AWS Associate Machine Learning Engineer Training Course in Crewe

AWS Associate Machine Learning Engineer Training Course Outline

Domain 1: Data Preparation for Machine Learning

• Ingest and Store Data

• Data Formats and Ingestion Mechanisms
• How to Use the Core AWS Data Sources
• How to Use AWS Streaming Data Sources to Ingest Data
• AWS Storage Options, Including Use Cases and Tradeoffs

• Transform Data and Perform Feature Engineering

• Data Cleaning and Transformation Techniques
• Feature Engineering Techniques
• Encoding Techniques
• Tools to Explore, Visualise, or Transform Data and Features
• Services That Transform Streaming Data
• Data Annotation and Labelling Services

• Ensure Data Integrity and Prepare Data for Modelling

• Pre-Training Bias Metrics for Numeric, Text, and Image Data
• Strategies to Address CI in Numeric, Text, and Image Datasets
• Techniques to Encrypt Data
• Data Classification, Anonymisation, and Masking
• Implications of Compliance Requirements

Domain 2: ML Model Development

• Choose a Modelling Approach

• Capabilities and Appropriate Uses of ML Algorithms to Solve Business Problems
• How to Use AWS AI Services to Solve Specific Business Problems
• How to Consider Interpretability During Model Selection or Algorithm Selection
• SageMaker Built-In Algorithms and When to Apply Them

• Train and Refine Models

• Elements in the Training Process
• Methods to Reduce Model Training Time
• Factors That Influence Model Size
• Methods to Improve Model Performance
• Benefits of Regularisation Techniques
• Hyperparameter Tuning Techniques
• Model Hyperparameters and Their Effects on Model Performance
• Methods to Integrate Models Built Outside SageMaker into SageMaker

• Analyse Model Performance

• Model Evaluation Techniques and Metrics
• Methods to Create Performance Baselines
• Methods to Identify Model Overfitting and Underfitting
• Metrics Available in SageMaker Clarify to Gain Insights into ML Training Data and Models
• Convergence Issues

Domain 3: Deployment and Orchestration of ML Workflows

• Select Deployment Infrastructure Based on Existing Architecture and Requirements

• Deployment Best Practices
• AWS Deployment Services
• Methods to Serve ML Models in Real Time and in Batches
• How to Provision Compute Resources in Production and Test Environments
• Model and Endpoint Requirements for Deployment Endpoints
• How to Choose Appropriate Containers
• Methods to Optimise Models on Edge Devices

• Create and Script Infrastructure Based on Existing Architecture and Requirements

• Difference Between On-Demand and Provisioned Resources
• How to Compare Scaling Policies
• Tradeoffs and Use Cases of IaC Options
• Containerisation Concepts and AWS Container Services
• How to Use SageMaker Endpoint Auto Scaling Policies to Meet Scalability Requirements

• Use Automated Orchestration Tools to Set Up CI/CD Pipelines

• Capabilities and Quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy
• Automation and Integration of Data Ingestion with Orchestration Services
• Version Control Systems and Basic Usage
• CI/CD Principles and How They Fit into ML Workflows
• Deployment Strategies and Rollback Actions
• How Code Repositories and Pipelines Work Together

Domain 4: ML Solution Monitoring, Maintenance, and Security

• Monitor Model Inference

• Drift in ML Models
• Techniques to Monitor Data Quality and Model Performance
• Design Principles for ML Lenses Relevant to Monitoring

• Monitor and Optimise Infrastructure and Costs

• Key Performance Metrics for ML Infrastructure
• Monitoring and Observability Tools to Troubleshoot Latency and Performance Issues
• How to Use AWS CloudTrail to Log, Monitor, and Invoke Re-Training Activities
• Differences Between Instance Types and How They Affect Performance
• Capabilities of Cost Analysis Tools
• Cost Tracking and Allocation Techniques

• Secure AWS Resources

• IAM Roles, Policies, and Groups That Control Access to AWS Services
• SageMaker Security and Compliance Features
• Controls for Network Access to ML Resources
• Security Best Practices for CI/CD Pipelines

Who should attend this AWS Associate Machine Learning Engineer Training Course?

The AWS Associate Machine Learning Engineer Training Course is perfectly suited for individuals eager to deepen their expertise in machine learning and cloud computing within the AWS ecosystem. It is particularly beneficial for:

• Data Scientists
• Machine Learning Engineers
• Cloud Architects
• Software Engineers
• IT Managers and Directors
• System Administrators
• Big Data Analysts
• AI Research Scientists

Prerequisites of AWS Associate Machine Learning Engineer Training Course

There are no formal prerequisites to attend this AWS Associate Machine Learning Engineer Training Course.

AWS Associate Machine Learning Engineer Training Course Overview

AWS, or Amazon Web Services, is a comprehensive and widely adopted cloud platform that offers over 200 fully featured services from data centres globally. Its importance lies in its vast array of tools and capabilities that allow businesses to scale and grow by providing powerful computing power, database storage, and other functionality.

For organisations, this training enhances their team's ability to effectively use AWS for innovative and efficient cloud solutions, reducing operational costs and improving system scalability. For individuals, the training offers the skills to confidently utilise AWS technologies, making them invaluable assets to their teams and enhancing job performance. Lastly, for delegates, this certification opens up numerous career opportunities, as AWS skills are highly sought after in the tech industry, potentially leading to higher job security and advancement prospects.

In this 3 Day AWS Associate Machine Learning Engineer Training provided by The Knowledge Academy, delegates will gain comprehensive insights into the end-to-end processes of machine learning on the AWS platform. From data preparation, model building, and training to deployment and securing machine learning solutions, the course covers essential skills for effectively harnessing the power of AWS for machine learning projects. Delegates will explore various AWS services and tools that facilitate data handling, feature engineering, model evaluation, and the deployment of machine learning models in production environments.

Course Objectives

• To understand AWS data ingestion and storage solutions
• To apply feature engineering on diverse datasets
• To develop and train machine learning models
• To evaluate models using AWS SageMaker
• To deploy models for batch and real-time processing
• To manage and monitor machine learning solutions
• To implement security best practices in AWS

After attending this training, delegates will be able to apply their knowledge to set up complete machine learning workflows on AWS. They will have the skills to choose the appropriate AWS tools for different stages of machine learning projects, from data collection and processing to training and deploying models.

What’s included in this AWS Associate Machine Learning Engineer Training Course? 

• World-Class Training Sessions from Experienced Instructors   
• AWS Associate Machine Learning Engineer Training Certificate 
• Digital Delegate Pack