Module 1: Introduction to Artificial Intelligence Systems

• Overview of AI Systems
  • Key definitions and concepts in AI Systems.
  • AI technologies evolution
  • Applications and use cases of AI in various industries.
    • AI in Retail
    • AI in Manufacturing
    • AI in Finance
    • AI in Energy/Oil and Gas
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• AI Ecosystem and Tools
  • Understanding AI frameworks (e.g., TensorFlow, PyTorch).
  • Tools for data handling and visualization (e.g., Pandas, NumPy, Matplotlib).

Module 2: Understanding Business Requirements and Challenges

• Business Requirements
  • Identifying business outcomes and map use cases where AI adds value.
  • Understanding Business objectives and technical feasibility.
• Requirements Gathering
  • Data sources and data availability
  • Computational needs
  • Understanding business stakeholder expectations.
• AI systems Implementation Success Metrics
  • Data prediction Accuracy,
  • Precision: understanding “true positive” data sets
  • Recall: Ability to identify all positive outputs
  • Latency: Time taken by AI systems for response
  • Explainability: Human understandability parameters.

Module 3: Data Quality and Data Handling for AI Systems

• Understanding Data Collection and Data Sources
  • Structured vs. unstructured data.
  • Web scraping, APIs, IoT, and proprietary datasets.
• Data Preprocessing
  • Cleaning and transforming data (e.g., handling missing values, normalization).
  • Feature engineering and selection techniques.
• Data Ethics and Governance
  • Ensuring fairness, privacy, and compliance.

Module 4: Machine Learning Fundamentals

 • Supervised vs. unsupervised learning

 • Regression and classification problems

 • Model evaluation metrics

 • Lab: Building a simple regression model using TensorFlow or PyTorch

Module 5: Deep Learning Concepts

 • Neural network Components (neurons, layers, activation functions)

 • Convolutional Neural Networks (CNNs) for image processing

 • Recurrent Neural Networks (RNNs) for sequential data

 • Lab: Building a CNN for image classification

Module 6: AI System Design and Architecture

• System Design Basics
  • Modular architecture for AI pipelines.
  • Choosing between batch and real-time processing.
• Cloud and On-Premise Solutions
  • Designing AI systems for on-prem and AWS
    • High Performance Networking
    • High Performance Computing
    • High Performance Storage
    • Securing AI Systems
  • Hybrid and edge computing.
• Scalability Considerations
  • Distributed training and inference using GPUs/TPUs.
  • Techniques for reducing latency and computational overhead.

• Lab: Desing exercise for On-Prem vs Cloud deployments
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Module 7: Model Deployment

• Model Packaging and Deployment
  • Exporting models (e.g., ONNX, TensorFlow Serving).
  • Deploying via APIs or microservices.
• Containerization and Orchestration
  • Docker, Kubernetes, and serverless architectures.
• Edge AI
  • Deploying AI models on IoT devices.
• Lab: Deploy LLMs on the On-Prem Data Center

Module 8: Monitoring and Maintenance

• Performance Monitoring
  • Tracking drift, decay, and real-time inference performance.
  • Tools for monitoring (e.g., MLflow, Prometheus).
• Continuous Improvement
  • Implementing pipelines for retraining models.
  • Automating updates using CI/CD.

• Labs: Monitoring tools for AI Systems

Module 9: Security, Ethics, and Compliance

• AI Security
  • Protecting models from adversarial attacks.
  • Securing data pipelines and APIs.
• Ethics in AI
  • Bias detection and mitigation.
  • Explainability and transparency in AI models.
• Legal and Compliance
  • GDPR, HIPAA, and other regulatory frameworks.

Module 10: Industry Case Studies and Best Practices

• Real-world examples of successful AI implementations.
• Lessons learned from failed AI projects.
• Guidelines for scaling AI initiatives.

Network Solutions Architects

Presales Architects

Systems Engineers

Channel Partners

AI Infrastructure Architects and Administrators

AI Engineers

ML Engineers