What is Amazon Sagemaker | Deploy ML Models on AWS Sagemaker | AWS Sagemaker Tutorial | Intellipaat

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In this comprehensive video, we'll be breaking down Amazon SageMaker. SageMaker is a machine learning service provided by AWS which enables data scientists and developers to efficiently build, train, and deploy machine learning models at a large scale. We'll be covering all the essential components of SageMaker along with a hands-on demonstration, where we'll be comparing local environment jupyter notebook code and SageMaker's jupyter notebook code.

GitHub Code Link:

📖 Below are the concepts covered in 'What is Amazon Sagemaker':
00:00 - Why do we need SageMaker?
00:57 - What is Amazon SageMaker?
01:14 - Real-time inference
01:18 - Batch processing
01:32 - Components of Amazon SageMaker
01:48 - User Interface - AWS Console, SageMaker Notebooks, SageMaker Studio,SageMaker Canva
01:55 - AWS Console
02:12 - SageMaker Notebooks
02:19 - SageMaker Studio
02:25 - SageMaker Canvas
02:36 - Developer Tools in SageMaker
02:40 - API (Application Programming Interface)
02:49 - SDK (Software Development Kit)
03:06 - What is Boto3 in SageMaker
04:34 - Creating a notebook instance in SageMaker
05:33 - GitHub code walkthrough
06:32 - Creating a bucket in S3
07:35 - Local python environment code walkthrough

✅ What is Amazon Sagemaker?
Amazon SageMaker is a machine learning service provided by AWS which enables data scientists and developers to efficiently build, train, and deploy machine learning models at a large scale.

✅ What is Amazon Sagemaker Used for?
SageMaker is used largely by Data Scientist to build, train and deploy various machine learning models. SageMaker offers a wide range of tools that streamlines the entire machine learning workflow, allowing Data Scientists to focus more on developing and refining models instead of managing the overall infrastructure.

✅ How Does Sagemaker Simplify the Deployment of ML Models?
SageMaker provides a fully managed environment for complicated tasks like scaling, managing the infrastructure, and monitoring various ML models. SageMaker also offers build-in tools that can be used for model training, tuning, and evaluation, and can be integrated efficiently wit other AWS services for data management and security.

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✅ Key Features - (Course Features)
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👉🏼30 Hrs Self-paced Videos
👉🏼70 Hrs Project & Exercises
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👉🏼Job Assistance
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What’s Covered in This Program? -
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