Deploy in AWS Sagemaker

What you'll be able to do

• What is AWS Sagemaker
• How Sagemaker works internally
• How to build ML models with Sagemaker
• How to do preprocessing in separate container
• Deploy via Batch processing and Model Endpoints
• Perform and track ML Experiments

1. 01SEC 1: Introduction to Sagemaker5 lessons · 18 min

   1. Introduction to SagemakerPreview11:06
   2. Download Resources1:00
   3. Sagemaker FeaturesPreview2:04
   4. Intro to the simple end-to-end ML problemPreview2:43
   5. Free Tier OptionsPreview1:12
2. 02SEC 2: Sagemaker Studio4 lessons · 24 min

   1. Sagemaker Studio Setup (Live)Preview5:00
   2. Exploring the StudioPreview9:54
   3. How sagemaker works internally7:56
   4. Course Review1:00
3. 03SEC 3: Sagemaker Initial Setup2 lessons · 12 min

   1. Setting up Boto and sagemaker packages6:18
   2. Execution Role and role of S35:26
4. 04SEC 4: Getting Data into Sagemaker and S32 lessons · 13 min

   1. Begin data preprocessing9:37
   2. How to upload data from S3 from Sagemaker?3:18
5. 05SEC 5: Preprocessing Jobs7 lessons · 34 min

   1. How to handle compute intensive tasks in dedicated container8:30
   2. Overview of Docker5:36
   3. Preprocessing job - part 16:19
   4. Preprocessing job - part 24:47
   5. Why transfer the Python code file to S31:34
   6. Understanding the arguments for running the processing job5:39
   7. Inspecting Processing Jobs1:39
6. 06SEC 6: Sagemaker Experiments5 lessons · 19 min

   1. Experiments and Trials3:16
   2. Steps to conduct an experiment in sagemaker2:57
   3. Creating tracker1:37
   4. Create Trial Image3:02
   5. Running training trial for XGBoost Estimator7:53
7. 07SEC 7: Model Deployment6 lessons · 33 min

   1. Batch processing vs model serving3:46
   2. Real Time Inference5:54
   3. Model Endpoint Part 1 - Creating Model.mp48:26
   4. Model Endpoint Pt 2 - Create Endpoint Config3:10
   5. Model Endpoint Pt 3 - Create Endpoint and monitor9:22
   6. Remember to delete endpoints2:17
8. 08SEC 8: Hyper Parameter Tuning in Sagemaker1 lesson · 15 min

   1. Hyper Parameter Tuning with Sagemaker15:06

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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