AWS SageMaker Training in Gurgaon, Delhi

AWS SageMaker

Build, train, and deploy machine learning models for any use case with fully managed infrastructure, tools, and workflows

Duration : 6 months Classes : 36 Days : Weekdays / Weekends

Overview
Curriculum
Pre-requisite
Review

Productionize ML at Hyperscale AWS SageMaker is the unified, fully managed service that revolutionizes the Machine Learning lifecycle, enabling data scientists and developers to build, train, and deploy models faster and at a fraction of the cost. It eliminates the complexity of managing infrastructure, allowing teams to focus purely on model innovation. Our comprehensive SageMaker training is designed to provide hands-on mastery of the entire MLOps workflow-from notebook preparation and scalable training to continuous deployment and monitoring. If your goal is to transition your ML experiments into reliable, production-ready services on the world's leading cloud platform, this course is essential.

Hands-On Mastery of the MLOps Workflow This intensive program provides deep, practical experience across the full SageMaker studio ecosystem. You will learn to use SageMaker Notebook Instances for development, configure high-performance, distributed training jobs using managed training clusters, and optimize models using automatic model tuning. Crucially, the training covers the MLOps pipeline, teaching you how to package models, use SageMaker Endpoints for low-latency inference, and leverage SageMaker Pipelines for automated, reproducible continuous integration and continuous delivery (CI/CD) of ML models.

Career Advancement in Applied AI and MLOps Engineering Proficiency in AWS SageMaker is the direct path to high-value roles such as Machine Learning Engineer, MLOps Engineer, and Cloud AI Specialist. Companies are actively seeking professionals who can bridge the gap between model prototyping and production deployment, and SageMaker is the definitive tool for this task. By mastering this platform, you will possess the specialized expertise to design and implement end-to-end ML production systems, ensuring scalability, performance, and monitoring-skills that are critical for advancing innovation in every major industry.

Target Audience:-
- Machine Learning Engineers:
- Data Scientists
- DevOps Engineers
- Cloud Architects

Learning Outcomes:-
- Confidently utilize the SageMaker environment, including notebooks, experiments, and resource management
- Launch, monitor, and configure scalable, distributed training jobs using built-in algorithms or custom scripts
- Utilize automatic model tuning (Hyperparameter Optimization - HPO) to find the best performing model versions efficiently
- Deploy models to high-availability, low-latency endpoints for real-time inference and understand different hosting options
- Design and implement automated ML workflows using SageMaker Pipelines for reproducible training and deployment
- Set up model monitoring to detect data drift and model quality issues in production environments

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas

To get the maximum benefit from the training, we expect that you are familiar with :-

- Basic SQL
- Basic Statistics
- Basics of Machine Learning
- Basics of Cloud Computing
- Python Programming

....

NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.

NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.

This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.

Module 1: Introduction to Machine Learning & SageMaker
✔ Overview of machine learning workflows
✔ Benefits of using SageMaker for ML development
✔ SageMaker architecture and components
✔ Setting up AWS environment and permissions for SageMaker

Module 2: Data Preparation & Exploration
✔ Creating and managing S3 buckets for data storage
✔ Using SageMaker Studio and notebooks
✔ Data wrangling with Pandas and AWS Data Wrangler
✔ Visualizing datasets and identifying data quality issues

Module 3: Model Building & Training
✔ Built-in algorithms vs custom models
✔ Using SageMaker Estimators and training jobs
✔ Hyperparameter tuning with SageMaker Automatic Model Tuning
✔ Training with frameworks: TensorFlow, PyTorch, XGBoost

Module 4: Model Evaluation & Debugging
✔ Evaluating model performance: accuracy, precision, recall, F1
✔ Confusion matrix and ROC curves
✔ Debugging training jobs with SageMaker Debugger
✔ Logging and metrics with CloudWatch

Module 5: Model Deployment & Inference
✔ Deploying models to SageMaker endpoints
✔ Real-time vs batch inference
✔ Using SageMaker Pipelines for CI/CD
✔ Scaling and monitoring deployed models

Module 6: Security, Monitoring & Cost Optimization
✔ IAM roles and policies for SageMaker
✔ Encryption and data protection
✔ Monitoring with CloudWatch and SageMaker Model Monitor
✔ Cost management and pricing strategies

Module 7: Capstone Project & Certification Prep
✔ Building an end-to-end ML pipeline in SageMaker
✔ Integrating with other AWS services (Lambda, S3, API Gateway)
✔ Preparing for AWS Certified Machine Learning - Specialty exam
✔ Resume tips and interview preparation

NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.

At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.

Bhawana

Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.

Harish

Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.