AWS
AWS Machine Learning Engineering
The AWS Machine Learning Engineering course teaches you to build, train, deploy, and manage ML models using AWS services like SageMaker, Forecast, and Personalize. It equips you with practical skills to create scalable AI solutions for real-world applications
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is AWS Machine Learning Engineering?
The AWS Machine Learning Engineering course gives you the skills you need to build cloud-based ML solutions from start to finish.
It talks about data engineering, training models, and deploying them with AWS SageMaker and other services.
You'll learn a lot about deep learning, forecasting, and recommendation systems.
This course gets you ready to use machine learning on a large scale to make a difference in the real world.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
AWS Machine Learning Engineering Course Curriculum
.
Introduction to Machine Learning on AWS
Section 1
Understand ML concepts, workflows, and AWS ecosystem.
Supervised & unsupervised learning
ML lifecycle & pipeline
AWS ML stack overview
ML on the cloud vs on-prem
.
Data Collection & Preprocessing
Section 2
Learn to prepare and clean data for ML models.
Data ingestion with AWS Glue & Kinesis
Data wrangling in SageMaker Data Wrangler
Handling missing values & outliers
Feature engineering basics
.
Exploratory Data Analysis (EDA)
Section 3
Gain insights before model training.
Descriptive statistics
Data visualization with QuickSight
Correlation analysis
Feature selection
.
Model Development with Amazon SageMaker
Section 4
Build and train models at scale.
Built-in SageMaker algorithms
Training jobs & hyperparameters
SageMaker Autopilot
Custom model training with frameworks
.
Model Evaluation & Optimization
Section 5
Ensure model accuracy and reliability.
Metrics (precision, recall, F1, AUC)
Cross-validation
Hyperparameter tuning with SageMaker
Bias detection with SageMaker Clarify
.
Model Deployment & Inference
Section 6
Deploy ML models securely and at scale.
SageMaker endpoints
Batch inference vs real-time inference
Multi-model endpoints
CI/CD for ML (MLOps)
.
Machine Learning Pipelines & Automation
Section 7
Automate ML workflows using AWS tools.
SageMaker Pipelines
Step Functions for ML orchestration
Feature Store
Model registry
.
Advanced ML & Deep Learning
Section 8
Go beyond basics into advanced techniques.
Neural networks & deep learning
Computer vision with Rekognition
NLP with Comprehend & Bedrock
Generative AI integration
.
Security, Monitoring & Governance
Section 9
Ensure responsible ML usage.
IAM for ML workloads
Monitoring with SageMaker Model Monitor
Responsible AI practices
Cost management strategies
.
Hands-On Projects & Exam Preparation
Section 10
Apply skills to real-world projects and prepare for certification.
End-to-end ML pipeline project
Predictive analytics use case
Hands-on labs with SageMaker
Exam readiness & practice tests
Key Projects
AWS Machine Learning Engineering
Real-Time Fraud Detection System
Use Amazon SageMaker and Kinesis to process live transaction data and build a fraud detection pipeline. Set up a classification model on a SageMaker endpoint and use CloudWatch to watch the predictions as they happen.
Demand Forecasting Model
Use Amazon Forecast to create a time-series forecasting solution that takes in sales data from S3. Train and deploy the model to predict future demand, and use QuickSight dashboards to see how the results affect your business.
Personalized Recommendation Engine
Use data about how customers interact with your site to make a recommendation system with Amazon Personalize. Use an API to deploy the model so that it can give real-time product recommendations, and then add it to a sample e-commerce app.
AWS Machine Learning Engineering – Associate Training Program
Category
Associate
Exam Name:
AWS Machine Learning Engineering – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
What is AWS Machine Learning Engineering?
AWS Machine Learning Engineering involves building, deploying, and maintaining ML models on AWS using services like SageMaker. It covers the full ML lifecycle from data preparation to model monitoring. Techpratham explains it empowers engineers to implement scalable, production-ready AI solutions.
Who should pursue AWS ML Engineering?
Ideal for data scientists, ML engineers, AI developers, and software engineers who want to design and deploy ML solutions on AWS. Techpratham emphasizes that prior knowledge of Python, statistics, and basic ML concepts is beneficial.
What skills are needed for AWS ML Engineering?
Skills include data preprocessing, feature engineering, model training and evaluation, hyperparameter tuning, deployment, and monitoring. Familiarity with SageMaker, Lambda, and AWS AI services is crucial. Techpratham notes hands-on experience with real datasets is key.
What AWS services are essential for ML Engineering?
Core services include Amazon SageMaker for modeling and deployment, S3 for data storage, Glue for ETL, Lambda for serverless operations, and SageMaker Studio for workflow management. Techpratham highlights that understanding service integration is vital for scalable solutions.
Are there prerequisites for AWS ML Engineering?
No formal prerequisites, but knowledge of Python, ML algorithms, statistics, and AWS fundamentals is recommended. Techpratham suggests hands-on practice with SageMaker notebooks and datasets before diving into advanced projects.
How do I prepare for AWS ML Engineering?
Preparation includes AWS training courses, SageMaker labs, whitepapers, and practice on real datasets. Techpratham recommends building end-to-end ML pipelines including deployment and monitoring for complete understanding.
What types of ML models can I deploy on AWS?
You can deploy regression, classification, clustering, deep learning, NLP, and computer vision models. Techpratham points out that SageMaker supports custom and prebuilt models for flexible deployment.
How do I monitor ML models in production?
Monitoring includes tracking model performance metrics, drift detection, logging predictions, and automating retraining. Techpratham emphasizes that continuous monitoring ensures models remain accurate and reliable.
What job roles can I get with AWS ML Engineering skills?
Roles include ML Engineer, Data Scientist, AI Developer, Cloud AI Specialist, and AI Solution Architect. Techpratham notes these skills are in high demand across cloud-first and AI-driven organizations.
Is AWS ML Engineering difficult to learn?
It can be challenging due to both ML concepts and cloud deployment considerations. Techpratham stresses that hands-on practice with datasets, SageMaker, and workflow automation is the best way to master it.
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