AI+ Developer Practitioner™
AT-310
Formerly known as AI+ Developer™ <br> <br> Get hands-on with the tools and technologies that power the AI ecosystem.- Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
- Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
- Advanced Modules: Includes time series, model explainability, and cloud deployment
- Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Job Roles & Industry Outlook
AI Systems Engineer
Design and optimize data pipelines, warehouses, and systems to enable AI-driven analytics and applications.
Robotics Engineer
Design and program robotic systems that use AI to sense, adapt, and act more reliably in the real world.
Cloud Architect / Engineer
Design and build multi-cloud systems that scale automatically, using AI to guide capacity and performance decisions.
Developer
Write and ship software faster by pairing your coding skills with AI tools that handle boilerplate, testing, and debugging.
Data Analyst / Engineer
Turn raw data into reliable pipelines and clear insights that help teams make faster, better-informed decisions.
Software Tester
Catch bugs faster by pairing manual testing instincts with AI tools that generate and run test cases automatically.
Industry Growth: AI+ Developer Practitioner™
- Master Key AI Development Skills: Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
- Specialize in Cutting-Edge AI Domains: Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
- Stay Ahead in AI Development: AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
- Advance Your Career in AI Development: With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.
Who Should Enroll?
- Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
- Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
- Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
- IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
- Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
What You'll Learn
- 1.1 Understanding Artificial Intelligence
- 1.2 Components of an AI Application
- 1.3 Beginner AI Development Workflow
- 1.4 AI Development Concepts and Limitations
- 1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
- 1.6 Use Case: Selecting the Right AI Approach
- 2.1 Python Foundations
- 2.2 Python Data Structures and File Handling
- 2.3 Beginner Software-Development Practices
- 2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
- 2.5 Use Case: Automated File Processing Utility
- 3.1 Working with NumPy and Pandas
- 3.2 Data Cleaning
- 3.3 Exploratory Data Analysis
- 3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
- 3.5 Use Case: Retail Sales Data Preparation
- 4.1 Essential Mathematical Concepts
- 4.2 Essential Statistics
- 4.3 Mathematical Reasoning for AI
- 4.4 Case Study: Average Performance Hides a Major Customer Problem
- 4.5 Use Case: Similarity-Based Product Recommendation
- 5.1 Understanding Machine Learning
- 5.2 Supervised Machine Learning
- 5.3 Unsupervised and Other Beginner Methods
- 5.4 Case Study: Customer Churn Prediction
- 5.5 Use Case: Delivery-Time Prediction
- 6.1 Model Evaluation Metrics
- 6.2 Improving Model Performance
- 6.3 Reliable Model Delivery
- 6.4 Case Study: A High-Accuracy Model Misses the Important Cases
- 6.5 Use Case: Spam Email Detection
- 7.1 Neural Network Fundamentals
- 7.2 Beginner Deep Learning with PyTorch
- 7.3 Computer Vision Foundations
- 7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
- 7.5 Use Case: Product Image Classification
- 8.1 Text Processing Fundamentals
- 8.2 Embeddings and Transformers
- 8.3 Large Language Model Fundamentals
- 8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
- 8.5 Use Case: Customer Review Analysis
- 9.1 Prompt Engineering Foundations
- 9.2 Building Controlled Generative AI Applications
- 9.3 Multimodal AI Foundations
- 9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
- 9.5 Use Case: Multimodal Product Information Assistant
- 10.1 Retrieval Fundamentals
- 10.2 Building a Basic RAG Workflow
- 10.3 RAG Quality and Control
- 10.4 Case Study: A Policy Assistant Returns an Outdated Rule
- 10.5 Use Case: Employee Handbook Assistant
- 11.1 API Development for AI
- 11.2 Basic AI Agents and Tool Use
- 11.3 Beginner Deployment and Operations
- 11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
- 11.5 Use Case: IT Support Triage Assistant
- 12.1 Responsible AI Foundations
- 12.2 AI Application Security
- 12.3 Monitoring and Production Readiness
- 12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
- 12.5 Use Case: Beginner AI Release Checklist
- 1.1 What Are AI Agents?
- 1.2 Significance of AI Agents for Developers
- 1.3 Applications and Trends of AI Agents for Developers
- 1.4 How Does an AI Agent Work?
- 1.5 Core Characteristics of AI Agents
- 1.6 Importance of AI Agents
- 1.7 Types of AI Agents
- 1.8 Comparison Table of AI Agents in Ethics
Tools You'll Explore
GitHub Copilot
Lobe
H2O.ai
Snorkel
Prerequisites
- Basic math, computer science fundamentals, fundamental programming skills
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Foundations of Modern AI for Developers - 7%
- Module 2: Python Programming for AI - 7%
- Module 3: Data Handling and Visualization - 7%
- Module 4: Practical Mathematics and Statistics for AI - 7%
- Module 5: Machine Learning Fundamentals - 8%
- Module 6: Model Evaluation and Improvement - 8%
- Module 7: Deep Learning and Computer Vision Basics - 8%
- Module 8: Natural Language Processing, Transformers, and LLM Fundamentals - 8%
- Module 9: Generative and Multimodal AI Application Development - 8%
- Module 10: Retrieval-Augmented Generation and Knowledge Assistants - 8%
- Module 11: Simple AI Agents, APIs, and Deployment - 8%
- Module 12: Responsible AI, Security, Monitoring, and Capstone - 8%
- Optional Module: AI Agents for Developer - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.