What you'll get

  • Govt. Verified Certification
  • Job Credibility
  • Certification Valid for Life
  • Lifetime Access To E-learning
  • On-demand video
  • Flexible Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Course Content

Total: 59 lectures
  • Technical requirements
  • Exploring the capabilities
  • Setting up Amazon SageMaker on your local machine
  • Setting up Amazon SageMaker Studio
  • Deploying one-click solutions and models with Amazon SageMaker JumpStart
  • Labeling data with Amazon SageMaker Ground Truth
  • Transforming data with Amazon SageMaker Data Wrangler
  • Running batch jobs with Amazon SageMaker Processing
  • Discovering Amazon SageMaker Autopilot
  • Using Amazon SageMaker Autopilot in SageMaker Studio
  • Using the SageMaker Autopilot SDK
  • Diving deep on SageMaker Autopilot
  • Discovering the built-in algorithms in Amazon SageMaker
  • Training and deploying models with built-in algorithms
  • Using the SageMaker SDK with built-in algorithms
  • Working with more built-in algorithms
  • Discovering the CV built-in algorithms in Amazon SageMaker
  • Preparing image datasets
  • Using the built-in CV algorithms
  • Discovering the NLP built-in algorithms in Amazon SageMaker
  • Preparing natural language datasets
  • Using the built-in algorithms for NLP
  • Discovering the built-in frameworks in Amazon SageMaker
  • Running your framework code on Amazon SageMaker
  • Using the built-in frameworks
  • Understanding how SageMaker invokes your code
  • Customizing an existing framework container
  • Using the SageMaker Training Toolkit with scikit-learn
  • Building a fully custom container for scikit-learn
  • Building a fully custom container for R
  • Training and deploying with your own code on MLflow
  • Building a fully custom container for SageMaker Processing
  • Understanding when and how to scale
  • Monitoring and profiling training jobs with Amazon SageMaker Debugger
  • Streaming datasets with pipe mode
  • Distributing training jobs
  • Scaling an image classification model on ImageNet
  • Training with the SageMaker data and model parallel libraries
  • Using other storage services
  • Optimizing training costs with managed spot training
  • Optimizing hyperparameters with automatic model tuning
  • Exploring models with SageMaker Debugger
  • Managing features and building datasets with SageMaker Feature Store
  • Detecting bias in datasets and explaining predictions with SageMaker Clarify
  • Examining model artifacts and exporting models
  • Deploying models on real-time endpoints
  • Deploying models on batch transformers
  • Deploying models on inference pipelines
  • Monitoring prediction quality with Amazon SageMaker Model Monitor
  • Deploying models to container services
  • Automating with AWS CloudFormation
  • Automating with AWS CDK
  • Building end-to-end workflows with AWS Step Functions
  • Building end-to-end workflows with Amazon SageMaker Pipelines
  • Autoscaling an endpoint
  • Deploying a multi-model endpoint
  • Deploying a model with Amazon Elastic Inference
  • Compiling models with Amazon SageMaker Neo
  • Building a cost optimization checklist


We all know that Machine Learning (ML) and Artificial Intelligence (AI) are the two fastest-growing areas in technology & the most desirable skillset in the present digital job market. You will find a number of ML and AI resources online nowadays, with a wide variety of not only online courses, but also certificates.

What is Amazon Sage Maker?

Amazon Sage Maker is a fully managed service launched by Amazon based on its two decades of experience developing real-world machine learning applications, including personalization, product recommendations,  intelligent shopping, robotics, &  voice-assisted devices. In simple terms, Sage Maker is an online platform that enables developers & data scientists to quickly & easily build, tune, train, and deploy( ML) machine learning models.

As part of the AWS (Amazon Web Services) certification, you can get started with Amazon Sage Maker certification. This certification is highly recommended for fresher as well as professionals in the industry.

Is Sage Maker Certification Worth It?

Definitely yes! Learning the foundations of Machine Learning with this certification will help professionals, as well as freshers, keep pace with the on-going industry growth, expand their skills & even help advance their careers.

Skills You Will Learn

This course teaches the certification seekers about ways to get started with AWS ML  Techniques.

Key topics covered:
  • Computer Vision on AWS,
  • Machine Learning on AWS,
  • Natural Language Processing or (NLP) on AWS.
The best part is you will get a chance to practice implementing them & getting them to work for yourself and prepare better for examinations.

All said & done, seeking Amazon Sage Maker certification is a smart move for all experts associated with the IT industry.


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