Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set.

With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management.

In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights.

Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

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Building and Deploying Generative AI with Amazon Bedrock

Building and Deploying Generative AI with Amazon Bedrock

Course Objective

This hands-on Amazon Bedrock workshop is designed to equip participants with in-depth knowledge and practical skills to harness the power of Amazon Bedrock for generative AI applications. Over the course of 2-3 days, participants will gain a foundational understanding of Bedrock’s services, learn to configure and integrate its APIs, and customize models to meet specific use cases. By the end of the workshop, participants will be able to build, deploy, and scale generative AI models, applying best practices for performance and cost-efficiency in real-world scenarios.

Course Prerequisites

To maximize the benefit from this workshop, participants should have:

  • Basic Knowledge of AWS Services: Familiarity with AWS Console, Identity and Access Management (IAM), and basic AWS services like S3 and Lambda.
  • Foundational Understanding of Machine Learning: Knowledge of machine learning concepts and model tuning is recommended but not essential.
  • Programming Skills: Experience with Python or another programming language for working with APIs and integrating model calls into applications.
  • API Basics: Understanding of API endpoints, authentication, and call handling will be beneficial.

What You Will Learn in This Class

Participants will learn to:

  • Set up, configure, and secure Amazon Bedrock environments
  • Explore and utilize Bedrock’s foundation models for text, image, and chatbot generation
  • Customize and fine-tune foundation models with domain-specific data
  • Integrate Bedrock’s API into applications and automate workflows using AWS services
  • Build an end-to-end generative AI application, including deployment, monitoring, and optimization
  • Scale applications with Amazon Bedrock while optimizing for cost and performance

Course Audience

This workshop is ideal for:

  • Data Scientists and ML Engineers: Looking to apply generative AI techniques in their projects using Amazon Bedrock.
  • Software Developers: Interested in building and integrating generative AI applications with real-world functionalities.
  • Cloud Engineers and Architects: Wanting to understand how Amazon Bedrock fits into the broader AWS ecosystem and how to scale AI workloads.
  • Business Analysts and Technical Leaders: Aiming to explore and lead generative AI initiatives using AWS’s powerful Bedrock service.

Each day allows for 6-8 hours of content, including hands-on labs and time for troubleshooting. Adjustments can be made for a 2-day workshop by combining or shortening less technical sections.

Day 1: Foundations of Amazon Bedrock and Initial Setup

Module 1: Introduction to Amazon Bedrock and Generative AI (1 hour)

  • Overview of Generative AI and Bedrock’s role in AWS ecosystem
  • Key use cases and industry applications

Module 2: Amazon Bedrock Architecture and Services (1 hour)

  • Components of Bedrock (foundation models, infrastructure, and APIs)
  • Supported models (Anthropic, AI21, Stability AI, etc.) and use cases

Module 3: Setting Up Bedrock Environment (2 hours)

  • Hands-on environment setup in AWS console
  • Role setup, API access, and basic configuration
  • Security and permissions management

Module 4: Using Bedrock for Text and Image Generation (2 hours)

  • Exploring text generation and image generation capabilities
  • Hands-on labs: Running first API calls for text and image generation

Module 5: Basics of Model Customization (1 hour)

  • Intro to model tuning and fine-tuning options in Bedrock
  • Hands-on: Running basic model customization

Day 2: Deep Dive into Amazon Bedrock APIs and Customization

Module 6: Working with Bedrock APIs (2 hours)

  • API configurations and methods
  • Securing API calls, setting quotas, and understanding rate limits
  • Hands-on labs: Integrating API calls into a simple application

Module 7: Advanced Model Customization (2 hours)

  • Techniques for fine-tuning with industry-specific data
  • Custom prompts and specialized outputs
  • Hands-on: Tuning and deploying a customized foundation model

Module 8: Bedrock Pipelines and Automation (1.5 hours)

  • Building automation pipelines with Bedrock
  • Integrating with other AWS services (Lambda, S3, etc.)
  • Hands-on lab: Creating a pipeline for automated model deployment and monitoring

Module 9: Cost Management and Performance Monitoring (1 hour)

  • Tools for monitoring model performance and managing costs
  • Strategies for balancing performance and resource use

Day 3: Real-World Applications, Scaling, and Troubleshooting

Module 10: Real-World Applications with Amazon Bedrock (1.5 hours)

  • Case studies in industries like customer service, e-commerce, and content creation
  • Example workflows and architectures

Module 11: Hands-On Lab: Building a GenAI Application (3 hours)

  • Developing an end-to-end generative AI application
  • Integrating Bedrock models for text or image generation
  • Troubleshooting and refining the application in real-time

Module 12: Scaling and Optimization (1 hour)

  • Best practices for scaling Bedrock applications
  • Optimizing for performance and cost-efficiency
  • Hands-on: Experimenting with scaling scenarios

Module 13: Troubleshooting and Support (1 hour)

  • Common challenges and resolutions
  • Support resources and community insights

Wrap-Up and Q&A (30 mins)

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