Section 1 : Introduction and Housekeeping
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction copy | 00:03:03 Duration |
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Lecture 3 | Increase the speed of learning | |
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Lecture 4 | Overview - AWS Machine Learning Specialty Exam | 00:09:05 Duration |
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Lecture 5 | Exam - Gap Analysis | |
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Lecture 6 | Preparation - AWS Machine Learning Specialty Exam | 00:04:21 Duration |
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Lecture 7 | AWS Account Setup, Free Tier Offers, Billing, Support | 00:07:00 Duration |
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Lecture 8 | Billing Alerts, Delegate Access | 00:08:10 Duration |
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Lecture 9 | Configure IAM Users, Setup Command Line Interface (CLI) | 00:11:30 Duration |
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Lecture 10 | Benefits of Cloud Computing | 00:06:12 Duration |
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Lecture 11 | AWS Global Infrastructure Overview | 00:05:58 Duration |
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Lecture 12 | Security is Job Zero AWS Public Sector Summit 2016 by Steve Schmidt |
Section 2 : SageMaker Housekeeping
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Lab - S3 Bucket Setup | 00:02:53 Duration |
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Lecture 3 | Lab - Setup SageMaker Notebook Instance | 00:02:49 Duration |
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Lecture 4 | Lab - Source Code Setup | 00:02:26 Duration |
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Lecture 5 | Kaggle Data Setup | |
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Lecture 6 | SageMaker Console looks different from the course videos - Why | |
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Lecture 7 | How to download Kaggle data with code |
Section 3 : Machine Learning Concepts
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Lecture 1 | Introduction to Machine Learning, Concepts, Terminologies | 00:10:23 Duration |
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Lecture 2 | Data Types - How to handle mixed data types | 00:12:42 Duration |
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Lecture 3 | Introduction to Python Notebook Environment | 00:10:33 Duration |
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Lecture 4 | Introduction to working with Missing Data | 00:09:35 Duration |
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Lecture 5 | Data Visualization - Linear, Log, Quadratic and More | 00:04:38 Duration |
Section 4 : Model Performance Evaluation
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Lecture 1 | Model Performance | |
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Lecture 2 | Downloadable Resources | |
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Lecture 3 | Introduction | 00:03:26 Duration |
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Lecture 4 | Regression Model Performance | 00:09:58 Duration |
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Lecture 5 | Binary Classifier Performance | 00:08:00 Duration |
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Lecture 6 | Binary Classifier - Confusion Matrix | 00:06:56 Duration |
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Lecture 7 | Binary Classifier - SKLearn Confusion Matrix | 00:03:18 Duration |
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Lecture 8 | Binary Classifier - Metrics Definition | 00:03:52 Duration |
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Lecture 9 | Binary Classifier - Metrics Calculation | 00:04:26 Duration |
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Lecture 10 | Question - Why not Model 1 | |
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Lecture 11 | Binary Classifier - Area Under Curve Metrics | 00:09:40 Duration |
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Lecture 12 | Multiclass Classifier | 00:12:36 Duration |
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Lecture 13 | Model Performance |
Section 5 : SageMaker Service Overview
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction to SageMaker | 00:04:54 Duration |
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Lecture 3 | Instance Type and Pricing | 00:10:21 Duration |
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Lecture 4 | DataFormat | 00:11:12 Duration |
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Lecture 5 | SageMaker Built-in Algorithms | 00:09:36 Duration |
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Lecture 6 | Popular Frameworks and Bring Your Own Algorithm | 00:05:24 Duration |
Section 6 : XGBoost - Gradient Boosted Trees
Section 7 : Invoke Model Endpoint From External Clients
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Lecture 1 | Install SageMaker SDK, GIT Client, Source Code, Security Permissions | |
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Lecture 2 | IAM users for the lab | |
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Lecture 3 | Integration Overview | 00:02:32 Duration |
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Lecture 4 | Client to Endpoint using SageMaker SDK | 00:09:26 Duration |
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Lecture 5 | Client to Endpoint using Boto3 SDK | 00:03:51 Duration |
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Lecture 6 | Microservice - Lambda to Endpoint - Payload | 00:03:24 Duration |
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Lecture 7 | Lambda UI Changes | |
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Lecture 8 | Microservice - Lambda to Endpoint | 00:09:10 Duration |
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Lecture 9 | Microservice - API Gateway, Lambda to Endpoint | 00:10:34 Duration |
Section 8 : SageMaker - Principal Component Analysis (PCA)
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Lecture 1 | Normalization and Standardization | |
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Lecture 2 | Downloadable Resources | |
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Lecture 3 | Introduction to Principal Component Analysis (PCA) | 00:05:49 Duration |
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Lecture 4 | PCA Demo Overview | 00:01:16 Duration |
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Lecture 5 | Demo - PCA with Random Dataset | 00:03:29 Duration |
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Lecture 6 | Demo - PCA with Correlated Dataset | 00:05:26 Duration |
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Lecture 7 | Cleanup Resources on SageMaker | |
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Lecture 8 | Demo - PCA with Kaggle Bike Sharing - Overview and Normalization | 00:03:52 Duration |
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Lecture 9 | Demo - PCA Local Mode with Kaggle Bike Train | 00:03:31 Duration |
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Lecture 10 | Use SageMaker SDK 2 | |
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Lecture 11 | Demo - PCA training with SageMaker | 00:04:23 Duration |
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Lecture 12 | Demo - PCA Projection with SageMaker | 00:02:42 Duration |
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Lecture 13 | Exercise Kaggle Bike Train and PCA | |
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Lecture 14 | Summary | 00:01:22 Duration |
Section 9 : Recommender Systems - Factorization Machines
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Lecture 1 | Recommender System | |
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Lecture 2 | Downloadable Resources | |
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Lecture 3 | Introduction to Factorization Machines | 00:05:59 Duration |
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Lecture 4 | MovieLens Dataset | |
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Lecture 5 | Use SageMaker SDK 2 | |
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Lecture 6 | Demo - Movie Recommender Data Preparation | 00:10:35 Duration |
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Lecture 7 | Demo - Movie Recommender Model Training | 00:05:35 Duration |
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Lecture 8 | Demo - Movie Predictions By User | 00:07:10 Duration |
Section 10 : Model Optimization and HyperParameter Tuning
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction to Hyperparameter Tuning | 00:06:11 Duration |
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Lecture 3 | Use SageMaker SDK 2 | |
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Lecture 4 | Lab Tuning Movie Rating Factorization Machine Recommender System | 00:18:05 Duration |
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Lecture 5 | Lab Step 2 Tuning Movie Rating Recommender System | 00:05:01 Duration |
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Lecture 6 | HyperParameter, Bias-Variance, Regularization (L1, L2) [Repeat from XGBoost] | 00:11:08 Duration |
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Lecture 7 | Nuts and Bolts of Optimization | |
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Lecture 8 | Model Optimization - related question |
Section 11 : Time Series Forecasting - DeepAR
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction to DeepAR Time Series Forecasting | 00:09:47 Duration |
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Lecture 3 | DeepAR Training and Inference Formats | 00:09:49 Duration |
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Lecture 4 | Working with Time Series Data, Handling Missing Values | 00:09:59 Duration |
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Lecture 5 | Use SageMaker SDK 2 | |
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Lecture 6 | Demo - Bike Rental as Time Series Forecasting Problem | 00:11:44 Duration |
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Lecture 7 | Demo - Bike Rental Model Training | 00:07:21 Duration |
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Lecture 8 | Demo - Bike Rental Prediction | 00:04:50 Duration |
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Lecture 9 | Demo - DeepAR Categories | 00:06:10 Duration |
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Lecture 10 | Demo - DeepAR Dynamic Features Data Preparation | 00:06:34 Duration |
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Lecture 11 | Demo - DeepAR Dynamic Features Training and Prediction | |
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Lecture 12 | Summary | 00:01:16 Duration |
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Lecture 13 | Question How to train a model for different products using DeepAR |
Section 12 : Integration Options for Model Endpoint
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Lecture 1 | Lectures moved to Invoke Model Endpoint From External Clients section |
Section 13 : Model Optimization and HyperParameter Tuning
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Lecture 1 | Lectures moved |
Section 14 : Anomaly Detection - Random Cut Forest
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction to Random Cut Forest and Intuition Behind Anomaly Detection | |
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Lecture 3 | Use SageMaker SDK 2 | |
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Lecture 4 | Lab - Taxi Passenger Traffic Analysis (AWS Provided Example) | 00:08:53 Duration |
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Lecture 5 | Lab - Auto Sales Analysis | 00:05:55 Duration |
Section 15 : Artificial Intelligence (AI) Services
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Lab Instructions | |
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Lecture 3 | Introduction | 00:03:15 Duration |
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Lecture 4 | 2.1 Amazon Transcribe and Lab | 00:05:33 Duration |
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Lecture 5 | 2.2 Amazon Transcribe and Lab | 00:06:35 Duration |
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Lecture 6 | 3. Amazon Translate | 00:04:29 Duration |
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Lecture 7 | Translate - Practical Scenario | |
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Lecture 8 | 4.1 Amazon Comprehend | 00:05:43 Duration |
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Lecture 9 | Pricing Comprehend | |
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Lecture 10 | 4.2 Amazon Comprehend | 00:05:00 Duration |
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Lecture 11 | 4.3 Amazon Comprehend training | 00:08:35 Duration |
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Lecture 12 | 5. Amazon Polly | 00:04:16 Duration |
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Lecture 13 | 6. Amazon Lex | |
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Lecture 14 | 7. Amazon Rekognition | 00:08:21 Duration |
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Lecture 15 | 8. Amazon Textract & Summary | 00:03:03 Duration |
Section 16 : S3 Data Lake Architecture - Data Consolidation
Section 17 : Deep Learning and Neural Networks
Section 18 : Bring Your Own Algorithm
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction and How built-in algorithms work | 00:05:06 Duration |
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Lecture 3 | Custom Image and Popular Framework | 00:03:55 Duration |
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Lecture 4 | Folder Structure and Environment Variables | 00:07:19 Duration |
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Lecture 5 | About Certification | |
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Lecture 6 | Lab - SKLearn Estimator Bring Your Own Part 1 | 00:09:22 Duration |
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Lecture 7 | Lab - SKLearn Estimator Bring Your Own Part 2 | 00:08:15 Duration |
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Lecture 8 | Lab - TensorFlow Estimator Bring Your Own | 00:03:51 Duration |
Section 19 : Storage for Servers
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | Introduction to Storage | 00:08:40 Duration |
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Lecture 3 | Elastic Block Store (EBS) | 00:13:09 Duration |
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Lecture 4 | Elastic File System, FSx for Windows, FSx for Lustre | 00:04:53 Duration |
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Lecture 5 | Elastic Block Store (EBS) Encryption |
Section 20 : AWS - Support Plans and Feedback
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Lecture 1 | AWS Product Improvement Feedback | |
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Lecture 2 | How to contact AWS for Production Support | 00:07:14 Duration |
Section 21 : Databases on AWS
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Lecture 1 | Downloadable Resources | |
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Lecture 2 | AWS Databases - Introduction, Benefits, and Types | |
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Lecture 3 | Relational Database Service (RDS) - Features and Benefits | 00:12:41 Duration |
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Lecture 4 | Aurora and Aurora Serverless Relational Database | 00:04:47 Duration |
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Lecture 5 | DynamoDB - Primary Key, Partitions, and Features | 00:08:03 Duration |
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Lecture 6 | Cassandra and DocumentDB | 00:02:30 Duration |
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Lecture 7 | Amazon ElastiCache - Usage Example, Features | 00:05:30 Duration |
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Lecture 8 | Amazon Redshift | 00:02:26 Duration |
Section 22 : On-Premises usage and other technologies
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Lecture 1 | On-Premises Usage and other technologies |