Deep Learning on AWS
Train and deploy deep learning models using Amazon SageMaker and GPU instances
- Duration
- Live batches
- Format
- Live online
- Modules
- 8 modules
- Certificate
- On completion
- ✓Live online instructor-led training in scheduled batches
- ✓Neural network fundamentals with PyTorch and TensorFlow
- ✓Training on GPU instances and with SageMaker managed training jobs
- ✓Distributed training, hyperparameter tuning and experiment tracking
- ✓Deploying models to SageMaker endpoints and monitoring them
- ✓Cost control for GPU workloads
About the course
What this course covers
Deep Learning on AWS shows how to move deep learning work from a laptop to scalable cloud infrastructure. You will learn the main ideas behind neural networks, then train models using frameworks such as PyTorch and TensorFlow on GPU-enabled EC2 instances and Amazon SageMaker.
The course covers preparing data in S3, building models for image and text tasks, running SageMaker training jobs, tuning hyperparameters, tracking experiments, and deploying models to real-time and batch endpoints. We introduce AWS-designed accelerators like Trainium and Inferentia at a conceptual level, discuss distributed training and managed Spot training, and show how to monitor deployed models and control costs.
There is no exam in this course. AWS offers machine learning credentials such as the Machine Learning Engineer - Associate; because AWS exam offerings change, check the current exam guide on the AWS site before you plan. Delivery is live, instructor-led and online.
Who should attend
- ● Data scientists and ML engineers wanting to scale training on AWS
- ● Software engineers moving into machine learning
- ● Researchers and students working with deep learning models
- ● Cloud engineers supporting ML teams
- ● Technical leads evaluating AWS for ML workloads
Prerequisites
- ● Python programming proficiency
- Basic machine learning knowledge: regression, classification, train/test split
Key skills
Skills you will walk away with
Neural network fundamentals
Explain layers, activation functions, loss, backpropagation and overfitting.
Framework use
Build and train models with PyTorch and TensorFlow.
GPU infrastructure
Choose GPU instance types and use Deep Learning AMIs and containers.
SageMaker training
Run managed training jobs with your own scripts and manage data from S3.
Tuning and experiments
Use automatic model tuning and experiment tracking to compare runs.
Distributed training
Understand data and model parallelism and managed Spot training.
Curriculum
Module-by-module syllabus
8 modules · 32 topics
1Module 1: Deep Learning Refresher4 topics ▾
Learning objective: Rebuild the core concepts.
- ▸ Perceptrons and multi-layer networks
- ▸ Activation, loss and optimisers
- ▸ Regularisation and overfitting
- ▸ Evaluation metrics
2Module 2: Frameworks and Environment4 topics ▾
Learning objective: Set up a working environment.
- ▸ PyTorch and TensorFlow basics
- ▸ SageMaker Studio and notebooks
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a live batch and attend the instructor-led sessions
- 2Complete the training and deployment labs
- 3Present your capstone model and deployment
- 4Receive an Assure Success course-completion certificate after meeting attendance and capstone requirements
Career outcomes
Roles this opens up
Machine Learning Engineer
₹8–22 LPA (approx.)Builds, trains and deploys ML models in production.
Deep Learning Engineer
₹9–24 LPA (approx.)Works on neural network models for vision or language tasks.
Data Scientist (cloud)
₹7–20 LPA (approx.)Experiments and models at scale on cloud platforms.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
From notebook to production
Learn the steps after the model works on your laptop.
Hands-on with GPUs
Practise on real accelerated instances with guidance on cost.
Framework flexibility
Bring your PyTorch or TensorFlow code to SageMaker.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Do I need to know deep learning already?
You need basic machine learning and Python. We refresh neural network concepts in the first module, but this is not a beginner introduction to AI.
Q.Will GPU instances cost a lot?
GPU instances are not free-tier. We use short training runs and small datasets, and we explain how to stop resources. Learners should budget a small amount for labs.
Q.Is there an exam?
No exam in the course. The completion certificate is issued by Assure Success.
Q.Do you cover generative AI?
We cover transformer concepts and fine-tuning in the NLP module, but the course is centred on training and deploying deep learning models rather than on a specific generative AI service.
Q.Which framework should I know?
Either PyTorch or TensorFlow. Examples are mainly PyTorch.
Q.Is it useful if I use Azure or GCP?
The concepts transfer, but tools and labs are AWS-specific.
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₹26,999
Deep Learning on AWS