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.
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
Placement Client
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About AWS Machine Learning Engineering
Training Plan
01
About trainer
Working professional who is carrying more then 10 years of industry experience.
02
Decks & Updated Content
Access to updated presentation decks shared during live training sessions.
03
e-Book
E-book provided by TechPratham. All rights reserved.
04
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
05
Video Recording
Daily Session would be recorded and shared to the candidate.
06
Projects
Live projects will be provided for hands-on practice.
07
Resume Building
Expert-guided resume building with industry-focused content support.
08
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
<h1>AWS Machine Learning Engineering</h1> Course Curriculum
Introduction to Machine Learning on AWS
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
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)
Gain insights before model training.
Descriptive statistics
Data visualization with QuickSight
Correlation analysis
Feature selection
Model Development with Amazon SageMaker
Build and train models at scale.
Built-in SageMaker algorithms
Training jobs & hyperparameters
SageMaker Autopilot
Custom model training with frameworks
Model Evaluation & Optimization
Ensure model accuracy and reliability.
Metrics (precision, recall, F1, AUC)
Cross-validation
Hyperparameter tuning with SageMaker
Bias detection with SageMaker Clarify
Model Deployment & Inference
Deploy ML models securely and at scale.
SageMaker endpoints
Batch inference vs real-time inference
Multi-model endpoints
CI/CD for ML (MLOps)
AWS Courses
No related courses found
Additional Program Highlights
Comprehensive study materials and resources
HD
Professional resume building session
HD
Master your interview skills
HD
Real-world project demonstrations
HD
Upcoming Batches
Can't find a batch you were looking for?
Who Should Take <h1>AWS Machine Learning Engineering</h1>
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Key Projects
<h1>AWS Machine Learning Engineering</h1>
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.
Placement Process
Live Class with Industry Use Cases
- Interactive learning
- Real-world examples
- Expert guidance
- Doubt resolution
Daily Assignment & Weekly Assessments
- Hands-on practice
- Concept reinforcement
- Weekly evaluation
- Timely feedback
Project Testing & Deployment
- Project development
- Testing practice
- Deployment exposure
- Portfolio creation
Interview Preparation (Mock, PD, Alumni Session)
- Mock interviews
- Personal development
- Alumni guidance
- Confidence building
Resume Building
- Resume crafting
- Industry-focused format
- Skills highlighting
- Professional presentation
Placement
- Job assistance
- Interview support
- Career guidance
- Opportunity access
Latest Hiring
No hiring posts
Recently Placed Candidates
No placements available
Latest Hiring
No hiring posts
Our Success Mantra
Commitment
- Ensuring quality training every day
Fulfillment
- Meeting learning goals with confidence
Accomplishment
- Students achieving industry-ready expertise
Our Learner Voice
Beyond Courses:
Additional Support We Provide
24/7 Support
LinkedIn Profile
Resume Writing
Alumni Sessions
Interview Preparation
Live Projects
What Makes TechPratham Training Different?
Feature
TechPratham Advantage
Industry Standard Institutes
Real-World Implementation
Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.
Restricted or simulation-based access that lacks real-world complexity.
Subject Matter Experts
Mentorship from certified professionals with 15+ years of global industry experience.
Generic instructors with no hands-on implementation background.
2026 Ready Curriculum
AI-driven curriculum with advanced platform integrations and real-world configurations.
Basic, outdated syllabus that misses key technical integrations.
Daily & Weekly assignments
Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.
Self-paced learning with no accountability or progress tracking.
End-to-End Lifecycle
Hands-on project testing and deployment with portfolio-ready real-world exposure.
Basic lab exercises without any deployment or testing practice.
Interview Readiness
Structured interview preparation including mock interviews, PD sessions, and alumni guidance.
Minimal support limited to a generic certificate of completion.
Placement Support
Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.
Basic career tips with no direct industry connections.
Training Support
24/7 technical doubt resolution and personalized mentoring support.
Limited mentor availability and no support after class hours.
Affordable Fees
Competitive fixed pricing with flexible payment options and transparent fee structure.
Inflated fees with hidden costs and low return on investment.
Real-World Implementation
Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.
Restricted or simulation-based access that lacks real-world complexity.
Subject Matter Experts
Mentorship from certified professionals with 15+ years of global industry experience.
Generic instructors with no hands-on implementation background.
2026 Ready Curriculum
AI-driven curriculum with advanced platform integrations and real-world configurations.
Basic, outdated syllabus that misses key technical integrations.
Daily & Weekly assignments
Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.
Self-paced learning with no accountability or progress tracking.
End-to-End Lifecycle
Hands-on project testing and deployment with portfolio-ready real-world exposure.
Basic lab exercises without any deployment or testing practice.
Interview Readiness
Structured interview preparation including mock interviews, PD sessions, and alumni guidance.
Minimal support limited to a generic certificate of completion.
Placement Support
Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.
Basic career tips with no direct industry connections.
Training Support
24/7 technical doubt resolution and personalized mentoring support.
Limited mentor availability and no support after class hours.
Affordable Fees
Competitive fixed pricing with flexible payment options and transparent fee structure.
Inflated fees with hidden costs and low return on investment.
What is AWS Machine Learning Engineering?
Who should pursue AWS ML Engineering?
What skills are needed for AWS ML Engineering?
What AWS services are essential for ML Engineering?
Are there prerequisites for AWS ML Engineering?
How do I prepare for AWS ML Engineering?
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