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AI CERTS® certification
AI+ Cloud Practitioner™
Formerly known as AI+ Cloud™ Transform Cloud Computing with Cutting-Edge AI integration
Enquire about this certificationCourse code: AT-110
Course overview
Review the course before deciding whether it is suitable.
Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies Capstone Project: Gain hands-on experience with real-world applications Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation
- Included
- Instructor-led OR Self-paced course + Official exam + Digital badge
- Duration
- Instructor-Led: 5 days (live or virtual)Self-Paced: 40 hours of content
- Exam format
- 50 questions, 70% passing, 90 minutes, online proctored exam
- Prerequisites
- Key concepts in both AI, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP
Course focus
Why this certification may be relevant.
Enhance AI Integration Across the Organization:
Use AI to accelerate the integration of AI-driven solutions, automating processe.
Stay Ahead in AI-Driven Innovation:
As demand for AI expertise rises, Chief AI Officers with advanced AI knowledge are highly sought after to spearhead AI.
Boost Strategic Decision-Making with AI Analytics:
Master AI models to analyze business data, predict outcomes, and enable more informed, real-time decisions.
Advance Your Career in AI Leadership:
With AI reshaping industries, this certification equips you with the skills needed to lead AI initiatives.
Who it may suit
Check the intended audience against your experience and goals.
- Cloud Professionals : Enhance your cloud management skills by integrating AI to optimize cloud performance, improve resource utilization.
- Cloud Architects & Engineers : Learn to leverage AI to design scalable cloud infrastructures, automate cloud provisioning, and enhance security.
- IT Infrastructure Managers : Use AI to optimize cloud deployment, automate system management, and improve cloud security and disaster recovery planning.
- Business Leaders : Drive innovation in your organization by adopting AI in cloud technologies to enhance scalability, reduce costs, and optimize cloud solutions.
- Students & Fresh Graduates : Gain a competitive edge in the cloud computing field by mastering AI tools and techniques that are revolutionizing cloud infrastructure.
Course content
Modules supplied by AI CERTs.
Open each module to review the stated topics.
01Module 1: Cloud Fundamentals
- 1.1 Cloud Computing Models
- 1.2 Core Cloud Services
- 1.3 Identity & Access Management (IAM), Security & Compliance Basics
- 1.4 Billing, Cost Optimization, and Cloud Economics
- 1.5 Multi-cloud Concepts
- 1.6 Infrastructure as Code (IaC) Basics with Terraform
- 1.7 Use Cases
- 1.8 Case Studies
- 1.9 Hands-On Activity
02Module 2: AI Fundamentals and Python Fundamentals
- 2.1 Introduction to Artificial Intelligence, Machine Learning Types
- 2.2 Neural Networks and Deep Learning Fundamentals
- 2.3 Python Programming
- 2.4 Essential Libraries
- 2.5 Mathematics for AI
- 2.6 Data Preprocessing, Exploration, and Visualization Techniques
- 2.7 Use Cases
- 2.8 Case Studies
03Module 3: Data Engineering for AI
- 3.1 Data Collection, Storage, and Processing Pipelines (ETL/ELT)
- 3.2 Big Data Technologies
- 3.3 Data Lakes, Data Warehouses, and Feature Stores
- 3.4 Data Quality, Governance, Versioning, and Cataloging
- 3.5 Real-Time Data Streaming
- 3.6 Use Cases
- 3.7 Case Studies
04Module 4: Cloud with AI
- 4.1 Managed AI/ML Platforms
- 4.2 Model Training, Deployment, and Inference on Cloud
- 4.3 Containerization with Docker and Orchestration with Kubernetes
- 4.4 Serverless AI Architectures
- 4.5 Scaling and Monitoring AI Workloads
- 4.6 Use Cases
- 4.7 Case Studies
05Module 5: Generative AI and LLM Models
- 5.1 Transformer Architecture, Attention Mechanism, and Tokenization
- 5.2 Major LLM Families: GPT, Llama, Gemini, Claude, Mistral
- 5.3 Prompt Engineering Techniques
- 5.4 Generative Model Lifecycle
- 5.5 Multimodal Generative AI
- 5.6 Use Cases
- 5.7 Case Studies
06Module 6: Cloud with Generative AI and LLM Models
- 6.1 Deploying and Hosting LLMs on Cloud Platforms
- 6.2 Inference Optimization Techniques
- 6.3 Integration with Cloud-Native Services
- 6.4 Cost Governance for GenAI Workloads
- 6.5 Hybrid and Edge Deployment Strategies
- 6.6 Use Cases
- 6.7 Case Studies
07Module 7: AI Workloads on Cloud
- 7.1 MLOps Lifecycle and Best Practices
- 7.2 Experiment Tracking (MLflow), Model Versioning, and CI/CD Pipelines
- 7.3 Model Monitoring and Performance Drift Detection
- 7.4 Orchestration Tools: SageMaker Pipelines, Vertex AI Pipelines, Kubeflow
- 7.5 Use Cases
- 7.6 Case Studies
08Module 8: Retrieval-Augmented Generation (RAG)
- 8.1 RAG Architecture and Components
- 8.2 Vector Databases and Embeddings
- 8.3 Advanced RAG Patterns
- 8.4 Evaluation Metrics for RAG Systems
- 8.5 Cloud-Native Vector Search Services
- 8.6 Use Cases
- 8.7 Case Studies
09Module 9: Fine-Tuning and Optimization on Cloud
- 9.1 Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT)
- 9.2 Distributed Training and Hyperparameter Optimization
- 9.3 Model Compression, Distillation, and Quantization
- 9.4 Domain Adaptation and Continual Learning
- 9.5 Cloud Tools for Efficient Fine-Tuning
- 9.6 Use Cases
- 9.7 Case Studies
10Module 10: Agentic AI on Cloud
- 10.1 AI Agents Fundamentals
- 10.2 Distributed Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel
- 10.3 Multi-Agent Systems and Orchestration
- 10.4 Autonomous Workflows and Decision Engines
- 10.5 Cloud Deployment of Agentic Systems
- 10.6 Use Cases
- 10.7 Case Studies
11Module 11: Evaluation, Monitoring, Security & Responsible AI
- 11.1 Comprehensive LLM and GenAI Evaluation Frameworks
- 11.2 Bias Detection, Fairness, and Explainability
- 11.3 Security Threats
- 11.4 Guardrails, Content Moderation, and Compliance (GDPR, SOC2)
- 11.5 Responsible AI Governance and Audit Practices
- 11.6 Use Cases
- 11.7 Case Studies
12Module 12: Capstone Project
- 12.1 Problem Identification and Solution Planning
- 12.2 AI Model Development and Cloud Deployment
- 12.3 Deliverables
13Optional Module: AI Agents for Cloud
- 1. What Are AI Agents?
- 2. Examples of AI Agents for Cloud Services
- 3. Significance of AI Agents in Cloud Services
- 4. Trends in AI Agents for Cloud Services
- 5. Importance of AI Agents
- 6. Types of AI Agents
- 7. Case Studies
- 8. Hands-On Activity
Tools referenced by AI CERTs
Platforms and tools included in the course information.
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMakerThis course information is supplied through the AI CERTs catalogue. Kiwi Fern Education will confirm current availability, delivery arrangements, inclusions and fees before enrolment. The programme is not presented as a New Zealand Qualifications Authority qualification.
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Tell us whether you are enquiring for yourself or a team and what capability you want to build. We will confirm the next steps before you commit.