From 14e7f1956bcf527dd50ce79889a6f12115a63f7e Mon Sep 17 00:00:00 2001 From: cristianexer Date: Wed, 8 Jan 2025 19:08:11 +0000 Subject: [PATCH] Add metadata to documentation files including titles, descriptions, and authors --- .../01-Machine-Learning-Basics.md | 5 +++++ .../02-Deep-Learning-and-Neural-Networks.md | 5 +++++ .../03-Natural-Language-Processing.md | 5 +++++ docs/1.-AI-Fundamentals/04-Computer-Vision.md | 5 +++++ .../05-Reinforcement-Learning.md | 5 +++++ docs/1.-AI-Fundamentals/index.md | 5 +++++ ...1-Problem-Framing-and-Requirements-Analysis.md | 5 +++++ .../02-AI-Architecture-Patterns.md | 5 +++++ ...-Scalability-and-Performance-Considerations.md | 5 +++++ .../04-Cost-Optimization-Strategies.md | 5 +++++ .../05-AI-Solution-Evaluation-Metrics.md | 5 +++++ .../06-Deployment-Strategies-for-AI-Solutions.md | 5 +++++ docs/2.-AI-Solution-Design/index.md | 5 +++++ .../01-Data-Storage-and-Management-Systems.md | 5 +++++ .../02-Data-Pipelines-and-ETL-Processes.md | 5 +++++ .../03-Data-Quality-and-Preprocessing.md | 5 +++++ .../04-Feature-Engineering.md | 5 +++++ .../05-Data-Versioning-and-Lineage.md | 5 +++++ docs/3.-Data-Architecture-for-AI/index.md | 5 +++++ .../01-Model-Development-Workflows.md | 5 +++++ .../02-Model-Training-and-Validation.md | 5 +++++ .../03-Hyperparameter-Tuning.md | 5 +++++ ...04-Model-Versioning-and-Experiment-Tracking.md | 5 +++++ .../05-Model-Deployment-and-Serving.md | 5 +++++ docs/4.-AI-Model-Lifecycle-Management/index.md | 5 +++++ .../01-API-Design-for-AI-Services.md | 5 +++++ .../02-Microservices-Architecture-for-AI.md | 5 +++++ .../03-Containerization-and-Orchestration.md | 5 +++++ .../04-CI\\CD-for-AI-Systems.md" | 5 +++++ .../05-Monitoring-and-Logging.md | 5 +++++ docs/5.-AI-Integration-and-Deployment/index.md | 5 +++++ .../01-Fairness-and-Bias-in-AI.md | 5 +++++ .../02-Transparency-and-Explainability.md | 5 +++++ .../03-Privacy-Preserving-AI-Techniques.md | 5 +++++ .../04-AI-Safety-and-Robustness.md | 5 +++++ .../05-Ethical-Guidelines-and-Frameworks.md | 5 +++++ docs/6.-Ethical-AI-Design/index.md | 5 +++++ .../01-The-Open-Group-Architecture-Framework.md | 5 +++++ .../02-TOGAF-Application-to-AI.md | 5 +++++ .../03-Zachman-Framework-for-AI-Architecture.md | 5 +++++ .../04-ITIL-for-AI-Service-Management.md | 5 +++++ .../05-COBIT-for-AI-Governance.md | 5 +++++ .../index.md | 15 ++++++++++----- .../01-AWS-AI-Services-and-Architecture.md | 5 +++++ .../02-Azure-AI-Platform.md | 5 +++++ .../03-Google-Cloud-AI-Solutions.md | 5 +++++ .../04-IBM-Watson-on-Cloud.md | 5 +++++ .../05-Multi-cloud-AI-Strategies.md | 5 +++++ docs/8.-Cloud-Platforms-for-AI/index.md | 5 +++++ .../01-AI-Risk-Assessment-and-Management.md | 5 +++++ ...2-Data-Protection-and-Privacy-in-AI-Systems.md | 5 +++++ .../03-Model-Governance-and-Compliance.md | 5 +++++ ...4-Securing-AI-Pipelines-and-Infrastructures.md | 5 +++++ .../05-Auditing-and-Monitoring-AI-Systems.md | 5 +++++ docs/9.-AI-Governance-and-Security/index.md | 5 +++++ .../{index.md => solution-design-template.md} | 5 +++++ docs/Learning-Materials/TOGAF/index.md | 5 +++++ docs/index.md | 5 +++++ mkdocs.yml | 4 ++++ 59 files changed, 299 insertions(+), 5 deletions(-) rename docs/Case-Studies-and-Best-Practices/{index.md => solution-design-template.md} (97%) diff --git a/docs/1.-AI-Fundamentals/01-Machine-Learning-Basics.md b/docs/1.-AI-Fundamentals/01-Machine-Learning-Basics.md index 837c8f0..c93e567 100644 --- a/docs/1.-AI-Fundamentals/01-Machine-Learning-Basics.md +++ b/docs/1.-AI-Fundamentals/01-Machine-Learning-Basics.md @@ -1,3 +1,8 @@ +--- +title: Machine Learning Basics +description: A comprehensive guide to machine learning fundamentals for beginners and practitioners by Inference Institute. +author: Inference Institute +--- # Machine Learning Basics ## Introduction to Machine Learning diff --git a/docs/1.-AI-Fundamentals/02-Deep-Learning-and-Neural-Networks.md b/docs/1.-AI-Fundamentals/02-Deep-Learning-and-Neural-Networks.md index 34cddfe..da7e886 100644 --- a/docs/1.-AI-Fundamentals/02-Deep-Learning-and-Neural-Networks.md +++ b/docs/1.-AI-Fundamentals/02-Deep-Learning-and-Neural-Networks.md @@ -1,3 +1,8 @@ +--- +title: Deep Learning and Neural Networks +description: Explore the architecture, concepts, and applications of deep learning and neural networks, from basic perceptrons to advanced transformer models. +author: Inference Institute +--- # Deep Learning and Neural Networks ## Introduction diff --git a/docs/1.-AI-Fundamentals/03-Natural-Language-Processing.md b/docs/1.-AI-Fundamentals/03-Natural-Language-Processing.md index b661e6a..a303d55 100644 --- a/docs/1.-AI-Fundamentals/03-Natural-Language-Processing.md +++ b/docs/1.-AI-Fundamentals/03-Natural-Language-Processing.md @@ -1,3 +1,8 @@ +--- +title: Natural Language Processing +description: Explore the core concepts and techniques in Natural Language Processing (NLP), including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. +author: Inference Institute +--- # Natural Language Processing ## Introduction diff --git a/docs/1.-AI-Fundamentals/04-Computer-Vision.md b/docs/1.-AI-Fundamentals/04-Computer-Vision.md index 46afd8f..aaaaf14 100644 --- a/docs/1.-AI-Fundamentals/04-Computer-Vision.md +++ b/docs/1.-AI-Fundamentals/04-Computer-Vision.md @@ -1,3 +1,8 @@ +--- +title: Computer Vision +description: Explore the core concepts and techniques in Computer Vision, from image processing and feature detection to object detection and semantic segmentation. +author: Inference Institute +--- # Computer Vision Computer Vision (CV) is a field of artificial intelligence that enables machines to interpret and understand visual information from the world around them. It's the science of making computers gain high-level understanding from digital images or videos, aiming to automate tasks that the human visual system can do. diff --git a/docs/1.-AI-Fundamentals/05-Reinforcement-Learning.md b/docs/1.-AI-Fundamentals/05-Reinforcement-Learning.md index 05b7c27..ff9bc4c 100644 --- a/docs/1.-AI-Fundamentals/05-Reinforcement-Learning.md +++ b/docs/1.-AI-Fundamentals/05-Reinforcement-Learning.md @@ -1,3 +1,8 @@ +--- +title: Reinforcement Learning +description: Explore the principles, algorithms, and applications of Reinforcement Learning (RL), a powerful paradigm in machine learning that enables agents to learn through interaction with an environment. +author: Inference Institute +--- # Reinforcement Learning Reinforcement Learning (RL) is a powerful paradigm in machine learning where an agent learns to make decisions by interacting with an environment. It's inspired by behavioral psychology, focusing on how software agents ought to take actions in an environment to maximize some notion of cumulative reward. diff --git a/docs/1.-AI-Fundamentals/index.md b/docs/1.-AI-Fundamentals/index.md index b934555..36dd452 100644 --- a/docs/1.-AI-Fundamentals/index.md +++ b/docs/1.-AI-Fundamentals/index.md @@ -1,3 +1,8 @@ +--- +title: AI Fundamentals +description: Explore the core concepts and technologies that form the foundation of modern artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. +author: Inference Institute +--- # AI Fundamentals Welcome to the AI Fundamentals section of our AI Solution Architect handbook. This section provides a comprehensive overview of the core concepts and technologies that form the foundation of modern artificial intelligence. diff --git a/docs/2.-AI-Solution-Design/01-Problem-Framing-and-Requirements-Analysis.md b/docs/2.-AI-Solution-Design/01-Problem-Framing-and-Requirements-Analysis.md index cbf4a7e..6fc26f4 100644 --- a/docs/2.-AI-Solution-Design/01-Problem-Framing-and-Requirements-Analysis.md +++ b/docs/2.-AI-Solution-Design/01-Problem-Framing-and-Requirements-Analysis.md @@ -1,3 +1,8 @@ +--- +title: Problem Framing and Requirements Analysis +description: Learn how to properly define the problem, gather requirements, and set the foundation for a successful AI project. +author: Inference Institute +--- # Problem Framing and Requirements Analysis In this section, we will dive into **Problem Framing and Requirements Analysis**, the crucial first step in designing an effective AI solution. This stage sets the foundation for the entire project, ensuring that the problem is well-understood, the requirements are clear, and the solution is aligned with business objectives. diff --git a/docs/2.-AI-Solution-Design/02-AI-Architecture-Patterns.md b/docs/2.-AI-Solution-Design/02-AI-Architecture-Patterns.md index a741827..f5db265 100644 --- a/docs/2.-AI-Solution-Design/02-AI-Architecture-Patterns.md +++ b/docs/2.-AI-Solution-Design/02-AI-Architecture-Patterns.md @@ -1,3 +1,8 @@ +--- +title: AI Architecture Patterns +description: Explore different AI architecture patterns and learn when to use them to build scalable, robust, and maintainable AI solutions. +author: Inference Institute +--- # AI Architecture Patterns In this section, we explore different **AI architecture patterns** that can be leveraged to build scalable, robust, and maintainable AI solutions. Selecting the right architecture pattern is a critical decision that directly impacts your system's performance, cost, and flexibility. This section will provide an overview of common architecture patterns and when to use them. diff --git a/docs/2.-AI-Solution-Design/03-Scalability-and-Performance-Considerations.md b/docs/2.-AI-Solution-Design/03-Scalability-and-Performance-Considerations.md index 16c819f..957e0f2 100644 --- a/docs/2.-AI-Solution-Design/03-Scalability-and-Performance-Considerations.md +++ b/docs/2.-AI-Solution-Design/03-Scalability-and-Performance-Considerations.md @@ -1,3 +1,8 @@ +--- +title: Scalability and Performance Considerations +description: Learn about the critical aspects of scalability and performance in AI solution design, including strategies, patterns, and best practices for building scalable and high-performance AI systems. +author: Inference Institute +--- # Scalability and Performance Considerations In this section, we focus on the critical aspects of **scalability and performance** in AI solution design. Building scalable and high-performance AI systems is essential to meet the growing demands of users and handle increasing data volumes effectively. This section will cover strategies, patterns, and best practices for designing AI solutions that are both scalable and performant. diff --git a/docs/2.-AI-Solution-Design/04-Cost-Optimization-Strategies.md b/docs/2.-AI-Solution-Design/04-Cost-Optimization-Strategies.md index 05c88a6..5cf5f69 100644 --- a/docs/2.-AI-Solution-Design/04-Cost-Optimization-Strategies.md +++ b/docs/2.-AI-Solution-Design/04-Cost-Optimization-Strategies.md @@ -1,3 +1,8 @@ +--- +title: Cost Optimization Strategies +description: Learn about cost optimization strategies for AI solutions, including efficient resource allocation, cloud cost management, model optimization, data storage optimization, and monitoring and budgeting. +author: Inference Institute +--- # Cost Optimization Strategies In this section, we focus on **Cost Optimization Strategies** for AI solutions. Developing and maintaining AI systems can be resource-intensive, especially when scaling for production use. Effective cost management involves balancing performance and scalability without overspending on infrastructure, storage, or compute resources. diff --git a/docs/2.-AI-Solution-Design/05-AI-Solution-Evaluation-Metrics.md b/docs/2.-AI-Solution-Design/05-AI-Solution-Evaluation-Metrics.md index 32159a1..a9c897c 100644 --- a/docs/2.-AI-Solution-Design/05-AI-Solution-Evaluation-Metrics.md +++ b/docs/2.-AI-Solution-Design/05-AI-Solution-Evaluation-Metrics.md @@ -1,3 +1,8 @@ +--- +title: AI Solution Evaluation Metrics +description: Explore how to effectively evaluate the performance of your AI solutions using a comprehensive set of metrics, including accuracy, performance, business impact, and user experience. +author: Inference Institute +--- # AI Solution Evaluation Metrics In this section, we will explore how to effectively evaluate the performance of your AI solutions using a comprehensive set of metrics. Proper evaluation is crucial to ensure that your AI models are not only accurate but also aligned with business goals and user expectations. diff --git a/docs/2.-AI-Solution-Design/06-Deployment-Strategies-for-AI-Solutions.md b/docs/2.-AI-Solution-Design/06-Deployment-Strategies-for-AI-Solutions.md index 5c765c4..d4b3a5b 100644 --- a/docs/2.-AI-Solution-Design/06-Deployment-Strategies-for-AI-Solutions.md +++ b/docs/2.-AI-Solution-Design/06-Deployment-Strategies-for-AI-Solutions.md @@ -1,3 +1,8 @@ +--- +title: Deployment Strategies for AI Solutions +description: Explore the best practices and strategies for deploying AI models, focusing on various deployment paradigms, infrastructure options, deployment strategies, monitoring, and maintenance. +author: Inference Institute +--- # Deployment Strategies for AI Solutions Deploying AI solutions into production is a critical step in the AI lifecycle. An effective deployment strategy ensures that the model performs well in real-world scenarios, scales to meet user demand, and can be easily maintained and monitored. This section explores the best practices and strategies for deploying AI models, focusing on various deployment paradigms, infrastructure options, deployment strategies, monitoring, and maintenance. diff --git a/docs/2.-AI-Solution-Design/index.md b/docs/2.-AI-Solution-Design/index.md index b58ebbb..44771fa 100644 --- a/docs/2.-AI-Solution-Design/index.md +++ b/docs/2.-AI-Solution-Design/index.md @@ -1,3 +1,8 @@ +--- +title: AI Solution Design +description: Learn how to design effective, scalable, and cost-efficient AI solutions, covering problem framing, architecture patterns, scalability, cost optimization, and evaluation metrics. +author: Inference Institute +--- # AI Solution Design Welcome to the AI Solution Design section of our AI Solution Architect handbook. This section focuses on the practical aspects of designing and implementing AI solutions that are effective, scalable, and cost-efficient. diff --git a/docs/3.-Data-Architecture-for-AI/01-Data-Storage-and-Management-Systems.md b/docs/3.-Data-Architecture-for-AI/01-Data-Storage-and-Management-Systems.md index 1164032..8f42216 100644 --- a/docs/3.-Data-Architecture-for-AI/01-Data-Storage-and-Management-Systems.md +++ b/docs/3.-Data-Architecture-for-AI/01-Data-Storage-and-Management-Systems.md @@ -1,3 +1,8 @@ +--- +title: Data Storage and Management Systems +description: Learn about various data storage solutions and how to choose the right one for your AI projects, including relational databases, NoSQL databases, data lakes, and data warehouses. +author: Inference Institute +--- # Data Storage and Management Systems Choosing the right data storage and management system is foundational for designing robust AI architectures. AI projects require storage solutions that can handle vast amounts of diverse data, provide fast access, and scale efficiently. This page covers the following key data storage options, highlighting their strengths, use cases, and potential pitfalls. diff --git a/docs/3.-Data-Architecture-for-AI/02-Data-Pipelines-and-ETL-Processes.md b/docs/3.-Data-Architecture-for-AI/02-Data-Pipelines-and-ETL-Processes.md index 8feab59..0b721a6 100644 --- a/docs/3.-Data-Architecture-for-AI/02-Data-Pipelines-and-ETL-Processes.md +++ b/docs/3.-Data-Architecture-for-AI/02-Data-Pipelines-and-ETL-Processes.md @@ -1,3 +1,8 @@ +--- +title: Data Pipelines and ETL Processes +description: Learn about the critical aspects of data pipelines and ETL processes in AI systems, including best practices, design patterns, and real-world examples. +author: Inference Institute +--- # Data Pipelines and ETL Processes Data pipelines and ETL (Extract, Transform, Load) processes are critical elements in the data architecture of AI solutions. They enable the movement, transformation, and management of data across various systems, ensuring that high-quality, clean, and enriched data is made available for analytics and AI model training. In this section, we will provide a comprehensive overview of data pipelines, ETL processes, and modern data processing frameworks, including best practices, design patterns, and real-world examples. diff --git a/docs/3.-Data-Architecture-for-AI/03-Data-Quality-and-Preprocessing.md b/docs/3.-Data-Architecture-for-AI/03-Data-Quality-and-Preprocessing.md index c413073..1cf38e2 100644 --- a/docs/3.-Data-Architecture-for-AI/03-Data-Quality-and-Preprocessing.md +++ b/docs/3.-Data-Architecture-for-AI/03-Data-Quality-and-Preprocessing.md @@ -1,3 +1,8 @@ +--- +title: Data Quality and Preprocessing +description: Learn about the key techniques, best practices, and tools for data quality and preprocessing in AI architectures. +author: Inference Institute +--- # Data Quality and Preprocessing High-quality data is the bedrock of successful AI projects. Poor data quality can lead to inaccurate model predictions, biased outcomes, and unreliable insights. Preprocessing ensures that data is clean, consistent, and ready for analysis, helping to maximize the performance of AI models. This section covers the key techniques, best practices, and tools for data quality and preprocessing in AI architectures. diff --git a/docs/3.-Data-Architecture-for-AI/04-Feature-Engineering.md b/docs/3.-Data-Architecture-for-AI/04-Feature-Engineering.md index ce8e633..f010007 100644 --- a/docs/3.-Data-Architecture-for-AI/04-Feature-Engineering.md +++ b/docs/3.-Data-Architecture-for-AI/04-Feature-Engineering.md @@ -1,3 +1,8 @@ +--- +title: Feature Engineering +description: Learn the art and science of creating, selecting, and transforming features to improve the performance of machine learning models. +author: Inference Institute +--- # Feature Engineering Feature engineering is the process of creating new input variables (features) or transforming existing ones to improve the performance of machine learning models. It is a crucial step in the data preparation phase and can often be the difference between a good model and a great model. Effective feature engineering leverages domain knowledge, statistical analysis, and data transformations to create features that provide the model with meaningful signals. diff --git a/docs/3.-Data-Architecture-for-AI/05-Data-Versioning-and-Lineage.md b/docs/3.-Data-Architecture-for-AI/05-Data-Versioning-and-Lineage.md index 5c59296..10ca786 100644 --- a/docs/3.-Data-Architecture-for-AI/05-Data-Versioning-and-Lineage.md +++ b/docs/3.-Data-Architecture-for-AI/05-Data-Versioning-and-Lineage.md @@ -1,3 +1,8 @@ +--- +title: Data Versioning and Lineage +description: Learn about the critical aspects of tracking data changes over time and maintaining clear lineage for reproducibility and compliance. +author: Inference Institute +--- # Data Versioning and Lineage Data versioning and lineage are critical components of a robust data architecture, especially for AI-driven systems. They help track how data evolves over time, document its journey through various stages of the data pipeline, and provide a transparent view of the entire data lifecycle. By implementing these practices, AI architects can ensure reproducibility, improve compliance, enhance collaboration, and streamline debugging efforts. diff --git a/docs/3.-Data-Architecture-for-AI/index.md b/docs/3.-Data-Architecture-for-AI/index.md index d55c9ad..ec91dd0 100644 --- a/docs/3.-Data-Architecture-for-AI/index.md +++ b/docs/3.-Data-Architecture-for-AI/index.md @@ -1,3 +1,8 @@ +--- +title: Data Architecture for AI +description: Learn about the critical aspects of designing and implementing robust data architectures to support AI systems. +author: Inference Institute +--- # Data Architecture for AI Welcome to the Data Architecture for AI section of our AI Solution Architect handbook. This section focuses on the critical aspects of designing and implementing robust data architectures to support AI systems. diff --git a/docs/4.-AI-Model-Lifecycle-Management/01-Model-Development-Workflows.md b/docs/4.-AI-Model-Lifecycle-Management/01-Model-Development-Workflows.md index 048ee0d..b95939f 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/01-Model-Development-Workflows.md +++ b/docs/4.-AI-Model-Lifecycle-Management/01-Model-Development-Workflows.md @@ -1,3 +1,8 @@ +--- +title: Model Development Workflows +description: Learn about best practices for establishing AI model development workflows, including data preparation, exploratory data analysis, prototyping, and iterative experimentation. +author: Inference Institute +--- # Model Development Workflows Effective model development workflows are crucial for building robust AI systems. A well-structured workflow ensures that data scientists and engineers can collaborate seamlessly, track progress, and iterate on models efficiently. This section covers best practices for establishing an AI model development workflow, including data preparation, exploratory data analysis (EDA), prototyping, and iterative experimentation. diff --git a/docs/4.-AI-Model-Lifecycle-Management/02-Model-Training-and-Validation.md b/docs/4.-AI-Model-Lifecycle-Management/02-Model-Training-and-Validation.md index a1e98a9..7731cd4 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/02-Model-Training-and-Validation.md +++ b/docs/4.-AI-Model-Lifecycle-Management/02-Model-Training-and-Validation.md @@ -1,3 +1,8 @@ +--- +title: Model Training and Validation +description: Learn about the key steps in model training and validation, including data splitting, algorithm selection, model training, validation techniques, evaluation metrics, and iterative improvement. +author: Inference Institute +--- # Model Training and Validation Model training and validation are core components of the AI model lifecycle. This stage is where the model learns patterns from the data, is evaluated for its predictive performance, and is iteratively refined based on the validation results. In this section, we will cover the end-to-end process of model training and validation, including best practices, techniques, and real-world examples. diff --git a/docs/4.-AI-Model-Lifecycle-Management/03-Hyperparameter-Tuning.md b/docs/4.-AI-Model-Lifecycle-Management/03-Hyperparameter-Tuning.md index cb37242..31019e7 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/03-Hyperparameter-Tuning.md +++ b/docs/4.-AI-Model-Lifecycle-Management/03-Hyperparameter-Tuning.md @@ -1,3 +1,8 @@ +--- +title: Hyperparameter Tuning +description: Learn about the process of hyperparameter tuning, its importance in optimizing machine learning models, and the strategies and tools used for efficient tuning. +author: Inference Institute +--- # Hyperparameter Tuning Hyperparameter tuning is the process of systematically searching for the best hyperparameters for a machine learning model. Unlike model parameters (e.g., weights in a neural network), hyperparameters are set before training and govern the model’s overall behavior, such as learning rate, depth of decision trees, or regularization strength. Effective hyperparameter tuning can significantly enhance model performance, reduce overfitting, and improve generalization. diff --git a/docs/4.-AI-Model-Lifecycle-Management/04-Model-Versioning-and-Experiment-Tracking.md b/docs/4.-AI-Model-Lifecycle-Management/04-Model-Versioning-and-Experiment-Tracking.md index 0e6af56..86c002d 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/04-Model-Versioning-and-Experiment-Tracking.md +++ b/docs/4.-AI-Model-Lifecycle-Management/04-Model-Versioning-and-Experiment-Tracking.md @@ -1,3 +1,8 @@ +--- +title: Model Versioning and Experiment Tracking +description: Learn about best practices, tools, and strategies for implementing effective model versioning and experiment tracking in AI projects. +author: Inference Institute +--- # Model Versioning and Experiment Tracking Model versioning and experiment tracking are essential practices in the AI model lifecycle that ensure reproducibility, improve collaboration, and maintain a clear record of model evolution. In complex AI projects, multiple versions of models are developed and tested, making it critical to track changes systematically. This section covers best practices, tools, and strategies for implementing effective model versioning and experiment tracking. diff --git a/docs/4.-AI-Model-Lifecycle-Management/05-Model-Deployment-and-Serving.md b/docs/4.-AI-Model-Lifecycle-Management/05-Model-Deployment-and-Serving.md index 80aa410..935afe9 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/05-Model-Deployment-and-Serving.md +++ b/docs/4.-AI-Model-Lifecycle-Management/05-Model-Deployment-and-Serving.md @@ -1,3 +1,8 @@ +--- +title: Model Deployment and Serving +description: Learn about best practices for deploying and serving machine learning models in production environments, including strategies, architectures, and tools for scalability, low latency, and robust monitoring. +author: Inference Institute +--- # Model Deployment and Serving Model deployment and serving are crucial steps in the AI model lifecycle. Once a model has been trained, validated, and optimized, it needs to be deployed into a production environment where it can serve real-time predictions or batch inference requests. This section focuses on best practices for model deployment and serving, including strategies, architectures, and tools to ensure scalability, low latency, and robust monitoring. diff --git a/docs/4.-AI-Model-Lifecycle-Management/index.md b/docs/4.-AI-Model-Lifecycle-Management/index.md index f3369a4..c6c117e 100644 --- a/docs/4.-AI-Model-Lifecycle-Management/index.md +++ b/docs/4.-AI-Model-Lifecycle-Management/index.md @@ -1,3 +1,8 @@ +--- +title: AI Model Lifecycle Management +description: Learn about the end-to-end lifecycle of AI models, from development and training to deployment and maintenance. +author: Inference Institute +--- # AI Model Lifecycle Management Welcome to the **AI Model Lifecycle Management** section of the AI Architect Handbook. This section provides a comprehensive overview of the end-to-end lifecycle of AI models, from development and training to deployment and maintenance. Managing the lifecycle of AI models is a critical aspect of building robust, scalable, and reliable AI solutions. By following best practices in lifecycle management, organizations can streamline the development process, ensure model reproducibility, and maintain consistent model performance in production environments. diff --git a/docs/5.-AI-Integration-and-Deployment/01-API-Design-for-AI-Services.md b/docs/5.-AI-Integration-and-Deployment/01-API-Design-for-AI-Services.md index ad8ca7e..458364d 100644 --- a/docs/5.-AI-Integration-and-Deployment/01-API-Design-for-AI-Services.md +++ b/docs/5.-AI-Integration-and-Deployment/01-API-Design-for-AI-Services.md @@ -1,3 +1,8 @@ +--- +title: API Design for AI Services +description: Learn about the essentials of designing APIs for AI services, including best practices for creating robust, efficient, and secure APIs that enable seamless integration of AI models into real-world applications. +author: Inference Institute +--- # API Design for AI Services In this section, we will cover the essentials of designing APIs for AI services. Effective API design is critical for integrating AI models into real-world applications, enabling seamless access, scalability, and maintainability. The goal is to create robust, efficient, and secure APIs that allow clients to easily interact with AI models, regardless of the underlying technology stack. diff --git a/docs/5.-AI-Integration-and-Deployment/02-Microservices-Architecture-for-AI.md b/docs/5.-AI-Integration-and-Deployment/02-Microservices-Architecture-for-AI.md index b281edf..109e91f 100644 --- a/docs/5.-AI-Integration-and-Deployment/02-Microservices-Architecture-for-AI.md +++ b/docs/5.-AI-Integration-and-Deployment/02-Microservices-Architecture-for-AI.md @@ -1,3 +1,8 @@ +--- +title: Microservices Architecture for AI +description: Learn about designing AI systems using a microservices approach, enabling scalability, modularity, and fault tolerance. +author: Inference Institute +--- # Microservices Architecture for AI The **Microservices Architecture for AI** section dives into designing AI systems using a microservices approach. Microservices provide a scalable, modular, and fault-tolerant structure for deploying AI models and services, allowing different components to be developed, deployed, and maintained independently. This architecture is ideal for complex AI solutions requiring agility, scalability, and resilience. diff --git a/docs/5.-AI-Integration-and-Deployment/03-Containerization-and-Orchestration.md b/docs/5.-AI-Integration-and-Deployment/03-Containerization-and-Orchestration.md index 98ec328..0adf406 100644 --- a/docs/5.-AI-Integration-and-Deployment/03-Containerization-and-Orchestration.md +++ b/docs/5.-AI-Integration-and-Deployment/03-Containerization-and-Orchestration.md @@ -1,3 +1,8 @@ +--- +title: Containerization and Orchestration +description: Explore the essential concepts of deploying AI models using containers and orchestration platforms like Docker and Kubernetes. +author: Inference Institute +--- # Containerization and Orchestration The **Containerization and Orchestration** section explores the essential concepts of deploying AI models using containers and orchestration platforms like Docker and Kubernetes. Containerization allows AI applications to be packaged with all their dependencies, ensuring consistency across environments. Orchestration platforms, in turn, manage these containers, providing scalability, reliability, and ease of maintenance. diff --git "a/docs/5.-AI-Integration-and-Deployment/04-CI\\CD-for-AI-Systems.md" "b/docs/5.-AI-Integration-and-Deployment/04-CI\\CD-for-AI-Systems.md" index 7004610..4115759 100644 --- "a/docs/5.-AI-Integration-and-Deployment/04-CI\\CD-for-AI-Systems.md" +++ "b/docs/5.-AI-Integration-and-Deployment/04-CI\\CD-for-AI-Systems.md" @@ -1,3 +1,8 @@ +--- +title: CI/CD for AI Systems +description: Learn about Continuous Integration (CI) and Continuous Deployment (CD) practices tailored for AI workflows, including data validation, model versioning, and automated testing. +author: Inference Institute +--- # CI/CD for AI Systems The **CI/CD for AI Systems** section focuses on Continuous Integration (CI) and Continuous Deployment (CD) practices tailored for AI workflows. Implementing CI/CD for AI projects helps automate the testing, integration, and deployment of AI models, reducing the time from development to production while ensuring high-quality, reproducible results. This approach enhances the agility and reliability of AI solutions, making it easier to adapt to changing data and evolving business requirements. diff --git a/docs/5.-AI-Integration-and-Deployment/05-Monitoring-and-Logging.md b/docs/5.-AI-Integration-and-Deployment/05-Monitoring-and-Logging.md index 4a6df33..9552895 100644 --- a/docs/5.-AI-Integration-and-Deployment/05-Monitoring-and-Logging.md +++ b/docs/5.-AI-Integration-and-Deployment/05-Monitoring-and-Logging.md @@ -1,3 +1,8 @@ +--- +title: Monitoring and Logging for AI Systems +description: Learn how to establish robust observability for AI models in production, including monitoring performance metrics, detecting data drift, and maintaining system health. +author: Inference Institute +--- # Monitoring and Logging for AI Systems The **Monitoring and Logging for AI Systems** section focuses on establishing robust observability for AI models in production. Effective monitoring and logging help ensure that AI models perform as expected, detect anomalies, and provide insights into system health. Observability is crucial for maintaining model reliability, detecting data and concept drift, and enabling quick debugging of issues in complex AI deployments. diff --git a/docs/5.-AI-Integration-and-Deployment/index.md b/docs/5.-AI-Integration-and-Deployment/index.md index 4bec27f..00456c5 100644 --- a/docs/5.-AI-Integration-and-Deployment/index.md +++ b/docs/5.-AI-Integration-and-Deployment/index.md @@ -1,3 +1,8 @@ +--- +title: AI Integration and Deployment +description: Learn about the practical aspects of integrating AI models into production systems, with an emphasis on building scalable, maintainable, and efficient solutions. +author: Inference Institute +--- # AI Integration and Deployment Welcome to the **AI Integration and Deployment** section of the AI Architect Handbook. This section focuses on the practical aspects of integrating AI models into production systems, with an emphasis on building scalable, maintainable, and efficient solutions. Successful integration involves key components such as API design, microservices architecture, containerization, automated CI/CD pipelines, and monitoring frameworks. diff --git a/docs/6.-Ethical-AI-Design/01-Fairness-and-Bias-in-AI.md b/docs/6.-Ethical-AI-Design/01-Fairness-and-Bias-in-AI.md index 3bd5a88..cdb6b94 100644 --- a/docs/6.-Ethical-AI-Design/01-Fairness-and-Bias-in-AI.md +++ b/docs/6.-Ethical-AI-Design/01-Fairness-and-Bias-in-AI.md @@ -1,3 +1,8 @@ +--- +title: Fairness and Bias in AI +description: Learn about common sources of bias in AI systems and strategies for detecting and mitigating bias to ensure fair outcomes. +author: Inference Institute +--- # Fairness and Bias in AI The **Fairness and Bias in AI** page addresses one of the most critical aspects of ethical AI design: ensuring that AI systems produce equitable outcomes across diverse user groups. Bias in AI can arise from various sources, including data collection, model training, and deployment contexts, leading to unfair decisions that disproportionately impact specific groups. This section focuses on understanding, detecting, and mitigating bias to promote fairness in AI systems. diff --git a/docs/6.-Ethical-AI-Design/02-Transparency-and-Explainability.md b/docs/6.-Ethical-AI-Design/02-Transparency-and-Explainability.md index 2822a6f..a5db96a 100644 --- a/docs/6.-Ethical-AI-Design/02-Transparency-and-Explainability.md +++ b/docs/6.-Ethical-AI-Design/02-Transparency-and-Explainability.md @@ -1,3 +1,8 @@ +--- +title: Transparency and Explainability in AI +description: Learn about techniques, best practices, and tools for achieving transparency and explainability in AI systems, ensuring decisions made by AI are interpretable and accountable. +author: Inference Institute +--- # Transparency and Explainability in AI Transparency and explainability are foundational pillars of ethical AI design. They involve making AI systems understandable to users, stakeholders, and regulators, ensuring decisions made by AI are interpretable and accountable. Transparency builds trust, while explainability helps address concerns over fairness, reliability, and usability. diff --git a/docs/6.-Ethical-AI-Design/03-Privacy-Preserving-AI-Techniques.md b/docs/6.-Ethical-AI-Design/03-Privacy-Preserving-AI-Techniques.md index c52af09..091bc65 100644 --- a/docs/6.-Ethical-AI-Design/03-Privacy-Preserving-AI-Techniques.md +++ b/docs/6.-Ethical-AI-Design/03-Privacy-Preserving-AI-Techniques.md @@ -1,3 +1,8 @@ +--- +title: Privacy Preserving AI Techniques +description: Explore methodologies and tools that protect user privacy while enabling powerful AI capabilities. Learn about differential privacy, federated learning, homomorphic encryption, and privacy by design principles. +author: Inference Institute +--- # Privacy-Preserving AI Techniques The **Privacy-Preserving AI Techniques** section focuses on methodologies and tools that protect user privacy while enabling powerful AI capabilities. As AI systems process vast amounts of sensitive data, safeguarding privacy is essential for maintaining trust, complying with regulations, and minimizing risks of data misuse. diff --git a/docs/6.-Ethical-AI-Design/04-AI-Safety-and-Robustness.md b/docs/6.-Ethical-AI-Design/04-AI-Safety-and-Robustness.md index fce6278..ddff998 100644 --- a/docs/6.-Ethical-AI-Design/04-AI-Safety-and-Robustness.md +++ b/docs/6.-Ethical-AI-Design/04-AI-Safety-and-Robustness.md @@ -1,3 +1,8 @@ +--- +title: AI Safety and Robustness +description: Learn about designing AI systems that are resilient, reliable, and safe in real-world applications, focusing on error handling, adversarial robustness, model uncertainty, and fail-safe mechanisms. +author: Inference Institute +--- # AI Safety and Robustness The **AI Safety and Robustness** page focuses on designing AI systems that are resilient, reliable, and safe in real-world applications. Safety involves ensuring AI systems do not cause unintended harm, while robustness ensures that systems can handle unexpected or adversarial inputs gracefully. Together, they form a critical part of building trustworthy AI solutions. diff --git a/docs/6.-Ethical-AI-Design/05-Ethical-Guidelines-and-Frameworks.md b/docs/6.-Ethical-AI-Design/05-Ethical-Guidelines-and-Frameworks.md index 374d065..7cd5221 100644 --- a/docs/6.-Ethical-AI-Design/05-Ethical-Guidelines-and-Frameworks.md +++ b/docs/6.-Ethical-AI-Design/05-Ethical-Guidelines-and-Frameworks.md @@ -1,3 +1,8 @@ +--- +title: Ethical Guidelines and Frameworks for AI +description: Explore established principles, industry standards, and frameworks that guide the ethical development, deployment, and use of AI systems. Learn about key ethical principles, established frameworks, and real-world examples of ethical AI implementation. +author: Inference Institute +--- # Ethical Guidelines and Frameworks for AI The **Ethical Guidelines and Frameworks for AI** section focuses on established principles, industry standards, and frameworks that guide the ethical development, deployment, and use of AI systems. Adhering to ethical guidelines ensures AI systems are aligned with societal values, comply with legal requirements, and build trust among users and stakeholders. diff --git a/docs/6.-Ethical-AI-Design/index.md b/docs/6.-Ethical-AI-Design/index.md index 9964d1f..93eaef3 100644 --- a/docs/6.-Ethical-AI-Design/index.md +++ b/docs/6.-Ethical-AI-Design/index.md @@ -1,3 +1,8 @@ +--- +title: Ethical AI Design +description: Explore the principles and practices of designing AI systems that are fair, transparent, safe, and aligned with societal values. +author: Inference Institute +--- # Ethical AI Design Welcome to the **Ethical AI Design** section of the AI Architect Handbook. This section delves into the principles and practices of designing AI systems that are fair, transparent, safe, and aligned with societal values. As AI becomes more prevalent in critical applications, ethical considerations are paramount to prevent unintended consequences, biases, and harm. By incorporating ethical principles into the design process, organizations can build AI systems that are trustworthy, equitable, and respectful of user privacy. diff --git a/docs/7.-Enterprise-Architecture-Frameworks/01-The-Open-Group-Architecture-Framework.md b/docs/7.-Enterprise-Architecture-Frameworks/01-The-Open-Group-Architecture-Framework.md index 80181b6..47e8d31 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/01-The-Open-Group-Architecture-Framework.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/01-The-Open-Group-Architecture-Framework.md @@ -1,3 +1,8 @@ +--- +title: The Open Group Architecture Framework (TOGAF) +description: A comprehensive framework for enterprise architecture that provides a methodical approach to designing, planning, implementing, and governing enterprise information technology architecture. +author: The Open Group +--- # The Open Group Architecture Framework ## Introduction to TOGAF diff --git a/docs/7.-Enterprise-Architecture-Frameworks/02-TOGAF-Application-to-AI.md b/docs/7.-Enterprise-Architecture-Frameworks/02-TOGAF-Application-to-AI.md index a697fa1..5fe4bf7 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/02-TOGAF-Application-to-AI.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/02-TOGAF-Application-to-AI.md @@ -1,3 +1,8 @@ +--- +title: TOGAF Application to AI +description: Learn how to apply The Open Group Architecture Framework (TOGAF) to AI initiatives. Explore the Architecture Development Method (ADM) for developing AI-enabled enterprise architectures. +author: Inference Institute +--- # Applying TOGAF to AI Initiatives ### Introduction diff --git a/docs/7.-Enterprise-Architecture-Frameworks/03-Zachman-Framework-for-AI-Architecture.md b/docs/7.-Enterprise-Architecture-Frameworks/03-Zachman-Framework-for-AI-Architecture.md index 6c75bdb..87dd18f 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/03-Zachman-Framework-for-AI-Architecture.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/03-Zachman-Framework-for-AI-Architecture.md @@ -1,3 +1,8 @@ +--- +title: Zachman Framework for AI Architecture +description: Learn how to apply the Zachman Framework to design and organize AI architecture. Explore the six perspectives and aspects of the Zachman Framework for AI systems. +author: Inference Institute +--- # Zachman Framework for AI Architecture The **Zachman Framework** is a foundational tool for designing complex systems, offering a structured way to organize and analyze architectural components. It provides a holistic perspective by categorizing information into six fundamental questions (**What**, **How**, **Where**, **Who**, **When**, and **Why**) across six perspectives or roles (e.g., Executive, Business Management, Architect). diff --git a/docs/7.-Enterprise-Architecture-Frameworks/04-ITIL-for-AI-Service-Management.md b/docs/7.-Enterprise-Architecture-Frameworks/04-ITIL-for-AI-Service-Management.md index d2a413e..03fd1de 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/04-ITIL-for-AI-Service-Management.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/04-ITIL-for-AI-Service-Management.md @@ -1,3 +1,8 @@ +--- +title: ITIL for AI Service Management +description: Explore how the ITIL framework can be adapted to manage AI services effectively. Learn about the application of ITIL principles to the lifecycle of AI systems. +author: Inference Institute +--- # ITIL for AI Service Management The **ITIL (Information Technology Infrastructure Library)** framework provides a structured approach to managing IT services, ensuring alignment with business goals, efficiency, and continuous improvement. Applying ITIL principles to **AI Service Management** adapts these best practices to the unique lifecycle and operational needs of AI systems. diff --git a/docs/7.-Enterprise-Architecture-Frameworks/05-COBIT-for-AI-Governance.md b/docs/7.-Enterprise-Architecture-Frameworks/05-COBIT-for-AI-Governance.md index 786ab58..e92c02e 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/05-COBIT-for-AI-Governance.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/05-COBIT-for-AI-Governance.md @@ -1,3 +1,8 @@ +--- +title: COBIT for AI Governance +description: Explore how the COBIT framework can be adapted to establish robust governance for AI systems, ensuring accountability, compliance, and strategic alignment. +author: Inference Institute +--- # COBIT for AI Governance The **COBIT (Control Objectives for Information and Related Technologies)** framework provides a comprehensive approach to governance and management of IT systems. Applying COBIT to **AI Governance** ensures that AI initiatives align with organizational goals, mitigate risks, and deliver measurable value. diff --git a/docs/7.-Enterprise-Architecture-Frameworks/index.md b/docs/7.-Enterprise-Architecture-Frameworks/index.md index 9bdc73c..aeaf46d 100644 --- a/docs/7.-Enterprise-Architecture-Frameworks/index.md +++ b/docs/7.-Enterprise-Architecture-Frameworks/index.md @@ -1,3 +1,8 @@ +--- +title: Enterprise Architecture Frameworks +description: Explore frameworks that provide structured approaches to design, plan, and implement enterprise-wide AI systems. Learn about TOGAF, Zachman Framework, ITIL, and COBIT for AI governance. +author: Inference Institute +--- # Enterprise Architecture Frameworks Welcome to the Enterprise Architecture Frameworks section of our AI Solution Architect handbook. This section explores various frameworks that can be applied to structure and govern AI initiatives within organizations. @@ -8,23 +13,23 @@ Enterprise Architecture Frameworks provide structured approaches to design, plan ## Subsections -###[The Open Group Architecture Framework (TOGAF)](01-The-Open-Group-Architecture-Framework.md) +### [The Open Group Architecture Framework (TOGAF)](01-The-Open-Group-Architecture-Framework.md) Discover TOGAF, one of the most widely used enterprise architecture frameworks. Learn about its core components and how it provides a comprehensive approach to enterprise architecture. -###[TOGAF Application to AI](02-TOGAF-Application-to-AI.md) +### [TOGAF Application to AI](02-TOGAF-Application-to-AI.md) Explore how TOGAF can be adapted and applied specifically to AI initiatives. Understand how to leverage TOGAF's Architecture Development Method (ADM) for AI projects. -###[Zachman Framework for AI Architecture](03-Zachman-Framework-for-AI-Architecture.md) +### [Zachman Framework for AI Architecture](03-Zachman-Framework-for-AI-Architecture.md) Delve into the Zachman Framework and its application to AI architecture. Learn how this matrix-based approach can help in classifying and organizing architectural artifacts for AI systems. -###[ITIL for AI Service Management](04-ITIL-for-AI-Service-Management.md) +### [ITIL for AI Service Management](04-ITIL-for-AI-Service-Management.md) Understand how the Information Technology Infrastructure Library (ITIL) can be applied to manage AI services. Explore best practices for AI service delivery and support. -###[COBIT for AI Governance](05-COBIT-for-AI-Governance.md) +### [COBIT for AI Governance](05-COBIT-for-AI-Governance.md) Learn about COBIT (Control Objectives for Information and Related Technologies) and its role in AI governance. Discover how to establish effective control and governance mechanisms for AI systems. diff --git a/docs/8.-Cloud-Platforms-for-AI/01-AWS-AI-Services-and-Architecture.md b/docs/8.-Cloud-Platforms-for-AI/01-AWS-AI-Services-and-Architecture.md index 716a602..25f17bd 100644 --- a/docs/8.-Cloud-Platforms-for-AI/01-AWS-AI-Services-and-Architecture.md +++ b/docs/8.-Cloud-Platforms-for-AI/01-AWS-AI-Services-and-Architecture.md @@ -1,3 +1,8 @@ +--- +title: AWS AI Services and Architecture +description: Explore the suite of AI services offered by Amazon Web Services (AWS) and learn how to architect end-to-end AI solutions on the AWS cloud platform. +author: Inference Institute +--- # AWS AI Services and Architecture ## Introduction diff --git a/docs/8.-Cloud-Platforms-for-AI/02-Azure-AI-Platform.md b/docs/8.-Cloud-Platforms-for-AI/02-Azure-AI-Platform.md index 933646d..66cfd18 100644 --- a/docs/8.-Cloud-Platforms-for-AI/02-Azure-AI-Platform.md +++ b/docs/8.-Cloud-Platforms-for-AI/02-Azure-AI-Platform.md @@ -1,3 +1,8 @@ +--- +title: Azure AI Platform +description: Explore Microsoft Azure's AI platform, services, and capabilities for building, deploying, and managing AI solutions at scale. Learn about key Azure AI services, architecture components, and best practices for implementing AI on Azure. +author: Inference Institute +--- # Azure AI Platform ## Introduction diff --git a/docs/8.-Cloud-Platforms-for-AI/03-Google-Cloud-AI-Solutions.md b/docs/8.-Cloud-Platforms-for-AI/03-Google-Cloud-AI-Solutions.md index 65b5a86..2672da0 100644 --- a/docs/8.-Cloud-Platforms-for-AI/03-Google-Cloud-AI-Solutions.md +++ b/docs/8.-Cloud-Platforms-for-AI/03-Google-Cloud-AI-Solutions.md @@ -1,3 +1,8 @@ +--- +title: Google Cloud AI Solutions +description: Explore Google Cloud's AI capabilities and learn how to build end-to-end AI platforms on Google Cloud. Discover best practices for deploying, monitoring, and securing AI solutions. +author: Inference Institute +--- # Google Cloud AI Solutions ## Introduction diff --git a/docs/8.-Cloud-Platforms-for-AI/04-IBM-Watson-on-Cloud.md b/docs/8.-Cloud-Platforms-for-AI/04-IBM-Watson-on-Cloud.md index dba4f10..25b66a9 100644 --- a/docs/8.-Cloud-Platforms-for-AI/04-IBM-Watson-on-Cloud.md +++ b/docs/8.-Cloud-Platforms-for-AI/04-IBM-Watson-on-Cloud.md @@ -1,3 +1,8 @@ +--- +title: IBM Watson on Cloud +description: Explore IBM Watson on Cloud, a powerful AI platform that enables enterprises to build, deploy, and scale AI-driven applications. Learn about key services, architecture components, and best practices for leveraging IBM Watson in your AI projects. +author: Inference Institute +--- # IBM Watson on Cloud ## Introduction diff --git a/docs/8.-Cloud-Platforms-for-AI/05-Multi-cloud-AI-Strategies.md b/docs/8.-Cloud-Platforms-for-AI/05-Multi-cloud-AI-Strategies.md index 6abedaa..90051e0 100644 --- a/docs/8.-Cloud-Platforms-for-AI/05-Multi-cloud-AI-Strategies.md +++ b/docs/8.-Cloud-Platforms-for-AI/05-Multi-cloud-AI-Strategies.md @@ -1,3 +1,8 @@ +--- +title: Multi-Cloud AI Strategies +description: Learn about multi-cloud AI strategies, their benefits, capabilities, challenges, and solutions. Explore multi-cloud AI architecture, workflows, and best practices for implementing a multi-cloud AI platform. +author: Inference Institute +--- # Multi-Cloud AI Strategies ## Introduction diff --git a/docs/8.-Cloud-Platforms-for-AI/index.md b/docs/8.-Cloud-Platforms-for-AI/index.md index d3c303e..2a5203e 100644 --- a/docs/8.-Cloud-Platforms-for-AI/index.md +++ b/docs/8.-Cloud-Platforms-for-AI/index.md @@ -1,3 +1,8 @@ +--- +title: Cloud Platforms for AI +description: Explore leading cloud platforms for AI, including AWS, Azure, Google Cloud, and IBM Watson. Learn about key capabilities, architectures, costs, and interoperability for building, deploying, and scaling AI solutions. +author: Inference Institute +--- # Cloud Platforms for AI Cloud platforms have become indispensable for building, deploying, and scaling AI solutions. A **Cloud AI Platform** integrates various technologies and services to facilitate AI workflows, including data storage, compute resources, model development, deployment, and monitoring. This section provides an overview of leading cloud providers for AI and examines their capabilities, architectures, costs, and interoperability across different cloud and hybrid environments. diff --git a/docs/9.-AI-Governance-and-Security/01-AI-Risk-Assessment-and-Management.md b/docs/9.-AI-Governance-and-Security/01-AI-Risk-Assessment-and-Management.md index c90fa9f..c4deada 100644 --- a/docs/9.-AI-Governance-and-Security/01-AI-Risk-Assessment-and-Management.md +++ b/docs/9.-AI-Governance-and-Security/01-AI-Risk-Assessment-and-Management.md @@ -1,3 +1,8 @@ +--- +title: AI Risk Assessment and Management +description: Learn about AI risk assessment and management, including common risks, risk management frameworks, and best practices for mitigating threats. +author: Inference Institute +--- # AI Risk Assessment and Management ## Introduction diff --git a/docs/9.-AI-Governance-and-Security/02-Data-Protection-and-Privacy-in-AI-Systems.md b/docs/9.-AI-Governance-and-Security/02-Data-Protection-and-Privacy-in-AI-Systems.md index 73c54ec..6312257 100644 --- a/docs/9.-AI-Governance-and-Security/02-Data-Protection-and-Privacy-in-AI-Systems.md +++ b/docs/9.-AI-Governance-and-Security/02-Data-Protection-and-Privacy-in-AI-Systems.md @@ -1,3 +1,8 @@ +--- +title: Data Protection and Privacy in AI Systems +description: Learn about the importance of data protection and privacy in AI systems, key challenges, principles, techniques, compliance with regulations, and best practices. +author: Inference Institute +--- # Data Protection and Privacy in AI Systems ## Introduction diff --git a/docs/9.-AI-Governance-and-Security/03-Model-Governance-and-Compliance.md b/docs/9.-AI-Governance-and-Security/03-Model-Governance-and-Compliance.md index 6ff52c2..e8e25cd 100644 --- a/docs/9.-AI-Governance-and-Security/03-Model-Governance-and-Compliance.md +++ b/docs/9.-AI-Governance-and-Security/03-Model-Governance-and-Compliance.md @@ -1,3 +1,8 @@ +--- +title: Model Governance and Compliance +description: Model governance and compliance ensure that AI models are developed, deployed, and managed responsibly, aligning with legal, ethical, and organizational standards. This page explores the frameworks, workflows, and tools necessary to establish effective model governance and compliance strategies. +author: Inference Institute +--- # Model Governance and Compliance ## Introduction diff --git a/docs/9.-AI-Governance-and-Security/04-Securing-AI-Pipelines-and-Infrastructures.md b/docs/9.-AI-Governance-and-Security/04-Securing-AI-Pipelines-and-Infrastructures.md index 640ec95..ca05e2b 100644 --- a/docs/9.-AI-Governance-and-Security/04-Securing-AI-Pipelines-and-Infrastructures.md +++ b/docs/9.-AI-Governance-and-Security/04-Securing-AI-Pipelines-and-Infrastructures.md @@ -1,3 +1,8 @@ +--- +title: Securing AI Pipelines and Infrastructures +description: Learn how to secure AI pipelines and infrastructures to protect data, models, and systems against unauthorized access, adversarial attacks, and operational failures. +author: Inference Institute +--- # Securing AI Pipelines and Infrastructures ## Introduction diff --git a/docs/9.-AI-Governance-and-Security/05-Auditing-and-Monitoring-AI-Systems.md b/docs/9.-AI-Governance-and-Security/05-Auditing-and-Monitoring-AI-Systems.md index 244d585..1c000b5 100644 --- a/docs/9.-AI-Governance-and-Security/05-Auditing-and-Monitoring-AI-Systems.md +++ b/docs/9.-AI-Governance-and-Security/05-Auditing-and-Monitoring-AI-Systems.md @@ -1,3 +1,8 @@ +--- +title: Auditing and Monitoring AI Systems +description: Learn about the importance of auditing and monitoring AI systems, key metrics for monitoring, auditing workflows, and best practices. +author: Inference Institute +--- # Auditing and Monitoring AI Systems ## Introduction diff --git a/docs/9.-AI-Governance-and-Security/index.md b/docs/9.-AI-Governance-and-Security/index.md index 72df46a..9705bd7 100644 --- a/docs/9.-AI-Governance-and-Security/index.md +++ b/docs/9.-AI-Governance-and-Security/index.md @@ -1,3 +1,8 @@ +--- +title: AI Governance and Security +description: AI Governance and Security are foundational to building responsible, trustworthy, and resilient AI systems. This section provides a comprehensive framework for governing AI systems while ensuring their security and ethical alignment. +author: Inference Institute +--- # AI Governance and Security ## Introduction diff --git a/docs/Case-Studies-and-Best-Practices/index.md b/docs/Case-Studies-and-Best-Practices/solution-design-template.md similarity index 97% rename from docs/Case-Studies-and-Best-Practices/index.md rename to docs/Case-Studies-and-Best-Practices/solution-design-template.md index d324f27..0d95ac8 100644 --- a/docs/Case-Studies-and-Best-Practices/index.md +++ b/docs/Case-Studies-and-Best-Practices/solution-design-template.md @@ -1,3 +1,8 @@ +--- +title: AI Enterprise Solution Design & Compliance Template +description: Use this template as a strategic blueprint for designing AI solutions within an enterprise setting. Each section includes guidance and placeholders to help you align your AI initiatives with business goals, comply with global/regional regulations (e.g., EU AI Act, GDPR, HIPAA, CCPA), and maintain an auditable, transparent record of all changes. +author: Inference Institute +--- # AI Enterprise Solution Design & Compliance Template **Instructions:** diff --git a/docs/Learning-Materials/TOGAF/index.md b/docs/Learning-Materials/TOGAF/index.md index 4a5279d..7f58734 100644 --- a/docs/Learning-Materials/TOGAF/index.md +++ b/docs/Learning-Materials/TOGAF/index.md @@ -1,3 +1,8 @@ +--- +title: TOGAF® Enterprise Architecture 10th Edition - Learning Resources +description: Access official learning resources for the TOGAF® Enterprise Architecture 10th Edition to enhance your understanding of the framework and its application in practice. +author: Inference Institute +--- # TOGAF® Enterprise Architecture 10th Edition - Learning Resources ## Introduction diff --git a/docs/index.md b/docs/index.md index fdd1e0b..6259371 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,3 +1,8 @@ +--- +title: AI Architectecture +description: A comprehensive guide to AI systems architecture for architects and developers by Inference Institute. +author: Inference Institute +--- # Introduction ![logo](static/logo.png){: width="300px"} diff --git a/mkdocs.yml b/mkdocs.yml index 15968bd..3cd35a9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,5 +1,6 @@ site_name: AI Architecture site_url: https://architecture.inference.institute +site_description: AI Architecture is a collection of resources for designing, building, and deploying AI systems. copyright: AI Architecture | Infernce Institute repo_name: inference-institute/ai-architecture repo_url: https://github.com/inference-institute/ai-architecture @@ -36,6 +37,7 @@ theme: name: Switch to light mode markdown_extensions: + - meta - attr_list - pymdownx.arithmatex: generic: true @@ -57,6 +59,8 @@ plugins: extra: + manifest: manifest.webmanifest + analytics: provider: google property: G-JF4QZPDSXY