Cloud infrastructure  
  • Basic experience working in a Jupyter notebook environment 

For more details, please refer to the FAQs 

Who Should Attend the ML Pipeline on AWS Course

Developers

Solutions architects

Data Engineers

Anyone looking to learn about the ML pipeline using Amazon SageMaker

The Machine Learning Pipeline on AWS Schedules

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What You Will Learn

Selecting the Appropriate ML Approach

Learn to select and justify the appropriate ML approach for a given business problem 

Solving Business Problems

Gain insights into real world applications of ML Pipeline solutions for specific business problems

Implementing AWS SageMaker

Train, evaluate, deploy, and tune an ML model using Amazon SageMaker 

Understanding the Types of Business Problems

Learn to identify fraud detections, recommendation engines, or flight delays

Designing ML Pipelines

Describe some of the best practices for designing scalable, cost-optimized ML pipelines  

Applying ML Best Practices

Understand and apply the best practices for scalable and secure Machine Learning Pipelines in AWS

Transform Your Workforce

Solve Business Problems using ML Pipelines

Enhance your productivity and reduce product turnaround time by selecting the appropriate ML approach for your business. Develop your team and enable its members to build the AWS Cloud skills they will need to innovate and drive transformation.

  • Leverage Immersive Learning
  • Develop and deploy cloud-based systems
  • Improve security and reliability in systems
  • Utilize AWS features, tools, and best practises 

500+ Clients

Machine Learning Pipeline on AWS Curriculum

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline
  • Introduction to course projects and approach 
  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks 
  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem
  • Demo: Amazon SageMaker Ground Truth
  • Hands-on: Amazon SageMaker Ground Truth
  • Practice problem formulation
  • Formulate problems for projects 
  • Overview of data collection and integration, and techniques for data pre-processing and visualization
  • Practice pre-processing
  • Pre-process project data
  • Class discussion about projects 
  • Choosing the right algorithm
  • Formatting and splitting your data for training
  • Loss functions and gradient descent for improving your model
  • Demo: Create a training job in Amazon SageMaker 
  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Initial project presentations 
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Practice feature engineering and model tuning
  • Apply feature engineering and model tuning to projects
  • Final project presentations 
  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge
  • Demo: Creating an Amazon SageMaker endpoint
  • Post-assessment
  • Course wrap-up 

Frequently Asked Questions

Certification

The Machine Learning Pipeline on AWS Course is an intermediate-level course that explores how to use the machine learning (ML) pipeline to solve a significant business problem in a project-based learning environment.  

The Machine Learning Pipeline on AWS course helps you understand how to use Machine Learning Pipeline to solve genuine business problems in a project-based environment. With this course, you will understand the three major business problems – Fraud Detection, Recommendation Engines or Flight Delays and learn about the various phases of the pipeline to minimize problems and risks.

By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.  

After completing the Machine Learning Pipeline on AWS certification training, you will be able to:

  • Select and justify the appropriate ML approach for a given business problem
  • Use the ML pipeline to solve a specific business problem
  • Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
  • Apply machine learning to a real-life business problem after the course is complete 

To be eligible for this course, you should have:

  • 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 

Workshop Experience

Currently all our courses are offered online as Live, interactive, trainer-led sessions where you will get to learn directly from the trainer with opportunities to discuss and clear doubts. 

Our instructors are approved by AWS to lead these sessions. They also have hands-on experience and will be able to tell you the practical aspects of what you are learning.  

Our courses are delivered through live interactive virtual classrooms and can be structured according to the requirements of the course.

Our training focuses on interactive learning. Most class time is dedicated to hands-on exercises, lively discussions, and team collaboration, all facilitated by the trainer who is an experienced AWS platform practitioner. The focus is on finding practical solutions to real-world scenarios in various projects environments, both big and small. 

In an online classroom, students can log in at the scheduled time to a live learning environment that is led by an instructor. You can interact, communicate, view, and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improve your online training experience. 

Internet Connectivity (2Mbps Link) and Laptop/PC (Windows/Mac) with 4GB RAM. 

No, you do not need to record the sessions, the sessions will be auto recorded on our LMS, you will be able to refer to them. 

Yes, you can switch your start date with prior notice of at least 24 hours and subject to availability in the desired batch.