QODO AI Certification Training

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Artificial Intelligence Engineering

Learn how to build, deploy, and scale QODO AI-powered agents and workflows with DevLabs Alliance’s expert-led QODO AI Certification Training.

The QODO AI Certification Training by DevLabs Alliance is designed to help learners master one of the fastest-growing platforms for building autonomous AI agents, workflow automation, and enterprise-level intelligent systems. QODO AI enables businesses to transform manual processes into AI-driven workflows using advanced LLM capabilities, memory, tools, and multistep reasoning.

+24 Hours

Practical Session

1000

Students Enrolled

Advanced

Level

14

Modules

About QODO AI Certification Training

This training covers everything from QODO AI fundamentals and agent configuration to workflow design, API integration, orchestration, and real-world deployment. Participants will learn how to build intelligent agents that can analyze data, execute tasks, interact with tools, collaborate with other agents, and automate complex business processes.

With hands-on labs, guided exercises, and practical case studies, this course prepares learners to confidently create scalable QODO AI systems. Upon completion, participants receive an industry-recognized QODO AI Certification from DevLabs Alliance, boosting their career in AI engineering, automation, and intelligent system design.

Who Can Take This Course

This course is ideal for AI/ML Engineers and Technical Architects who want to leverage QODO AI for building intelligent workflow solutions.

Suitable For

AI/ML Engineers
Data Scientists
Software Developers
Automation Specialists
Product Managers
Technical Architects
Students
Innovators
Researchers
Business professionals

Total Skills Covered

QODO AI fundamentals

Agent creation & configuration

Workflow automation

Task planning & multistep reasoning

API & tool integrations

Memory systems

Multi-agent orchestration

Real-time monitoring

Deployment & scaling strategies

Real-world applications

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Pay your course fee through flexible EMI plans, with no financial constraints in your learning journey.

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QODO AI Training Certification

To unlock DevLabs Alliance’s certification, you have to complete and submit the use case assignments provided during the course, then assignments submitted by you will be evaluated by DevLabs Alliance’s team of experts and practitioners. Your result will be shared after assessment and certification will be awarded accordingly.

Sample Certificate

DevLabs Alliance's certified professionals do work for prominent international corporations such as Accenture, Optum, Capgemini, Cognizant, TCS, Wipro, IBM, Sapient, Nucleus, Oracle, Expedia, and more.
To gain access to DevLabs Alliance's courses and their corresponding certifications, you need to adhere to the following steps:

Successfully complete and submit the practical assignments assigned during the course.
The DevLabs Alliance team of seasoned experts and practitioners will assess the assignments you submit.
Your assessment results will be communicated to you, and certification will be granted accordingly.

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Doubt Clearing Sessions

Certification Guidance

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Live-Classroom

Online Instructor-Led Training

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24 Hrs Intructor-led

Doubt Clearing Sessions

Certification Guidance

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Program Curriculum

Total Chapters 14

1: END-TO-END WORKFLOW MASTERY

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

2: MULTI-MODULE TEST GENERATION

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

3: KEY USE CASES & VALUE PROPOSITION

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

4: SPEC-FIRST DEVELOPMENT WITH QODO

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

5: INSTALLATION & CONFIGURATION

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

6: CODE QUALITY & ERROR HANDLING

Hands-on Lab 3: Review-Driven Improvements

Apply Qodo review suggestions

Enhance error handling patterns

Implement defensive coding practices

Strengthen input validation

7: QODO BACKGROUND & ARCHITECTURE

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

8: QODO PROGRAM WRAP-UP

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

9: ADVANCED CONCEPTS

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

10: PRACTICAL PROJECT - CODE REVIEW & TEST GENERATION

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

11: SHOWCASE & KNOWLEDGE TRANSFER

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

12: FEATURE FLAGS IMPLEMENTATION PROJECT

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

13: CAPSTONE PROJECT EXECUTION (4 hours)

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)

14: CONCEPT COVERAGE - EXECUTION & APPLICATION

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

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