AWS Certified Machine Learning Engineer - Associate By Nick Garner - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials) +--- Thread: AWS Certified Machine Learning Engineer - Associate By Nick Garner (/Thread-AWS-Certified-Machine-Learning-Engineer-Associate-By-Nick-Garner)

AWS Certified Machine Learning Engineer - Associate By Nick Garner - OneDDL - 04-13-2025 Free Download AWS Certified Machine Learning Engineer - Associate By Nick Garner Released 4/2025 By Nick Garner MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 9h 36m | Size: 2.24 GB Table of contents Introduction AWS Certified Machine Learning Engineer - Associate: Introduction Module 1: Data Preparation for Machine Learning Module Introduction Lesson 1: Data Ingestion and Storage Basics Learning objectives 1.1 Course Overview 1.2 Overview of Common Data Formats 1.3 Ingesting Data with Amazon S3 and SageMaker Data Wrangler 1.4 Data Ingestion Demonstration 1.5 Data Storage Optimization and Transfer in AWS Lesson 2: Data Transformation and Feature Engineering with SageMaker Learning objectives 2.1 Data Cleaning and Preprocessing with SageMaker Data Wrangler 2.2 Data Preprocessing Demonstration 2.3 Feature Scaling and Encoding Techniques 2.4 Handling Missing Values and Outliers Lesson 3: Preparing Data for Modeling Learning objectives 3.1 Validating Data Quality with AWS Tools 3.2 Configuring Data for SageMaker Training Jobs 3.3 Managing SageMaker Feature Store 3.4 SageMaker Feature Store Demonstration Module 2: ML Model Development Module Introduction Lesson 4: Choosing and Training Models in SageMaker Learning objectives 4.1 Overview of SageMaker's Built-in Algorithms and JumpStart Models 4.2 SageMaker Algorithms Demonstration 4.3 Setting Up and Running SageMaker Training Jobs 4.4 SageMaker Training Demonstration 4.5 Hyperparameter Tuning with SageMaker Automatic Model Tuning 4.6 Hyperparameter Tuning Demonstration 4.7 Preventing Overfitting and Underfitting 4.8 Model Over/Underfitting Demonstration Lesson 5: Model Evaluation and Bias Detection Learning objectives 5.1 Model Evaluation Metrics: Accuracy, Precision, and Recall 5.2 Using SageMaker Clarify for Bias Detection and Interpretability 5.3 Comparing Model Performance Using A/B Testing 5.4 Model A/B Testing Demonstration 5.5 Managing Model Versions with SageMaker Model Registry 5.6 Model Registry Demonstration Module 3: Deployment and Orchestration of ML Workflows Module Introduction Lesson 6: Deploying Models with SageMaker Learning objectives 6.1 Real-Time Inference with SageMaker Endpoints 6.2 Real-Time Inference Demonstration 6.3 Batch Inference and Asynchronous Inference 6.4 Batch and Asynchronous Inference Demonstration 6.5 Using SageMaker Neo for Edge Deployment 6.6 SageMaker Edge Deployment Demonstration Lesson 7: Automating ML Workflows with SageMaker Pipelines Learning objectives 7.1 Building and Automating ML Pipelines in SageMaker 7.2 SageMaker Pipeline Demonstration 7.3 Integrating Data Processing and Training Steps 7.4 Training and Data Processing in SageMaker Pipelines Demonstration 7.5 Triggering Pipelines with EventBridge for Retraining 7.6 Triggering SageMaker Pipelines via EventBridge Demonstration Module 4: ML Solution Monitoring, Maintenance, and Security Module Introduction Lesson 8: Monitoring and Optimizing ML Solutions Learning objectives 8.1 Using SageMaker Model Monitor for Data Drift and Quality 8.2 SageMaker Model Monitor Demonstration 8.3 Setting Up Alerts and CloudWatch Dashboards 8.4 Cost Optimization with Auto-scaling and SageMaker Savings Plans 8.5 SageMaker Auto-scaling Demonstration Lesson 9: Securing ML Models and Data in SageMaker Learning objectives 9.1 IAM Roles and Permissions for SageMaker 9.2 IAM Demonstration 9.3 VPC Configurations for Secure Endpoint Deployment 9.4 VPC Demonstration Module 5: Exam Preparation and Strategy Module Introduction Lesson 10: Exam Strategies and Practice Learning objectives 10.1 Key Focus Areas for the AWS ML Engineer Associate Exam 10.2 Exam Question Types and Tips for Multiple-Choice Questions 10.3 Time Management and Final Exam Day Tips Summary AWS Certified Machine Learning Engineer - Associate: Summary [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable