Why Modernize
* Setting up modernization Journey
*Business Framing
*Technical Discovery
*Design Thinking, MVP Build, Build Out
*Drivers for Application Modernization
*Twelve Factor App Review
*Derive opportunity cost of modernization
*Define and Discover application modernization Strategy
* Explore Assessment Criteria
*User research
*Code remediation of existing application
*Security and Compliance
*Performance
*Integration
*Reports and Analytics
*DevOps (includes Develop and Test and Deliver)
*Data migration
*Service Management & Operations
* Resiliency (HA / Backup / DR)
*Infrastructure Delivery (Networking)
* Serviceability
* Implement Continuous Modernization Strategy
*Setup Modernization Goals
*Patterns of Application Modernization
*Monolith to microservices
*Cloud Migration
*Derive Functionality via APIs
* Explore Existing Application and its Stack
*Key Technologies for Application Modernization
*Select optimal modernization Approach
*Identify KPIs to setup monitoring and optimize
*7 R Strategy for Modernizing Applications

Security Introduction
* Factors impacting Application Security
* Application Security Assessment 
* Explore Security Findings
* What to Secure
Security Domains
* Network Access Security
* Network Domain Security
* User Domain Security
* Application Domain Security
* Visibility and Configurability of Security
Infrastructure
* Common Infrastructure Security Threats
* Levels of Infrastructure Security
* Techniques to secure Infrastructure
* Infrastructure Security Solutions
* Securing Message Channels and Carriers
Web and Application Servers
* Common Threats
* OWASP Top 10
* Tips to Developer to Secure Applications
* Configure web/App Server for secure access 
* Authentication and Authorization Frameworks
* Policies guiding Authentication and Authorization
DevOps Security
* Security challenges in DevOps 
* Exploring DevSecOps
* OpenSource IaC Challenges
* Securing Cloud Infrastructure
Data protection Strategies
* Data obfuscation
* Data Encryption
* Data Privacy Preservation
* Adopting Security Framework
*  IT Security Framework
*  Importance of Adopting Framework
* Selecting Right IT Security Framework for domain
Security Standards
* PCI/DSS
* HIPPA
* GDPR
* CIS
* COBIT
Scenarios:
* Banking
* Supply Chain
* Clinical Research


Decomposition Pattern
• Integration patterns
• Database Patterns
• Observability Patterns
• Cross-Cutting Concern Patterns
Implementations-Hand-on(Using Cloud and Others)
* Aggregator.
* API Gateway.
* Chained or Chain of Responsibility.
* Asynchronous Messaging.
* Database or Shared Data.
* Event Sourcing.
* Branch.
* Command Query Responsibility Segregator
* Using Containers and Container orchestration for microservices

Why modernizes Infrastructure
* Mainframe Modernization
* Server Modernization
* Storage Modernization
* Application and IT Modernization
Case Study: Infrastructure Modernization
 * Identify Business Challenge
 *Transformation
 * Result Outcome benefit

* Cloud native Architectures
* Advantage of cloud native over traditional 
* Cloud Native Development
* Cloud Native Principles and Practices
* Practical: Building Cloud Native Applications: using Cloud Native Spring Frameworks

Architecture Transformation
* Embracing Platform Approach
* Phased Approach
* Explore Cloud native 
*Technical Evaluation of Microservices
Technology Transformation
* Build Foundation for digital assets and inventory
* Consider Containerization 
* Serverless 
*  Integration Need and adopting right integration method
Organizational Transformation
* Shift from Project based to product-based development

* Identify Containers impact on Application Modernization
* Engineering Approach to Application Containerization 
* Comparison with Virtual Machines, its benefits 
* Technologies (Hands-on)
* Docker (Container management, packaging and distribution)
* Kubernetes (Orchestration)
* Helm (Simplifying Orchestration)
* OpenShift (Using OpenShift to simplify Container application Management)

Explore Common Software Architectural Patterns
Layered pattern
• Client-server pattern
* Master-slave pattern
* Pipe-filter pattern
* Broker pattern
* Peer-to-peer pattern
* Event-bus pattern
* Model-view-controller pattern
* Blackboard pattern
* Interpreter pattern


Message Construction patterns
* Routing Patterns
* Transformation patterns
* Endpoints and Channels patterns
* Implementation using camel, Kafka and ActiveMQ

* Benefits
* Challenges
* Case study: real case scenario of UI/UX modernization
* Design thinking for UI/UX modernization
* UI/UX Standards
* Tools and Frameworks

Objective of maturity model and maturity assessment 
Levels of Maturity
* Initial
* Managed
* Defined
* Quantitatively Managed
* Optimizing
Maturity model assessment
* Maturity Models Assessment Methods
* Maturity Assessment Tools
* Assessment using Architectural Model template
* Assessment using maturity model Ontology
* Process maturity Gap 
* Business Process Maturity Model 
* Strategic Alignment
* Governance
* Process Model
* Change Management
* Process performance
* Process Improvement
* Tools and Technologies
* Process Management 
* Finalizing the Roadmap
Case Study:1 Modernizing legacy Application 
Case Study 2: Selecting right Approach 7rs

* Cloud Migration Strategies
* Cloud Migration Process
* Benefits and challenges of Cloud Migration
* Container to Container
* Monolith to Microservices
* Traditional DB to Cloud 

Why to infuse Automation to modernize IT
Core principles of DevOps
Building Pipelines in DevOps
Jenkins
Gitlab
Infrastructure as Code
* IaC Concept
* Frameworks
* Ansible
*Terraform
* Saltstack
* Robotic Process Automation 
* Evaluate need and benefit of RPA
* Scenarios that fits use of RPA
* RPA Frameworks and Tools
* Steps to implement RPA


Introduction To Cloud
* Cloud benefits and its role in modernization
* Cloud providers
* Hand-os on AWS,GCP and Azure 
* VPC and asset management 
* DevOps to Cloud Operations
* Cloud Architectures
* Cloud Migration Strategies
* Setting up Hybrid and Multi-cloud Environment
* Cloud Application deployment Strategies

Application Development Languages 
• Web Application frameworks (comprehensive coverage of popular frameworks)
• Distributed Business Logic 
• Web Services and its Frameworks*
* Modernizing Application Servers 
* Messaging Engine 
* Integration and Transformation Logic Implementation
* Logging Frameworks
* Development Methodologies
* Deployment Techniques 
* Monitoring tools and capability Matrix
* Evaluating Application Stack Assessment 
* Creating Tech stack Architecture diagram

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live

self

solo

Effective IT architecture and design serve as the foundation for creating robust, scalable, and secure software systems. This category delves into key concepts for building efficient IT infrastructures, covering software architecture patterns, system design principles, and network architecture. You’ll explore how to design scalable applications, enhance performance, strengthen security, and align IT architecture with your business goals. These courses include critical topics such as microservices, cloud-native architecture, enterprise architecture, and DevOps methodologies, giving you the knowledge to build systems that meet the ever-evolving demands of modern technology environments.

Architecture and Design


*Developer Experience (DX) Focus:
*Documentation quality review
*Script and automation assessment
*Onboarding considerations

*Continuous Improvement:
*Advanced Windsurf features to explore
*Integration with team workflows
*Performance optimization opportunities

*DAY 3 DELIVERABLES:
*Capstone PR#6
*Demo clip
*Reviewer checklist completed
*Release notes draft
*CLI help output
*PR review summary


*Implementing Robust Systems:
*Structured logging patterns
*Error handling strategies
*Observability considerations
*Performance monitoring

*DAY 2 DELIVERABLES:
*PR#5 with refactor + test updates
*Changelog entry
*Refactor checklist completed
*Before/after structure diagram
*Regression safety documentation

*Primary Use Cases:
*Intelligent code planning and architecture
*Multi-file refactoring
*Complex feature implementation
*Debugging and troubleshooting
*Documentation generation

*Cascade AI Capabilities:
*Natural language to code
*Codebase understanding
*Task decomposition
*Dependency tracking
*Test-driven development assistance

*Repository Pattern Introduction:
*Benefits of repository pattern
*Separation of concerns
*Testability improvements
*Dependency injection principles

*Refactoring Strategy:
*Identifying code smells
*Planning refactoring approach
*Maintaining backward compatibility
*Incremental refactoring techniques

*Hands-on Lab 3: Agent-Guided Debugging
*Identify flaky test
*Use Cascade to analyze root cause
*Implement fix with AI guidance
*Verify test stability
*Document debugging process

*Advanced Debugging Techniques:
*Breakpoint strategies
*Log analysis with AI
*Race condition detection
*Memory leak identification

*Hands-on Lab 2: CLI Development with Cascade
*Task: Implement 'report:lint' command
*Use Cascade for planning
*Generate command structure
*Add auto-lint functionality
*Create test hooks
*Verify execution

*Hands-on Lab 3: Multi-File Editing
*Identify files requiring changes
*Use Cascade for coordinated edits
*Maintain consistency across files
*Run tests from IDE terminal
*Debug failures with AI assistance

*Project Task 4: Health-Check Endpoint
*Design health-check API endpoint
*Implement service layer logic
*Create comprehensive tests
*Update README documentation
*Ensure CI pipeline passes


*Feature Selection:
*Review WIN-Feature.md
*Select "Export to CSV" feature
*Analyze requirements and scope
*Rolling Lab: Full Feature Implementation
*Service layer implementation
*CLI command creation
*CSV generation logic
*Error handling and validation
*Comprehensive test suite
*Documentation updates
*Release notes drafting

*Periodic Commits:
*Atomic commit strategy
*Meaningful commit messages
*Branch management


*Deliverable Creation:
*Record feature demo (5-10 minutes)
*Complete reviewer checklist
*Finalize PR#6 documentation
*Prepare CLI help output
*Generate PR review summary

*Team Showcase:
*Feature demonstrations
*Technical deep-dives
*Lessons learned sharing
*Q&A session

*Setup Implementation:
*Download and install Windsurf IDE
*Authentication and account setup
*Project workspace configuration
*Terminal access verification
*Agent permissions and settings

*Hands-on Lab 1: Environment Familiarization
*Clone starter repository: repo-windsurf-refactor
*Navigate Windsurf interface
*Activate Cascade assistant
*Explore code reading features
*Run initial task breakdown exercise

*Effective Cascade Prompting:
*Structuring complex requests
*Providing context effectively
*Iterative refinement techniques
*Multi-step task management

*IDE Workflow Optimization:
*Keyboard shortcuts and efficiency
*Terminal integration best practices
*File navigation strategies
*Git workflow within Windsurf

*DAY 1 DELIVERABLES:
*PR#4 with health-check endpoint + tests
*Updated README
*Passing CI confirmation
*Cascade prompt log
*PR diff documentation
*Test run screenshots

*Hands-on Lab 1: Cascade Refactor Planning
*Analyze UserService module
*Identify seams and interfaces
*Design repository structure
*Plan dependency injection approach
*Create refactoring checklist

*Hands-on Lab 2: Implementation
*Create repository interfaces
*Implement concrete repositories
*Refactor UserService to use repositories
*Update dependency injection configuration
*Modify existing tests
*Add new tests for repositories

*Project Task 5: UserService Refactor
*Complete repository pattern implementation
*Ensure all tests pass
*Improve test reliability metrics
*Create changelog entry
*Document architectural changes
*Create PR#5


*End-to-End Feature Planning:
*Requirements analysis
*Component identification
*API design considerations
*Data model planning
*Test strategy definition

*Cascade Orchestration:
*Managing complex multi-file changes
*Maintaining context across sessions
*Iterative development approach
*Code review preparation

*Introduction to Windsurf IDE
*Purpose-built AI-native IDE
*Cascade AI assistant capabilities
*Difference from traditional IDEs
*Integration philosophy

*Core Technology:
*Cascade AI engine architecture
*Multi-file reasoning capabilities
*Context management systems
*Terminal and tooling integration

*Comparison with Other AI IDEs:
*Cursor AI comparison
*VS Code Copilot comparison
*Unique Windsurf advantages

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*Understanding AI-Generated Code Quality
*How to evaluate AI suggestions
*When to accept vs. modify recommendations
*Best practices for prompt engineering with Qodo
*Integration with CI/CD pipelines
*Documentation and Knowledge Sharing
*Creating lab manual entries
*Building team quick-reference guides
*Establishing review protocols

*DAY 1 DELIVERABLES:
*PR#1 merged or reviewed
*Test run logs
*Qodo review report
*Day 1 lab manual
*Qodo quick-reference guide
*Commit history documentation

*CI/CD Integration Deep Dive
*Automated test execution in pipelines
*Quality gates and thresholds
*Failure analysis and reporting

*Coverage Analysis:
*Interpreting coverage reports
*Identifying untested code paths
*Prioritizing test creation

*DAY 2 DELIVERABLES:
*PR#2 with feature flag + tests
*Coverage report delta
*Prompt templates (spec-first/TDD)
*Refactor checklist
*Backward compatibility notes

*Retrospective:
*What worked well
*Challenges encountered
*Improvements for ongoing use

*Next Steps:
*Continued learning resources
*Community engagement
*Advanced features to explore
*Integration with next tool (Windsurf)

*DAY 3 DELIVERABLES:
*Capstone PR#3
*Before/after metrics report
*Demo recording
*Capstone presentation deck (3 slides)
*Merged PR or sign-off documentation

*Introduction to Qodo (formerly Codium)
*Evolution from Codium to Qodo
*AI-powered code integrity platform overview
*Integration with development workflows
*Privacy and security architecture
*Core Technology Stack
*Large Language Models (LLMs) used in Qodo
*Code analysis engine
*Test generation algorithms
*Integration with Git providers (GitHub, GitLab, Bitbucket)
*Licensing and Enterprise Considerations
*Individual vs. Team vs. Enterprise plans
*Data handling and privacy policies
*On-premise vs. cloud deployment options

*Hands-on Lab 2: AI Code Review Workflow
*Create sample pull request
*Trigger Qodo AI review
*Analyze suggestions and recommendations
*Apply automated fixes
*Review security and best practice alerts

*Hands-on Lab 3: Automated Test Generation
*Select existing module for test coverage
*Generate unit tests using Qodo
*Review and customize generated tests
*Execute test suite
*Analyze coverage reports

*Project Task 1: Fix Failing Tests
*Identify failing tests in baseline
*Use Qodo suggestions to diagnose issues
*Apply AI-recommended fixes
*Improve test coverage
*Create PR: Fix failing tests and improve coverage via Qodo

*Team Presentations:
*Individual capstone demos
*Lessons learned sharing
*Best practices documentation
*Q&A and peer feedback

*Building Organizational Assets:
*Team playbooks
*Prompt libraries
*Review checklists
*Troubleshooting guides

*Project Task 2: Feature Flag with Testing
*Create feature branch: feature/flags
*Implement 'betaMode' feature flag
*Write comprehensive unit tests
*Ensure backward compatibility
*Document migration path
*Create PR#2 with change summary

*Backward Compatibility Strategies:
*A/B testing approaches
*Gradual rollout patterns
*Rollback procedures

*Project Selection:
*Choose issue from QODO-issues.md backlog
*Analyze requirements and acceptance criteria
*Plan implementation approach
*Rolling Lab: Full Cycle Implementation
*Issue -> Design -> Code -> Test -> Review -> PR
*Continuous Qodo integration throughout
*Documentation at each stage
*Regular checkpoint reviews

*Deliverable Creation:
*Capstone PR#3 with full documentation
*Before/after metrics compilation
*Demo recording (5-10 minutes)
*Capstone presentation (3 slides)

*Hands-on Lab 1: Spec-First Prompting
*Write specification for service module
*Generate comprehensive test suite from spec
*Review test coverage across scenarios
*Refine and optimize tests

*Hands-on Lab 2: Cross-Module Testing
*Generate tests across two interdependent modules
*Handle dependencies and mocking
*Ensure integration test coverage
*Validate test isolation

*Primary Use Cases:
*Automated unit test generation
*AI-powered code review
*Test coverage improvement
*Bug detection and code quality enhancement
*Technical debt reduction

*ROI Metrics:
*Time savings in test writing (60-70% reduction)
*Defect detection rate improvements
*Code coverage increase benchmarks
*Developer productivity metrics

*Complete Development Cycle:
*Issue selection and analysis
*Planning and decomposition
*Implementation with AI assistance
*Test generation and validation
*Code review and refinement
*PR creation and merge process

*Quality Metrics Framework:
*Before/after measurements
*Quantifying improvements
*Building business cases for AI tools

*Hands-on Lab 3: Review-Driven Improvements
*Apply Qodo review suggestions
*Enhance error handling patterns
*Implement defensive coding practices
*Strengthen input validation

*Concept Overview:
*Specification-driven test generation
*Test-Driven Development (TDD) with AI assistance
*Creating effective test specifications

*Advanced Prompting Techniques:
*Crafting detailed test scenarios
*Edge case identification
*Boundary condition testing
*Mock and stub generation strategies

*Setup Checklist Implementation:
*IDE extension installation (VS Code, IntelliJ, etc.)
*Git provider authentication
*Repository connection and configuration
*Privacy settings and data governance
*Team workspace setup (if applicable)

*Hands-on Lab 1: Environment Setup
*Clone starter repository: repo-qodo-quality
*Install Qodo extension
*Authenticate with Git provider
*Verify connectivity and permissions
*Run baseline test suite

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*Whiteboarding Session: Design a holistic, production-grade architecture for an agentic application.
*Key Components & Concerns:
*API Layer & Task Ingestion: How users interact with the system (e.g., FastAPI, message queues like RabbitMQ or Kafka for async jobs). 
*Orchestration Service: The containerized LangGraph/CrewAI runtime. Discuss scaling strategies.
*State Management: The critical choice between in-memory, Redis, or a relational database for managing graph state, especially for long- running agents.
*Secure Tool Execution: A deep dive into security. Discuss sandboxing tool execution using Docker, WebAssembly (WASM), or serverless functions to mitigate risks.
*Evaluation & Monitoring: How do you test and monitor a non-deterministic system? Introduce evaluation frameworks (e.g., RAGAs) and the concept of a "golden dataset" for regression testing.
*Capstone Project: Design & Build
*Final Wrap-up, Future Trends & Q&A

*Introduction: Position CrewAI as an opinionated, higher-level framework for orchestrating role-playing autonomous agents.
*Core Abstractions: 
*Agents: Defined by role, goal, backstory, and tools.
*Tasks: The specific, descriptive assignments for each agent.
*Crews & Processes: How agents collaborate, focusing on Sequential and Hierarchical processes.

*Models: Understanding the difference between LLMs and ChatModels and how to integrate various providers (e.g., OpenAI, Anthropic, open-source models via Ollama).
*Prompts: Mastering PromptTemplates for creating reusable, secure, and effective prompts.
*Output Parsers: A critical component for architects. Learn how to enforce schemas and structure LLM responses into reliable data formats (like JSON or Pydantic models) to ensure clean data contracts within a larger system.
*LCEL (LangChain Expression Language): A deep dive into the | (pipe) syntax for building declarative, streamable, and batch-able data flows.
*Hands-on Lab 1: Building a Production-Ready RAG Pipeline

*The Architect's Choice: A detailed discussion on when to use which framework.
*CrewAI: Ideal for rapid prototyping and use cases that fit a collaborative, role-playing model.
*LangGraph: The choice for ultimate control, complex custom state, fine-grained logic, and integrating human-in-the-loop workflows.
*Hands-on Lab 5: Building a Research Team with CrewAI

*State: The central, persistent memory of the graph. We will explore different ways to model state.
*Nodes: The functions or actions that manipulate the state.
*Edges: The directed connections between nodes, with a focus on Conditional Edges, which enable routing, loops, and complex logic.
*Hands-on Lab 3: Refactoring an Agent into a LangGraph

*Concept: Moving beyond single-shot LLM calls to Agentic AI—systems that can perceive, reason, plan, and act to achieve a goal.
*Architectural Discussion: Contrasting traditional software architectures with agentic architectures. We will cover key challenges such as non-
determinism, state management, security, and observability.
*Introduction to LangChain: Positioning LangChain as a foundational framework for composing LLM applications.

*The ReAct Framework: Deconstruct the core Reason-Act loop that gives an agent its autonomy.
*Tools: Define an agent's capabilities. We will cover how to create custom tools and, most importantly, how to write effective tool descriptions so the LLM can reason about their use.
*Agent Executor: Understand the runtime that orchestrates the agent's interaction with its tools.
*Observability with LangSmith: Introduce LangSmith as an essential tool for debugging and tracing an agent's "thought process." Discuss why this is non-negotiable for production systems.
*Hands-on Lab 2: Building a Research Assistant Agent

*Problem Statement: Discuss the architectural limitations of a basic AgentExecutor. When do you need loops, conditional branching, human-in- the-loop validation, or multi-agent collaboration?
*Conceptual Introduction to LangGraph: Position LangGraph as a library for building robust agentic state machines. It provides the control and flexibility needed for enterprise-grade workflows.

*Multi-Agent Collaboration: Design systems using the Agent Supervisor pattern, where one node acts as a manager that delegates tasks to specialized worker agents (other nodes or sub-graphs).
*Human-in-the-Loop: A critical pattern for safety and validation. Learn how to implement checkpoints in a graph where execution pauses and awaits human input or approval.
*Self-Correction Loops: Build graphs where one agent node critiques or reflects on the output of another, forcing a revision cycle until a quality threshold is met.
*Hands-on Lab 4: Building a Multi-Agent "Plan and Execute" Graph

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*AI-Assisted Review Process:
*Leveraging Copilot for PR reviews
*Identifying code smells
*Security vulnerability detection
*Best practice recommendations

*Review Workflow Integration:
*Setting up automated checks
*Custom review checklists
*Collaborative review processes


*Metrics Collection:
*Response time measurements
*Throughput analysis
*Security scan results
*Code quality scores

*Optimization Strategies:
*Query optimization
*Caching strategies
*Connection pooling
*Asynchronous processing

DAY 2 DELIVERABLES:
*PR: pagination + caching + security fixes
*Security review checklist
*Benchmark notes and metrics
*PR review summary


*Primary Use Cases:
*Real-time code suggestions
*Test generation
*Documentation creation
*Code explanation and learning
*Boilerplate generation
*API integration assistance

*Enterprise Capabilities:
*Organization policy controls
*IP indemnification
*Usage analytics
*Security and compliance features


*Introduction to GitHub Copilot:
*Evolution and development by GitHub/Microsoft
*OpenAI Codex foundation
*Enterprise vs. Individual vs. Business plans
*Integration ecosystem

*Core Capabilities:
*Code completion engine
*Chat interface features
*Context awareness mechanisms
*Multi-language support

*Competitive Landscape:
*Comparison with Amazon CodeWhisperer
*Comparison with Tabnine
*GitHub's market position


*Project Selection:
*Choose from COPILOT-issues.md backlog
*Analyze complexity and scope
*Plan end-to-end approach
*Rolling Lab: Full Cycle Execution
*Issue -> Planning -> Implementation -> Testing -> Documentation -> PR -> Merge
*Continuous Copilot integration
*Regular progress checkpoints
*Demo preparation

*Deliverable Creation:
*Capstone PR with complete documentation
*Final showcase recording
*Enablement package preparation


*Team Presentations:
*Capstone project demos
*Technical deep-dives
*Before/after comparisons
*ROI metrics presentation

*Enablement Pack Creation:
*Prompt library compilation
*Checklist collection

*Best practices documentation
*Troubleshooting guides
*Quick reference cards


*Hands-on Lab 1: Test Generation
*Clone starter repository: repo-copilot-app
*Select API controller
*Generate unit tests via Copilot
*Execute and debug generated tests
*Refine test quality

*Hands-on Lab 2: Code Documentation
*Identify complex function
*Use Chat to explain functionality
*Generate comprehensive docstrings
*Add inline comments
*Create README sections

*Hands-on Lab 3: Endpoint Scaffolding
*Design new endpoint structure
*Use Copilot for implementation
*Scaffold test suite
*Implement basic validation

*Project Task: Metrics Endpoint with Rate Limiting
*Implement /metrics endpoint
*Add rate limiting logic
*Create comprehensive tests
*Document API specifications
*Ensure CI passes
*Create PR


*Effective Copilot Prompting:
*Comment-driven development
*Natural language specifications
*Example-based prompting
*Iterative refinement techniques

*Quality Control:
*Reviewing AI-generated code
*Testing and validation strategies
*Security considerations
*Performance implications
*
*DAY 1 DELIVERABLES:
*PR with /metrics endpoint + tests + docs
*CI green confirmation
*Prompt/response snippets collection
*Commit log
*CI badge screenshot


*Hands-on Lab 1: Security-Focused Review
*Analyze vulnerable PR

*Identify security issues:
*Hardcoded secrets
*SQL injection vulnerabilities
*XSS vulnerabilities
*Authentication bypasses
*Apply Copilot-suggested fixes
*Implement security best practices
*Add validation layers

*Secure Coding Patterns:
*Input validation
*Output encoding
*Authentication and authorization
*Secrets management
*Error handling security


*Overall Program Retrospective:
*Cross-tool comparisons (Qodo vs. Windsurf vs. Copilot)
*Use case alignment
*Tool selection criteria
*Integration opportunities

*Learning Metrics:
*Productivity improvements
*Code quality enhancements
*Test coverage increases
*Time savings quantification

*Next Steps & Resources:
*Advanced learning paths
*Community resources
*GitHub Copilot updates and roadmap
*Enterprise adoption strategies
*Continuous improvement framework

*Final Q&A and Feedback:
*Open discussion
*Addressing remaining questions
*Program evaluation
*Certificate distribution

*DAY 3 DELIVERABLES:
*Capstone PR
*Final showcase recording
*Enablement pack (complete)
*Final report draft
*Prompt library
*Complete checklist collection
*Training completion certificates


*Hands-on Lab 2: Pagination Implementation
*Analyze listing endpoint
*Implement cursor-based pagination
*Add query parameter handling
*Create pagination tests
*Validate performance improvements

*Hands-on Lab 3: Caching Layer
*Design in-memory cache strategy
*Implement caching logic
*Add cache invalidation
*Write invalidation tests
*Benchmark performance gains

*Project Task: Security Hardening
*Review auth/validation on API routes
*Implement security enhancements
*Update security documentation
*Create security review checklist
*Create PR: pagination + caching + security


*Setup Implementation:
*Verify Copilot access/license
*Install GitHub Copilot extension
*Enable Copilot Chat
*Configure IDE settings
*Review organization policies

*Security and Governance:
*Data handling policies
*Secure prompting practices
*Avoiding credential exposure
*Managing sensitive information
*Content filtering options


*Complete Development Lifecycle:
*Issue analysis and planning
*Solution design
*Implementation strategy
*Testing approach
*Documentation requirements
*Release preparation

*Best Practices Consolidation:
*Lessons from Days 7-8
*Prompt library organization
*Workflow optimization
*Team collaboration patterns

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 Analyzing Prompt Responses
*Evaluating the effectiveness of prompts
*Techniques for refining prompts based on outputs
*Contextual and Conditional Prompting
*Using context to improve responses
*Conditional prompts for specific tasks
*Hands-on practice: Contextual prompt crafting
*Case Studies and Real-World Applications
*Examining successful prompt engineering examples
*Discussion of industry applications (e.g., chatbots, content generation)

 Ethical Considerations in Prompt Engineering
*Addressing bias and fairness in AI responses
*Best practices for ethical prompt design
*Project Work
*Group project: Developing prompts for a specific application
*Presentations of group projects and peer feedback
* Future Trends in Prompt Engineering
*Emerging technologies and trends in AI
*The evolving role of prompt engineers in AI development


*Introduction to Prompt Engineering
*Overview of Prompt Engineering
*Importance and applications in AI
*Understanding AI Models
*Types of AI models (e.g., GPT, BERT)
*How models interpret prompts
*Crafting Effective Prompts
*Key principles for prompt design
*Techniques for clarity and specificity
*Hands-on practice: Writing basic prompts

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*Reducing hallucination risks
*Maintaining data accuracy
*Ethical AI usage
*Validation techniques
*Balancing speed and accuracy

*Image generation for business
*No-code app development
*AI agent ecosystem
*Workflow automation strategies

*Task + Objective: Defining precise tasks and clear objectives
*Role + Context: Establishing professional context and background information
*Output Format: Specifying desired response structure and presentation requirements
*Guardrails: Setting explicit boundaries to prevent undesired outputs
*Includes practical exercises like:
*Crafting prompts for various business scenarios
*Analyzing and improving existing prompts
*Hands-on prompt engineering workshops

*Document Management (PDF analysis, report summarization)
*Data Analysis (Excel insights, visual representations)
*Communication Tools (email drafting, multilingual communication)
*Research and Insight Generation (credible information sourcing)

*Chain of Thought Prompting
*Step-Back Prompting
*Zero-Shot vs. Few-Shot Prompting
*Context Optimization
*Covers business application scenarios such as:
*Report generation
*Data analysis
*Email drafting
*Strategic planning
*Research compilation

*Advanced document and data management
*Comprehensive data analysis techniques
*Communication and workflow tool mastery
*In-depth research and insight generation strategies

*Basics of Large Language Models
*Differences between traditional search and AI interaction
*Potential risks like hallucination and digression
*Understanding AI's capabilities and limitations

*Practical prompt engineering test
*Case study analysis
*Individual and group exercises
*Feedback and continuous improvement strategies

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Artificial Intelligence Engineering

*Validating Job Definitions
*Promoting Jobs to Production Environment
*Scheduling Approvals and Audit Trails
*Assessment: Scenario-Based Exercises + Practical Evaluation

*Scheduling by Time, Event, or Dependency
*Managing Smart Tables and Groups
*Creating Cyclic and Repetitive Jobs
*Working with Variables and Parameters

*Performing Manual Job Actions (Hold, Rerun, Force, Delete, etc.)
*Using the Monitoring Domain effectively
*Understanding Job Flow Visualization
*Incident Handling Best Practices

*Viewing and Filtering Active Jobs
*Monitoring Job Status, Dependencies, and Conditions
*Handling Job Failures and Reruns
*Managing Job Outputs and Logs
*Working with Alerts

*Viewing Logs and Alerts
*Generating Basic Reports
*Basic Troubleshooting Techniques
*Assessment: Hands-on Lab + Quiz

*Control-M Job Lifecycle
*Creating and Editing Job Definitions
*Understanding Calendars, Tables, and Folders
*Setting up Dependencies and Conditions

*Detailed Overview of Control-M/Enterprise Manager
*Control-M/Server and Control-M/Agent Architecture
*Communication and Configuration

*Batch Flow Design Best Practices
*Utilizing Control-M Workload Policy
*Analyzing Workload Impact

*Managing High Availability and Failover
*Configuring Alerts and Notifications
*Security and Audit Controls
*Assessment: Hands-On Lab + Final Evaluation

*Integrating Control-M with Other Tools (e.g., ServiceNow, SAP, AWS)
*Control-M Automation API Overview
*Using Control-M Self Service Portal

*Overview of Workload Automation
*Control-M Architecture and Components
*Understanding the Control-M GUI and Interfaces
*Role of an Operator

*Control-M Logs and Diagnostic Tools
*Handling Performance and Resource Issues
*Backup and Recovery Procedures
*Version Management and Upgrades

*Installing and Configuring Control-M Components
*User & Security Management
*Defining Data Centers and Nodes

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*Classes and objects
*Attributes and methods
*Inheritance and encapsulation

*Types of errors
*Try, except blocks
*Raising and handling exceptions

*math, datetime, random, etc.
*Working with external libraries (intro)

*Mini projects to apply concepts
*Practice tests for certification readiness

*Defining and calling functions
*Function arguments and return values
*Creating and importing modules

*Numbers, strings, lists, tuples, sets, dictionaries
*Type conversion and input/output

*History and features
*Setting up the environment
*Writing your first Python program

*Conditional statements (if, elif, else)
*Loops (for, while)
*Loop control (break, continue, pass)

*Lists, tuples, dictionaries, and sets
*Indexing, slicing, and built-in methods

*Debugging techniques
*Writing simple test cases

*Reading from and writing to files
*Working with different file formats

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*RESTful APIs and JSON
*Authentication and authorization
*Caching and performance optimization
*Testing and debugging

*Build a complete web application using Rails

*Basic syntax and data types
*Control structures and loops
*Classes, objects, and modules
*Error handling and exceptions

*MVC architecture
*Routing and controllers
*Models and database interaction
*Views and templates

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*Setting Up Rational Rhapsody 
*Step-by-step guidance on installation and configuration.

*UML Basics
*Use Case (UC) Diagram
*Activity Diagram
*Class Diagram
*Sequence Diagram
*State Chart Diagram

*Hands-On Exercises
*Exercise 1: Hello World – Build a simple model to understand core concepts.
*Exercise 2: Count Down – Implement a countdown system to explore functionality.

*Understanding Properties 
*Learn how to configure and utilize properties in Rhapsody.

*Practical Applications
*Exercise 3: Dishwasher – Model a basic dishwasher system.
*Exercise 4: Dishwasher System – Expand the model to include system-level interactions.

*Essential Prerequisites 
*Overview of required knowledge and tools for the course.

*Course Agenda 
*Detailed breakdown of the training schedule and objectives.




*Requirements Analysis and Traceability 
*Techniques to capture and trace system requirements.

*Defining Functional Flow 
*Use activity diagrams to map system processes.

*Use Case Scenarios 
*Develop scenarios to define system interactions.

*System Modeling
*Modeling ports and interfaces.
*Modeling system behavior using state machines.

*Model Animation and Validation
*State-Chart animation.
*Animated Sequence and State Chart execution.


*Verification and validation through model execution.

*Architectural Integration
*Merging functional models into an architectural framework.
*Allocating functions to structural components.
*Hand-off of a subsystem to another team or process.

*Reverse Engineering 
*Techniques to reconstruct models from existing code.

*Rational Rhapsody Framework Overview 
*Understand the underlying framework and its applications.

*Generating Reports 
*Create detailed reports for documentation and analysis.

*Testing with Rational Rhapsody TestConductor
*Introduction to TestConductor for automated testing.
*Practical testing workflows.

*Triggered Operations 
*Explore advanced operations triggered by system events.

*Configuration Management Basics 
*Introduction to version control and model management.

*Advanced Exercises
*Stopwatch Exercise – Build and test a stopwatch model.
*Speed Camera Exercise – Model and simulate a speed camera system.

*Best Practices
*Useful tips for efficient modeling.
*Avoiding common mistakes in Rational Rhapsody.

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*Networking
*OSI Layers
*Various Protocols of Networking in Linux
*Linux Firewall

*Kernel Configuration
*Boot Management
*Grub Bootloader configurations
*Red Hat Package Manager
*Libraries

*Process Management
*System Calls
*Bash Operations
*Expect Script
*Python Scripting

*Linux Installations
*Packages
*Architecture
*File System
*User Management
*Scripting Data

*Linux Security Administration
*Shell Scripting
*Securing Shell
*Hands on

*Users in Linux
*User Configuration
*Group Administration
*Sudo
*Default File Permissions
*Access control Lists (ACLs)
*Hands on

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*What are Enums?
*Operators in Java
*Modifiers-File Level, Access level and Non-access level
*Data Types, Variables, Literals, Type Conversion, Casting
*Control Statements in Java
*Method and Types of methods
*Variables and Types of Variables
*Constructors and Types of Constructors

*Introduction to Thread
*How to create and start a Thread
*Sleep Thread
*Synchronization in Threads

*What is Generic List
*What is Generic Map in Java
*Generic Classes & Methods in Java
*For loop Generic
*Generic Wild Card

*String Operations and Operators
*String Objects
*Passing by Value
*Immutable Strings

*What is Java?
*Editions and Versions of Java
*Java History
*Installing Java, JDK and JVM
*IDE-Eclipse
*Writing your first java program

*Arrays
*Multi-Dimensional Arrays
*Sorting and Searching Arrays
*For-Each

*What are Abstract Classes?
*What are Interfaces?
*Difference between an Abstract Class and Interface

*What is Exception?
*Types of Exception
*Throw, Throws and Throwable
*Creating Exception
*try-catch block
*Finally block

*Encapsulations
*Access Modifiers
*What is Inheritance?
*Method Overloading
*Method Overriding
*Difference between Method Overloading and Method Overriding
*What is Polymorphism?
*Benefits of Polymorphism
*Run-time and Compile-time Polymorphism

*What is Object-Oriented Programming?
*Instantiating Objects
*Using Objects
*Creating Object Types
*Default Constructors
*Building Constructors
*What is Class?
*What is Object?
*Class vs Object
*Static Variables
* Static Method
*“this” keyword
*Garbage Collection
*Java Memory Leaks
*Java Packages
*Java Classpath

*What are Collections?
*List
*Set
*Queue
*Priority Queue
*Maps
*Hashmap

*What are Annotations?
*Why annotations are used
*Built-in Java Annotations
*Custom Annotations
*Placements in Annotations

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*Advanced Data Modelling
*Exploring MongoDB Shell & Server
*Using MongoDB Compass

*Introduction to Databases
*SQL vs NoSQL
*Introduction to MongoDB
*JSON vs MongoDB NoSQL

*Basics of MongoDB
*CRUD Operations (Overview)
*Schemas & Relationships

*Best Practices & Use Cases
*Q&A and Recap

*Working with Indexes
*Advanced Index Concepts

*Deep Dive into Query Operations
*Deep Dive into Read Operations
*Deep Dive into Update Operations
*Deep Dive into Delete Operations

*Backup and Repair Operations
*Tools for Managing Large Data Sets
*Deployment Options for MongoDB Applications

*MongoDB Setup and Installation
*Understanding MongoDB Architecture & Ecosystem

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*Differentiate WLAN architectures
*Describe the stages of the WLAN life cycle

*Explain RF modulation and how it relates to WLAN data rates
*Describe the relationship between SNR and MCS

*Examine the Juniper Mist architecture
*Create a Juniper Mist account
*Summarize Juniper Mist subscriptions
*Summarize the MSP dashboard

*Define the features and benefits
*Identify popular use cases
*Categorize the product options
*Summarize the installation
*Review the Edge management
*Troubleshoot the device and connectivity

*Describe the properties of an RF wave
*Convert dBm to Milliwatts using RF math
*Explain factors that contribute to RF signals and how they relate to WLANs

*Describe site, AP, and client events
*Explain the packet capture functionality of the Juniper Mist system
*Describe the 802.11 MAC header and list 802.11 MAC frame types

*Assess Juniper Mist's application of artificial intelligence
*Describe the reactive and proactive troubleshooting methodologies

*Describe the 2.4-GHz, 5-GHz, and 6-GHz frequency bands used for WLANs and their channels

*Summarize access points and connectivity
*Describe the boot procedure for a Juniper Access Point, its requirements, and the process of adding a Juniper Access Point to the Juniper Mist cloud
*Describe common AP configuration settings
*Use the Juniper Access Points dashboard to get information about an Access Point

*Explain the difference between Marvis natural language and Marvis query language

*Explain the difference between organization-level and site-Ievel configuration objects
*Define Juniper Mist configuration objects and their uses

*Define a SSIDs, BSSIDs, and their functions
*Review additional WLAN configuration options
*Explain WLAN security options and how they are configured in a Juniper Mist WLAN configuration object
*Describe data rates and how they are configured in Juniper Mist
*Explain SSID strategies for multiband deployments

*Describe the 802.11 state machine and steps required for an 802.11 station to connect to an access point
*Explain the protocols used in a client's connection to the network

*Describe WLAN security threats detected by the Juniper Mist WLAN system

*Explain custom dashboard and report options

*List Wi-Fi Assurance SLEs and their classifiers

*Describe the purpose of the 802.11 standard and its physical later amendments

*Explain WLAN policies and how they are configured

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Instructor-led Practical Oriented Training

*Modeling Behavior with SysML (1.5 hours): Activity, Sequence, and State Machine Diagrams for Smart Home Security System.
*Project Work Session 1 - Part A (1.5 hours): Initial hands-on modeling using open-source or conceptual tools.

*Project Session 2B: Final Feedback
*Action Plan Creation

*Continued Traceability (1 hour): Automated linking between requirements, activities, and test cases.
*Introduction to SysML (1 hour): Use Case, Block Definition, Activity, and other diagrams; ATM example.
*Modeling Structure with SysML (1.5 hours): BDD and IBD using a Smart Home Security System example.

*Key Takeaways
*Post-Assessment & Survey
*Flexible Deep Dive or 1:1 Consultations

*Ask the Professor
*Model Management Plans & Defined Roles
*MBSE Design Patterns

*Performing an MBSE Critique (2 hours): Critique frameworks and walkthroughs.
*Current Debates & Future Trends (1 hour): Digital Twins, AI/ML in MBSE, interoperability.

*Integrating MBSE into System Lifecycle
*Agile + MBSE, MBSE Tooling Ecosystem
*Project Session 2A: Critique Application

*Review & Q&A (1 hour): Clarifying content from Week 1.
*Implementation Challenges of MBSE (2 hours): Real-world challenges, Boeing case study.

*Project Work Session 1 - Part B (2 hours): Refining behavioral models with instructor guidance.
*Graded Activity / Self-Assessment (1 hour): Quiz or exercise for concept reinforcement.

*Welcome & Course Kick-off (30 min): Course navigation, collaboration tools, learning objectives, and setting expectations.
*MBSE: Beyond Documents (1 hour): Key concepts, SE vs. MBSE, MBSE benefits and use cases.
*Qualities of Excellent Models & Scoping (1 hour): Attributes like accuracy, clarity, completeness; example of drone system.
*Models as Data Repositories & Traceability (1 hour): Models as queryable data sources; safety-critical function traceability example.

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