Machine Learning Engineering on AWS is an intermediate-level training program designed for professionals who want to build, deploy, scale, and operationalize machine learning solutions on Amazon Web Services. As organizations increasingly adopt cloud-based artificial intelligence solutions, AWS provides a comprehensive ecosystem for managing the entire machine learning lifecycle—from data preparation and model training to deployment and monitoring.

Throughout this hands-on course, participants gain practical experience using Amazon SageMaker AI, Amazon EMR, SageMaker Data Wrangler, SageMaker Pipelines, Model Registry, MLOps, CI/CD, and advanced monitoring services to build production-ready machine learning applications.

Combining theoretical knowledge, guided labs, and real-world projects, this training prepares learners to develop secure, scalable, and enterprise-grade machine learning solutions on AWS.

For corporate groups, we can organize this training as an on-site private group.

Prerequisites

Participants are recommended to have:

Familiarity with basic Machine Learning concepts
Experience with Python programming
Knowledge of NumPy, Pandas, and Scikit-Learn
Basic understanding of cloud computing concepts
Familiarity with AWS services
Experience with Git or other version control systems (beneficial)

Who Should Attend

This course is ideal for:

Machine Learning Engineers
Data Scientists
AI Engineers
DevOps Engineers
Cloud Engineers
SysOps Engineers
Software Developers
Data Engineers
Professionals building ML solutions on AWS

What You Will Learn

By the end of this course, learners will be able to:

Build machine learning solutions on AWS.
Train and deploy models using Amazon SageMaker AI.
Process large-scale datasets with Amazon EMR.
Perform data preparation and feature engineering.
Select appropriate modeling approaches.
Evaluate and optimize model performance.
Apply Hyperparameter Tuning techniques.
Implement production-grade deployment strategies.
Establish MLOps workflows.
Monitor model performance and data quality.
Design secure AWS machine learning architectures.

Training Outline

Introduction to Machine Learning on AWS

Machine Learning Fundamentals

Machine Learning Fundamentals
Supervised and unsupervised learning
Machine learning lifecycle
Translating business problems into ML solutions

Machine Learning on AWS

AWS machine learning ecosystem
Amazon SageMaker AI
AWS ML services overview
Cloud-native AI solutions

Responsible AI

Ethical AI principles
Fairness and bias mitigation
Explainable AI
Responsible machine learning practices

Analyzing Machine Learning Challenges

Business Problem Evaluation

ML use cases
Defining business objectives
Success criteria and KPIs

Training Approaches

Model development strategies
Training methodologies
Algorithm selection techniques

Data Processing and Preparation

Data Types and Management

Structured and unstructured data
Data collection processes
Data storage strategies

AWS Storage Solutions

Amazon S3
AWS storage services
Choosing the right storage architecture

Exploratory Data Analysis

Data exploration techniques
Data visualization
Data quality assessment

Data Transformation and Feature Engineering

Data Cleaning

Handling missing data
Correcting inaccurate records
Removing duplicate entries

Feature Engineering

Feature creation techniques
Feature selection strategies
Data transformation methods

AWS Data Processing Services

Amazon SageMaker Data Wrangler
Amazon EMR
SageMaker Processing

Choosing a Modeling Approach

SageMaker Built-In Algorithms

SageMaker Built-In Algorithms
Algorithm selection
Business use cases

Amazon SageMaker Autopilot

AutoML
Automated model development
Intelligent model recommendations

Model Selection Considerations

Performance evaluation
Business alignment
Cost optimization

Training Machine Learning Models

Model Training Concepts

Training methodologies
Training infrastructure
Distributed training approaches

Training with Amazon SageMaker

SageMaker Training Jobs
Resource allocation
Training optimization techniques

Model Evaluation and Optimization

Performance Evaluation

Evaluation metrics
Accuracy and error analysis
Model comparison methodologies

Hyperparameter Optimization

Hyperparameter Tuning
Reducing training time
Automated optimization strategies

Model Deployment Strategies

Deployment Approaches

Real-time inference
Batch inference
Edge deployment

Inference Infrastructure

Endpoint management
Containerized inference
Resource optimization

A/B Testing

Traffic shifting strategies
Model comparison
Canary deployment approaches

Securing AWS Machine Learning Resources

Identity and Access Management

IAM policies
Role-based access controls
Secure ML environments

Network Security

Network access controls
VPC integration
Data protection strategies

CI/CD Security

Secure deployment pipelines
Pipeline protection
Security validation controls

MLOps and Automation

MLOps Fundamentals

Machine Learning Operations (MLOps)
Model lifecycle management
Continuous integration and delivery

Amazon SageMaker Pipelines

Amazon SageMaker Pipelines
Workflow automation
Process standardization

Model Registry

Amazon SageMaker Model Registry
Model versioning
Model governance

Monitoring Model Performance and Data Quality

Model Monitoring

SageMaker Model Monitor
Performance tracking
Production observability

Data Drift and Model Drift

Detecting data drift
Monitoring model degradation
Automated remediation strategies

Automated Troubleshooting and Continuous Improvement

Anomaly detection
Automated corrective actions
Performance optimization
Operational excellence

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Machine Learning Engineering on AWS in Sweden through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from Sweden or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Machine Learning Engineering on AWS through face-to-face Classroom Based training in Sweden. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Machine Learning Engineering on AWS in Sweden as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Arrange the training in Sweden or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.

Machine Learning Engineering on AWS Training in Sweden