Overview
Build and orchestrate sophisticated GenAI applications using Amazon Bedrock Agents, Knowledge Bases, and Prompt Flows., Leverage Amazon SageMaker JumpStart to deploy, fine-tune, and manage foundation models for your specific use cases., Master the end-to-end ML lifecycle, including bias detection and model monitoring using Amazon SageMaker Clarify., Integrate pre-trained AI services like Amazon Rekognition, Textract, and Transcribe directly into your applications.
Software Developers who want to build and integrate Generative AI applications on AWS., AWS Certification Candidates preparing for the AWS Certified Generative AI - Developer exam., Solutions Architects designing GenAI solutions using Amazon Bedrock and SageMaker., Machine Learning Engineers looking to leverage managed services like SageMaker JumpStart., Technical Leads who need to understand the capabilities of the AWS Generative AI stack.
An active AWS account is required to perform the hands-on lab exercises., A solid understanding of core AWS services (IAM, S3, EC2, Lambda) is assumed., Basic familiarity with machine learning concepts (e.g., training vs. inference) is helpful but not strictly required., No prior experience with Amazon Bedrock or SageMaker is required; we will start from the basics.
Generative AI is not just a buzzword—it is the biggest shift in software development in decades.
As organizations rush to adopt AI, the demand for developers who can build, secure, and deploy Generative AI applications on AWS has skyrocketed. The AWS Certified Generative AI - Developer certification is the industry's gold standard for validating these cutting-edge skills.
But passing the exam—and actually building these applications—requires more than just reading documentation. You need practical, hands-on experience.
This course is your complete guide to mastering the AWS Generative AI stack.
Designed specifically for developers, this course bridges the gap between theory and real-world implementation. We strip away the complexity and focus on the practical "how-to" of building GenAI solutions.
You will master the following key domains through hands-on labs:
Amazon Bedrock Deep Dive: Go far beyond simple text generation. You will learn to build complex, agentic workflows using Bedrock Agents, implement Retrieval-Augmented Generation (RAG) using Knowledge Bases, and engineer widely effective prompts using Prompt Management and Prompt Flows.
Amazon SageMaker for Developers: You don't need to be a data scientist to use SageMaker. We focus on the developer-centric features, showing you how to use SageMaker JumpStart to deploy and fine-tune foundation models, SageMaker Clarify to detect bias, and SageMaker Data Wrangler to prepare your data efficiently.
MLOps and Governance: Learn how to operationalize your models using SageMaker Model Monitor and Model Registry to ensure your applications remain reliable in production.
AI Services: Integrate powerful, pre-trained AI capabilities into your apps using Amazon Rekognition (computer vision), Amazon Textract (document extraction), and Amazon Transcribe (speech-to-text).
Who is this course for?
Software Developers looking to transition into AI/ML development.
Candidates preparing for the AWS Certified Generative AI - Developer exam.
Solutions Architects designing GenAI applications.
Anyone who wants to move beyond "Hello World" and build enterprise-grade AI solutions.
Don't get left behind. Enroll today and start building the future of software on AWS!
Deepak Dubey
Highly Skillful, Knowledgeable and Result-Driven IT Professional with over 20 years of Experience (Totally Hands-on).
AWS Cloud Architecture, Design, Development, DevOps, SysOps - EC2, ECS, EKS, Elastic Beanstalk, Lambda, S3, Glacier, Storage Gateway, RDS, DynamoDB, ElastiCache, Redshift, VPC, ELB, Route 53, CloudWatch, CloudFormation, CloudTrail, OpsWorks, Elastic MapReduce, Kinesis, Data Pipeline, SWF, SQS, SNS, SES
Big Data Engineering - Hadoop, HDFS, YARN, Hive, Spark, Kafka, MongoDB, HBase, Cassandra, Storm, ZooKeeper, Redis, Elastisearch, Logstash, Kibana, Grafana
Data Science, Machine Learning, AI, Python, TensorFlow, Natural Language Processing
Java/Java EE/Web/Web Services, Spring, Spring MVC, Microservices, RESTful API, Spring Boot, Hibernate Development
Full Stack Development experience using MERN (MongoDB, Express, ReactJS, Node) Stack. Proficient with Angular, HTML5, CSS3, Bootstrap, Ant Design
DevOps - Kubernetes, Docker, Jenkins, Maven, Gradle, GIT, JIRA, Terraform, Puppet, Chef, Ansible, Prometheus
Google Cloud Platform - Cloud SQL, Datastore, Bigtable, Cloud Spanner, Cloud Dataflow, Dataproc, BigQuery, AI Platform, Datalab, Dataprep, Data Studio, Cloud Composer
I hold 50+ Internationally recognized Professional Certifications listed as below:-
Amazon Web Services (9X Certified)
AWS Certified Solutions Architect - Professional
AWS Certified DevOps Engineer - Professional
AWS Certified Machine Learning - Specialty
AWS Certified Security - Specialty
AWS Certified Advanced Networking - Specialty
AWS Certified Data Analytics - Specialty
AWS Certified Database - Specialty
Google Cloud Certified Professional Machine Learning Engineer
TensorFlow Developer Certificate
Google Cloud Certified Professional Data Engineer
Microsoft Azure
Microsoft Certified: Azure Solutions Architect Expert
Microsoft Certified: Azure AI Engineer Associate
Big Data
Elasticsearch, Logstash, Kibana Certified Engineer
Cloudera Certified Associate (CCA) Spark and Hadoop Developer
Confluent Certified Developer For Apache Kafka
Confluent Certified Operator for Apache Kafka
DevOps
Certified Kubernetes Application Developer
Certified Kubernetes Administrator
Docker Certified Associate
Certified Jenkins Engineer 2018
Python
Certified Python Associate
Java
OCP Java SE 6 & 8 Programmer
SOA and Web Services
Oracle SOA Suite 12c Certified Implementation Specialist
Oracle Certified Expert, Java EE 6 Web Services Developer
Security
CISSP & CISM
Enterprise Architecture and Management
TOGAF 9 Certified, PRINCE2 and ITIL