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
Level
Advanced
Duration
8 weeks
Placement Client
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About 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 & Updated Content
Access to updated presentation decks shared during live training sessions.
e-Book
E-book provided by TechPratham. All rights reserved.
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
Video Recording
Daily Session would be recorded and shared to the candidate.
Projects
Live projects will be provided for hands-on practice.
Resume Building
Expert-guided resume building with industry-focused content support.
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
AWS Machine Learning Engineering
Course Curriculum
Introduction to Machine Learning on AWS
Understand ML concepts, workflows, and AWS ecosystem.
Data Collection & Preprocessing
Learn to prepare and clean data for ML models.
Exploratory Data Analysis (EDA)
Gain insights before model training.
Model Development with Amazon SageMaker
Build and train models at scale.
Model Evaluation & Optimization
Ensure model accuracy and reliability.
Model Deployment & Inference
Deploy ML models securely and at scale.
AWS Courses
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Who Should Take AWS Machine Learning Engineering
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
Knowledge Center
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