AWS Machine Learning Engineer Course in Hyderabad
Build, deploy and monitor machine learning models on Amazon SageMaker
The AWS Machine Learning Engineer course trains you to take a machine learning idea through the full production lifecycle on AWS, following the four domains of the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam guide: data preparation, model development, deployment and orchestration, and monitoring, maintenance and security. You begin with Python, data handling and core machine learning concepts, then move to Amazon S3, AWS Glue and SageMaker Data Wrangler for data, SageMaker training, tuning and built-in algorithms for modelling, and SageMaker endpoints, Pipelines and Model Registry for deployment. You also cover CI/CD for ML with CodePipeline, monitoring with SageMaker Model Monitor and CloudWatch, security with IAM, VPC and KMS, and cost control. Labs and projects use real datasets. The course suits graduates and working professionals with basic Python and statistics who want ML engineering roles; beginners are guided through a bridging module. Classes run in classroom batches at the GloryTecks centre in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Python & ML Foundations Refresher
- •Python, NumPy and pandas essentials for data work
- •Supervised and unsupervised learning, overfitting and bias-variance
- •Evaluation metrics for classification and regression
- •Lab: Train and evaluate a scikit-learn model locally
- •Outcome: Understand the ML workflow that AWS services automate
AWS Essentials for ML
- •Core services for ML: IAM roles, S3, EC2, VPC and CloudWatch
- •AWS CLI and the boto3 SDK
- •Cost basics for ML workloads: instance types, Spot and clean-up
- •Lab: Move data to S3 and read it from a notebook with boto3
- •Outcome: A secure, low-cost workspace for ML experiments
Data Ingestion & Storage
- •Batch and streaming ingestion with Amazon Kinesis and AWS Glue
- •Data formats for ML: CSV, Parquet, RecordIO and TFRecord
- •Querying data with Amazon Athena; data governance with AWS Lake Formation
- •Lab: Build a Glue crawler and ETL job that prepares a training dataset
- •Outcome: Build repeatable data pipelines for model training
Data Preparation & Feature Engineering
- •Data cleaning, imputation, outlier handling and class imbalance
- •Encoding, scaling and feature selection
- •SageMaker Data Wrangler, SageMaker Feature Store and Ground Truth for labelling
- •Data quality checks and bias detection with SageMaker Clarify
- •Lab: Create reusable features and store them in SageMaker Feature Store
- •Outcome: Produce consistent, high-quality features for training and inference
Model Training on Amazon SageMaker
- •SageMaker Studio, notebooks and training jobs
- •Built-in algorithms such as XGBoost and Linear Learner; bring your own scikit-learn, PyTorch or TensorFlow script
- •Distributed training concepts and managed Spot training
- •Experiment tracking with SageMaker Experiments
- •Lab: Train an XGBoost model and compare runs
- •Outcome: Run reproducible training jobs and track experiments
Hyperparameter Tuning & Model Evaluation
- •SageMaker Automatic Model Tuning and search strategies
- •Cross-validation, confusion matrix, ROC-AUC and RMSE
- •Explainability with SageMaker Clarify and SHAP concepts
- •Handling overfitting, underfitting and convergence problems
- •Lab: Tune an XGBoost model and interpret feature importance
- •Outcome: Select and justify the best model for a business metric
Deep Learning & Foundation Model Basics on AWS
- •Neural network concepts and training a PyTorch model on SageMaker
- •SageMaker JumpStart and pre-trained models; transfer learning
- •Amazon Bedrock and fine-tuning overview for generative AI use cases
- •AWS AI services: Rekognition, Comprehend, Textract and Transcribe at a high level
- •Lab: Deploy a pre-trained model from JumpStart and call it from code
- •Outcome: Decide when to build, fine-tune or call a managed AI service
Model Deployment & Inference
- •Real-time, serverless, asynchronous and batch transform inference
- •Multi-model endpoints, auto scaling and SageMaker Neo
- •Containers for inference and Amazon ECR; Lambda and API Gateway in front of endpoints
- •Safe rollout: blue/green, canary and shadow tests
- •Lab: Deploy an endpoint, load test it and set auto scaling
- •Outcome: Choose an inference option that meets latency and cost goals
ML Pipelines & CI/CD
- •SageMaker Pipelines steps, parameters and conditions
- •SageMaker Model Registry and approval workflows
- •CI/CD for ML with AWS CodeCommit alternatives, CodeBuild, CodePipeline and Git
- •Orchestration with AWS Step Functions and Amazon MWAA overview
- •Lab: Build a train-evaluate-register-deploy pipeline
- •Outcome: Automate the model lifecycle end to end
Monitoring & Maintenance of ML Solutions
- •Data drift, concept drift and model quality monitoring
- •SageMaker Model Monitor, CloudWatch metrics, logs and alarms, AWS X-Ray
- •Triggering retraining with EventBridge and pipelines
- •Cost and performance monitoring of ML infrastructure
- •Lab: Configure Model Monitor and an alarm for data drift
- •Outcome: Keep deployed models accurate and reliable
Security, Governance & Responsible AI
- •IAM roles for SageMaker, least privilege and service control policies
- •Network isolation with VPC endpoints and private subnets; encryption with KMS
- •Governance with SageMaker Model Cards and audit logging with CloudTrail
- •Privacy, fairness and responsible AI practices
- •Lab: Run training in a VPC with encrypted data and a restricted role
- •Outcome: Apply secure and compliant ML practices on AWS
Capstone & Exam Readiness
- •Capstone: ingest, prepare, train, deploy and monitor a model
- •Review of the four MLA-C01 exam domains with scenario questions
- •Timed practice tests and answer review
- •Capstone presentation and portfolio write-up on GitHub
- •Outcome: A portfolio project and a revision plan for the vendor exam
Tools Covered
Projects Included
- Customer Churn Prediction Pipeline: Prepare data with Glue, train XGBoost on SageMaker and deploy a real-time endpoint; deliverable is a working endpoint with evaluation report.
- Feature Store for Retail Demand: Build reusable features with Data Wrangler and Feature Store for training and inference; deliverable is a documented feature group and notebook.
- Automated Retraining Pipeline: Build a SageMaker Pipeline that trains, evaluates, registers and deploys with an approval step; deliverable is a pipeline definition and run history.
- Drift Monitoring Dashboard: Configure Model Monitor and CloudWatch alarms for a deployed model; deliverable is a monitoring dashboard and runbook.
- End-to-End Capstone on Image or Text Classification: Fine-tune or reuse a pre-trained model, deploy with auto scaling and secure it in a VPC; deliverable is a GitHub repo and architecture write-up.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the AWS Machine Learning Engineer course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on AWS Machine Learning Engineer.
Certificate of Completion
AWS Machine Learning Engineer
This certifies that
[Your Name]
has successfully completed the
AWS Machine Learning Engineer
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured AWS Machine Learning Engineer course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best AWS Machine Learning Engineer Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Machine Learning Engineer (entry level), MLOps Engineer, Data Scientist (AWS), AI Engineer; support is guidance with the job search, not a guarantee of a job.