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Unleashing the Power of Azure AI: A Comprehensive Guide

Accelerate Your Understanding of Artificial Intelligence with Azure Cognitive Services, Machine Learning, and More!

Welcome to this issue of “All in AI”. Today, we dive into the world of Azure AI Fundamentals.

We will explore the Microsoft AI-900 exam, preparing for it, and the fundamental concepts of artificial intelligence. Additionally, we will cover key Azure AI services, responsible AI principles, and the usage of Azure Cognitive Services. We will also touch upon topics such as knowledge mining, face detection, speech and translation services, text analytics, OCR and computer vision, form recognition, LUIS, QnA Maker, Azure Bot Service, Azure Machine Learning Service, and Custom Vision. Get ready to expand your knowledge in the exciting field of AI! Stay tuned for more updates and insights in the next issue.

AI-900 Exam: Azure AI Fundamentals The AI-900 exam is designed to validate skills for entry-level machine learning roles. It covers various topics, including Azure Cognitive Services, AI concepts, responsible AI principles, ML pipelines, classical ML models, automated ML (AutoML), and Azure Machine Learning Studio. The exam consists of 60-65 multiple choice and drag/drop questions, with a mix of case studies and standalone questions. There are no coding questions or simulations. The exam can be taken online or at a testing center, with a duration of 60 minutes and a total seat time of 90 minutes. The passing threshold is around 700/1000.

Preparing for AI-900 AI-900 is beginner-friendly, but it is recommended to spend around 15 hours studying if you're new to AI/ML. To prepare for the exam, you can watch video lectures to grasp key concepts, engage in hands-on practice in the Azure portal and ML Studio, reinforce your knowledge with flashcards, quizlets, and practice questions, and take AI-900 certification practice exams. Supplemental certifications such as Azure Fundamentals (AZ-900) and Data Fundamentals (DP-900) can also be beneficial.

Fundamentals of Artificial Intelligence Artificial Intelligence (AI) refers to systems that imitate human behavior and capabilities. Machine Learning (ML) involves models that learn patterns from data to make predictions. Deep Learning uses ML models with deep neural networks inspired by the human brain. There are different types of machine learning, including supervised learning (trained on labeled data), unsupervised learning (find patterns in unlabeled data), and reinforcement learning (interact with the environment to achieve goals). Concepts such as neural networks, GPUs (hardware optimized for parallel computing), labeling, confusion matrix, anomaly detection, and computer vision are important in AI. Classical machine learning tasks include regression (predicting a numerical value), classification (categorizing data into classes), and clustering (grouping data based on similarities and differences).

Key Azure AI Services Azure Cognitive Services offer a range of prebuilt AI services and cognitive APIs. These include Computer Vision (analyze images and videos), Natural Language Processing (understand human languages), and Conversational AI (chatbots, voice assistants, recommendation agents). Azure also provides prebuilt and customizable machine learning capabilities, such as AutoML (quickly build ML models with little data science expertise required) and Azure Machine Learning Studio (an integrated platform for ML model development).

Responsible AI Microsoft follows responsible AI principles, including fairness (treating users fairly without discrimination), reliability and safety (rigorous testing for risks), privacy and security (protecting personal data), inclusiveness (designing for minority populations), transparency (explaining why systems behave as they do), and accountability (assigning responsibility for AI systems). To address bias and ethical concerns, tools like Fairlearn SDK can help assess model fairness, impact assessments can quantify risks and mitigate harm, and ongoing monitoring and testing are essential.

Azure Cognitive Services Azure Cognitive Services offer a family of prebuilt AI services and cognitive APIs. These services cover vision, speech, language, decision, and search capabilities, and they are created by AI researchers at Microsoft. The benefits of using Azure Cognitive Services include the ability to create custom models without needing ML expertise, leverage pre-trained models, deploy on cloud or edge devices, and adhere to ethical AI guidelines. Some examples of Azure Cognitive Services include Computer Vision (analyze images and videos), Text Analytics (perform sentiment analysis and extract key phrases), and Translator (detect and translate languages).

Knowledge Mining Knowledge Mining involves AI-powered information analysis. It includes steps such as ingesting content from data sources, enriching data using Cognitive Services, and exploring the data with search, bots, and analytics. Knowledge Mining can be used for various use cases, including content research, audit risk compliance, business process management, customer support, digital asset management, and contract management.

Face Service The Face Service allows you to detect, recognize, and analyze faces. It has capabilities such as detecting faces in images, identifying attributes like age and emotion, recognizing the same face across images, and identifying specific faces.

Speech and Translation Services Azure offers Speech and Translation services. The Speech service includes features like speech to text, text to speech, and speech translation. The Translation service enables translation among 90+ languages using neural networks.

Text Analytics Text Analytics provides capabilities such as sentiment analysis, key phrase extraction, language detection, and named entity recognition (NER). Sentiment analysis determines if input text has positive, negative, or neutral sentiment. Key phrase extraction identifies key nouns and topics within a text corpus. Language detection detects the language of a given text, and NER identifies and categorizes entities within the text.

OCR and Computer Vision OCR (Optical Character Recognition) allows you to extract printed or handwritten text from images. Computer Vision enables the analysis of image and video content.

Form Recognizer Form Recognizer is a specialized OCR service that can analyze receipts to extract structured data. It uses predefined fields to identify and extract key receipt data, such as merchant name, total, subtotal, tax, and items purchased. Form Recognizer supports both custom models for business forms and prebuilt models for receipts, invoices, and IDs.

LUIS (Language Understanding) LUIS allows you to add natural language understanding to applications and devices. It can classify user intent and extract entities from user queries.

QnA Maker QnA Maker helps create a conversational layer over data. It can be used to filter information, power chatbots, and provide responses to common questions.

Azure Bot Service Azure Bot Service is an intelligent bot service that integrates with channels like Teams, Alexa, and more. It provides tools and resources to build, test, and publish bots.

Azure Machine Learning Service Azure Machine Learning Service enables running ML workloads at scale. It offers various capabilities such as notebooks, automated ML, model registry, compute instances and clusters, and responsible ML features.

Custom Vision Custom Vision allows for image classification. It enables the creation of custom models by uploading labeled images and applying tags. Custom Vision supports quick training with less accuracy or advanced training for improved results. Evaluation metrics include precision, recall, and average precision.

The world of AI is full of endless possibilities and exciting advancements. As you continue your journey into the realm of artificial intelligence, remember that learning is a never-ending process. Embrace the challenges and keep exploring the vast potential of AI. With each new concept you grasp and each new skill you acquire, you are empowering yourself to make a difference in the world. So, stay curious, stay determined, and keep pushing the boundaries of what is possible. The future of AI is in your hands, and the possibilities are truly limitless. Happy learning!