Microsoft Certified Training Program

AWS AI, ML & GenAI Job-Oriented Program with 4 Certifications

Transform your career with AWS AI, ML & GenAI! Gain the skills to land a high-paying job and earn 4 AWS AIML certifications. 

12 Weeks

Intensive Training

Live Sessions

Expert Instructors

100% Job

Placement Support

4 Certificates

Earn 4 AWS Certifications

4.5 ( 141 Ratings )
Learners
40000 +

About Our Course

  • This comprehensive program equips you with the essential skills in AI, ML, and GenAI.
  • You will earn 4 AWS Certifications: AWS Cloud Practitioner, AWS AI Practitioner, AWS Machine Learning Engineer Associate, and AWS Machine Learning Specialty.
  • Learn through hands-on labs, industry projects, and 100+ lessons.
  • Boost your career by mastering cutting-edge AI/ML tools and technologies with AWS.

Who Should Enroll in This Program?

IT Professionals

Looking to upgrade their skills and transition into roles like AI Engineers, Data Scientists, and Machine Learning Specialists.

Software Developers

Who wants to build, deploy, and scale applications using AWS AI services and ML

Aspiring Cloud Engineers

Who wants to start a career in AWS with globally recognized certifications

Professionals from non-cloud backgrounds

Looking to upskill and enter the AWS & AIML field.

Fresh graduates

Fresh graduates eager to build a solid career in AWS & AIML

Experienced professionals

who want to upgrade their skills in AWS AI, ML, and GenAI.

Tools Covered

Key Outcomes of This Program

Earn 4 AWS Certifications

Gain valuable credentials like AWS Cloud Practitioner, AWS AI Practitioner, AWS Machine Learning Engineer Associate, and AWS Machine Learning Specialty to boost your career prospects.

Hands-On Learning

Get practical experience with real-world projects using AWS tools like SageMaker, Rekognition, Polly, and Bedrock—skills that are highly valued by employers and recruiters when showcased on your resume.

Real-World Project Portfolio

Build a portfolio of projects, such as Predicting University Admissions and AI Stylist, that you can showcase in interviews.

Job-Ready Skills

Learn the essential skills needed for roles like AI Engineer, ML Engineer, and Data Scientist, with a focus on cloud technologies and AI/ML frameworks.

Job Placement Support

Receive guidance on resume building, interview preparation, and job search strategies to help you secure high-paying roles.

6-Month Money-Back Guarantee

No risk involved! If you're not satisfied or don't get the desired results, we offer a full money-back guarantee within 6 months, see T&C.

Lifetime Access

Get lifetime access to course materials, updates, and ongoing support, ensuring you're always up-to-date with the latest in AI/ML.

Expert Support

Learn from experienced instructors

Increased Earning Potential

With the skills and certifications gained from this program, you’ll be positioned to land higher-paying jobs in the growing AI/ML field.

60+ Labs

Practice Labs

100+ Videos

Video Course

12 Projects

Projects Course

500+ QA's

Q&A Exams

Course Break-Down

Lessons:
  • Overview of AI, ML, DL, and GenAI
  • AI vs ML vs DL vs GenAI
  • Machine Learning vs Traditional Programming
  • Types of Machine Learning
  • Common AI/ML Use Cases
  • SageMaker Built-in Algorithms
Lessons:
  • Introduction to Generative AI
  • GenAI Use Cases
  • Foundation Models Overview
  • Prompt Engineering and Prompting Techniques
  • Transformers, Tokenization, and Embedding
  • Risks and Governance in GenAI
  • Amazon Bedrock and Guardrails
Labs:
  • Enable Foundation Models in Bedrock
  • Generate Images with Titan Image Generator G1
  • Summarize Text using Claude and Titan
  • Advanced Prompt Techniques
  • Build a RAG Knowledge Management System
  • Set up Guardrails with Bedrock
  • Watermark Detection in Bedrock
  • Adv. Q&A App with Amazon Bedrock & RAG
  • Fine-tuning FMs and inferencing in Bedrock
Lessons:
  • Introduction to Amazon Bedrock
  • Accessing Bedrock via Console
  • Transformers Overview
  • Tokenization and NLP
  • Working with SageMaker and Boto3
Labs:
  • Lab: Invoke Zero-Shot Prompt for Text Generation
  • Lab: Automating Python Code Generation
  • Lab: Build a Bedrock Agent with Action Groups
  • Lab: Text & Vector Embedding with Amazon Titan
  • Lab: Exploring Transformers Tokenization
  • Lab: Perform Text Generation
  • Lab: Text Generation using prompt includes Context
  • Lab: Text summarize: Titan Text Premier
  • Lab: Abstractive Text Summarization
  • Lab: Use Amazon Bedrock for Question Answering
  • Lab: Chat with Llama 3 and Titan Premier LLMs
  • Lab: Invoke the Bedrock model for code generation
  • Lab: Bedrock model integration @ Langchain
Lessons:
  • Overview of AWS AI Services:
    • Comprehend, Translate, Transcribe
    • Polly, Lex, Rekognition, Textract
    • Kendra, Personalize
    • Amazon Q
  • Introduction to SageMaker
Labs:
  • Explore AWS Managed AI Services
  • Enhancing Clinical Documentation with AI
  • Amazon Q Business and Amazon Q Apps
  • Analyze Insight In Text With AWS Comprehend
  • Image Labeling in SageMaker Ground Truth
  • Requesting AWS Service Quota Increases
  • Create & Manage SageMaker Studio
  • Setting Up SageMaker Domain, Studio & Canvas
  • Build a Sample Chatbot Using Amazon Lex
  • Build Lex Chatbot Using 3rd Party API
Lessons:
  • Object Storage Concepts
  • Amazon S3 Basics
  • S3 Bucket Policies, ACLs, and Versioning
  • Cross-Region Replication
Labs:
  • Create S3 Bucket Upload and Access Files
  • Introduction to AWS Glue
  • Amazon Athena
  • AWS Glue – Using the Data Catalog with Athena
Lessons:
  • Data Transformation Techniques
  • Using AWS Glue
  • Feature Engineering with SageMaker Data Wrangler
  • Principal Component Analysis
Labs:
  • Building Data Pipelines with No-Code ETL
  • Prepare Data for TF-IDF @Sagemaker JupyterLab
  • Data Preprocessing With DataBrew
  • Running ETL Job Using Glue
Lessons:
  • Introduction to Data Analysis
  • Time Series Analysis
  • Overview of Amazon QuickSight
  • Apache Spark on EMR
  • PCA for Dimensionality Reduction
Labs:
  • Streamlining Data Analysis with No-Code Tools: SageMaker Canvas and Data Wrangler
Lessons:
  • Training, Testing, and Validation
  • Hyperparameter Tuning Strategies
  • Distributed Training Concepts
  • Data Parallelism in Machine Learning
Labs:
  • Hyperparameter Optimization in SageMaker
  • Train and Predict with TensorFlow
  • Use Autopilot for Data Preparation and Training
Lessons:
  • Real-Time Inference vs Batch Processing
  • Setting Up SageMaker Endpoints
  • Monitoring with Model Monitor
Labs:
  • Build, Train, and Deploy a Model
  • Low-Code Deployment with SageMaker Studio
Lessons:
  • ML Pipelines with SageMaker
  • CI/CD for ML with CodePipeline
  • Workflow Orchestration with Step Functions
Labs:
  • Create Docker Image and Push to ECR
  • Create Amazon EKS Cluster
  • Deploy Infrastructure Using CloudFormation
  • Event-Driven ML Workflows with EventBridge
Lessons:
  • Responsible and Augmented AI
  • IAM, Macie, and PrivateLink
  • Compliance with ISO, SOC, and other standards
Labs:
  • Set Up AWS X-Ray
  • Enable and Store Logs with CloudTrail
  • Configure AWS Config

Project works

Predict University Admission with SageMaker Autopilot

This project aims to predict the likelihood of university admission using machine learning. Designed for beginners, it introduces automated ML capabilities with AWS SageMaker Autopilot, guiding you through data preprocessing, automated model training, and deployment. You will learn how SageMaker simplifies the ML workflow, enabling you to build effective predictive models with minimal coding.

Build a RAG Assistant with AWS Bedrock & SageMaker

This project guides you in building a Retrieval-Augmented Generation (RAG) assistant leveraging AWS Bedrock and Foundation Models. You will learn how to combine external knowledge retrieval with generative AI to create intelligent assistants that provide accurate and context-aware responses.

Real-Time Stock Data Processing

This project implements a real-time data processing pipeline for stock market data using AWS services. You will learn to ingest streaming data, process it, and apply ML models for timely stock trend analysis and prediction, highlighting practical real-time analytics solutions on AWS

Building an End-to-End ML Pipeline using SageMaker

This project demonstrates how to design and deploy a full machine learning pipeline using SageMaker. From data ingestion and preprocessing to model training, hyperparameter tuning, deployment, and monitoring, you will gain hands-on experience automating ML workflows for scalable production environments.

Predicting Customer Churn Using ML Model

Use AWS SageMaker to build a classification model that helps businesses identify customers likely to churn. You’ll gain hands-on skills in feature engineering, model training, evaluation, and deploying solutions with real business impact.

Build a Crypto AI Agent using Amazon Bedrock

This project explores building an AI agent using Amazon Bedrock’s Foundation Models to analyze cryptocurrency market data. It covers data ingestion, model customization, and deploying intelligent agents capable of providing market insights and predictions, demonstrating the power of GenAI in financial technology.

Image Semantic Segmentation using AWS SageMaker

This project covers training and deploying deep learning models for semantic segmentation tasks using AWS SageMaker. You will learn how to label images, build models that classify each pixel in an image, and deploy solutions for computer vision applications like medical imaging or autonomous driving.

MLOps on AWS: ML Pipeline Automation

Focusing on MLOps best practices, this project teaches how to automate machine learning workflows using SageMaker Pipelines and AWS Step Functions. You will build CI/CD pipelines that enable continuous integration, testing, and deployment of ML models for efficient lifecycle management.

Heart Disease Prediction Using SageMaker Canvas

Leverage SageMaker Canvas, AWS's no-code platform, to predict the likelihood of heart disease. Perfect for non-coders, this project teaches you how to upload data, train models, and make predictions—all without writing a line of code.

AWS Data Strategy: Using Data & AI/ML Services

This project gives you a complete view of how to design and implement an end-to-end data strategy on AWS by integrating key services like S3, Glue, Redshift, SageMaker, and Bedrock. You’ll learn how to efficiently ingest, store, process, and analyze data while incorporating AI/ML services to unlock real-time insights and automation. Perfect for learners aiming to build scalable, intelligent data pipelines, this project bridges the gap between data engineering and AI-powered solutions using real business use cases.

Skills You Need to Get Started

Basic IT knowledge

You don’t need to be an expert, but having a basic understanding of computing concepts will help.

Interest in cloud technologies

Enthusiasm to explore AWS services.

Desire to upskill

Willingness to learn new tools and technologies for career growth.

Problem-solving attitude

Ability to think critically and creatively in the cloud environment.

No programming knowledge

Python or any scripting language is beneficial but not required.

Why You Should Enroll

4 Industry Certifications

Earn globally recognized AWS certifications (CLF-C01, AIF-C01, MLS-C01) that prove your skills and open doors to high-paying roles.

Hands-On Experience

Practice on 100+ labs and real-world projects using AWS AI/ ML services & Tools

Career Support

Get personalized help with resumes, mock interviews, and job strategies designed to fast-track your career.

Higher Earning Potential

Position yourself for promotions, career transitions, and top salaries with in-demand AI/ML in AWS expertise.

Flexible Learning

Learn at your own pace with full access to training materials, labs, and recordings

Why Choose Us for the Azure Job-Oriented Program with 6 Certifications?

Hands-On Learning

Focus on practical training with 100+ labs and projects—no boring theory.

Query Support Anytime

Clear your doubts quickly via WhatsApp, Community, Ticketing System, or during live Q&A sessions.

Weekly Live Interactive Sessions

Learn directly from experts, ask questions, and stay on track with your learning.

Proven Roadmap

Our structured path, designed by Atul (with 20+ years of IT experience), takes you from beginner to certified professional.

Ongoing Guidance

Even after landing a job, continue receiving mentorship and support for real-world project success.

Course Validity

Enjoy 1-year unlimited access to training materials, labs, and recordings. Learn at your own pace and revisit topics whenever needed.

Testimonials/Feedback

Join 45,000+ learners worldwide who have upskilled, transformed their careers, and landed high-paying roles. Learn from their success stories—and start creating your own.

What Our Trainees Say

Trusted by thousands of satisfied trainees across multiple platforms

Insights from Our Achievers..

FAQs – Frequently Asked Questions

How long is the program?

The program is designed to be completed in 6-8 months, depending on your pace.

You will earn 4 AWS Certifications:

  1. AWS Cloud Practitioner
  2. AWS AI Practitioner
  3. AWS Machine Learning Engineer Associate
  4. AWS Machine Learning Specialty

These certifications will help you showcase your expertise and boost your career prospects in the AI/ML field.

Absolutely! This program is designed for beginners. No prior AI, ML, or cloud experience is required. We start with the basics and provide all the support you need to succeed, making it perfect for those new to the field.

After completing the program, you can apply for various high-demand roles, including:

  • AI Engineer
  • Machine Learning Engineer
  • Data Scientist
  • Cloud AI Architect
  • AWS Cloud Engineer
  • ML Ops Engineer
  • AI/ML Researcher
  • Non-Coding Roles

This program equips you with the skills to pursue a wide range of positions in the fast-growing AI/ML industry.

Yes, we offer a 6-month money-back guarantee. If you’re not satisfied, we’ll refund your payment, see T&C.

We offer both live sessions and recorded content. You can learn at your own pace with our self learning portal & recordings.

Yes! The program is tailored for beginners, and we walk you through the entire learning journey.

You’ll receive expert support, including:

  • Personalized job guidance
  • Interview preparation
  • Resume reviews
  • 1-year support after course completion

You’ll have access to recorded sessions for 1 year, so you can catch up anytime.

Yes, we offer flexible payment plans to suit your budget.

Yes, we provide exam preparation support to help you pass your AWS AIML certification exams.

While we can’t guarantee a job, we provide job placement support, including interview prep, CV assistance, and job application guidance to help you succeed in the job market. 

6 Months Money Back Guarantee

When you join the K21Academy, you are fully protected by our 100% Money back guarantee.

We strive to provide the best training programs, but if you don’t get the desired results even after following every step of our learning style, you can claim your money back! 100% money-back guarantee covers the price of online training.

You have 6 Months from the date of the original purchase, to claim a refund. All you will be required to do is, show us the proof that you took action and attended sessions, completing the hands-on labs, Projects & applying to at least 50 jobs & get CV Reviewed (share proof) & you feel that the program is not worth the money you invested, you will receive a full refund.

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My 24+ Years of Experience with over 45,000+ trainees

I started my IT career in 2000 as an Oracle DBA/Apps DBA. The first few years were tough (<$100/month), with very little growth.

In 2004, I moved to the UK. After working really hard, I landed a job that paid me £2700 per month.

In February 2005, I saw a job that was £450 per day, which was nearly 4 times of my then salary.

So I looked at training from Oracle for 5 days. In November, I successfully transitioned to Oracle Security & IAM, and my career took off.

Around 2012–13, Cloud, DevOps & Cloud Automation were gaining popularity & there were many job opportunities in these fields.

So, I decided to make a change in my career path, and I transitioned from working on On-premises (Security, Infrastructure & Databases) to focusing on Cloud & DevOps.
Learning Cloud & DevOps gave me the opportunity to work with some of the world’s largest and most prestigious clients.

I then used the same roadmap with 45,000+ individuals (like you) to help them get their dream jobs.

If they can do it, you can do it too!