AI-900 Azure Machine Learning

 

Artificial Intelligence : Found in 1956

The Field of computer science that seeks to create intelligent machines that can replicate or exceed human intelligence.

                                

Predicting outcomes, recognizing patterns based on historic data.

                                

Recognizing abnormal events and making decisions

Interpreting visual input.

Understanding language and engaging in conversations.

Extracting information from sources to gain knowledge.

                                

Machine Learning : Found in 1996

Subset of AI that enables machines to learn from existing data and improve upon that data to make decisions or predictions.

Deep Learning : Found in 2012

A machine learning technique in which layers of neural networks are used to process data and make decisions.

Computer Vision : Interpret the world visually through cameras, video and images

NLP (Natural Language Processing) : Interpret written or spoken language and respond appropriately.

Document Intelligence : Deals with document processing and using high volumes of data found in forms and documents.

Knowledge Mining : Extract the information from large volumes of often unstructured data to create a searchable knowledge store.

Generative AI : Found in 2021

Create new written, visual, and auditory content given prompts or existing data.

LLM (Large Language Models) :

Takes text as input and produce text as output.

It is trained on massive amounts of text data from books, websites, and other sources to learn grammar, facts, reasoning, and context.

Works using Tokenization.

SLM (Small Language Models) :

A Small Language Model works on the same concept as an LLM but with fewer parameters and a smaller training dataset.

While it’s less powerful, it’s faster, more cost-efficient, and can run on local devices like laptops or smartphones.

Computer Vision :

What is Computer Vision in AI?

Computer vision is a field of artificial intelligence that enables machines to see, understand, and interpret visual information from the world around them, just like humans do. It works by training AI models on large sets of labeled images. This training helps the system recognize patterns, objects, and scenes automatically.

Through computer vision, machines can identify what an image contains. They can also locate, classify, and analyze the objects within it.

How Computer Vision Works

Image Classification: The model learns to recognize the main subject in an image (e.g., “cat,” “car,” or “tree”).

Object Detection: The AI detects specific objects within an image and marks their positions.

Semantic Segmentation: This technique identifies each pixel belonging to a specific object, providing detailed visual understanding.

Multi-Modal Models: By combining computer vision with language models (like GPT), AI systems can analyze images and describe them in natural language. This bridges the gap between vision and text understanding.

Real-World Applications

Computer vision powers many technologies we use every day, including:

- Auto-captioning and tag generation for photos

- Visual search tools

- Retail automation (stock monitoring, item recognition)

- Security and surveillance systems

- Facial recognition for authentication

- Robotics and self-driving vehicles

In short:

Computer vision allows AI to make sense of the visual world by identifying, describing, and interacting with what it “sees.” This forms the foundation of modern visual intelligence systems.

What is Speech Technology in AI?

Speech Technology is a part of artificial intelligence that helps machines hear, understand, and generate human speech. It connects human language with computer systems, allowing people to interact with technology in a natural way—simply by speaking.

There are two main components that make this possible:

Key Components of AI Speech

Speech Recognition (Speech-to-Text):

This lets AI systems listen to and interpret spoken words by turning them into written text. It’s how digital assistants understand voice commands and how meetings are automatically transcribed.

Speech Synthesis (Text-to-Speech):

This allows AI to respond by converting written text into realistic spoken words, often using natural-sounding voices.

AI speech systems are becoming more capable. They can filter background noise, recognize accents, detect interruptions, and produce tones and emotions that sound more human-like.

Real-World Applications

AI speech technology is used in many everyday tools and services, such as:

Voice assistants on smartphones, computers, and smart home devices

Automated transcription for calls, lectures, or meetings

Audio descriptions for videos or written content

Real-time language translation through speech

In Short

AI speech technology gives computers a voice and ears, enabling natural, spoken interaction between humans and machines.

What is Natural Language Processing (NLP) in AI?

Natural Language Processing (NLP) is an important area of artificial intelligence that allows computers to understand, interpret, and generate human language in a meaningful way. It helps machines make sense of text, whether that’s analyzing a document, classifying information, or responding in a conversation.

While many modern NLP tasks rely on Generative AI models, simpler and more focused NLP models are still cost-effective and efficient for common text analytics use cases.

Key NLP Tasks

- Entity Extraction: Identifies key elements like people, places, organizations, or products mentioned in text.

- Text Classification: Categorizes documents or messages into predefined topics or groups.

- Sentiment Analysis: Determines the emotional tone of text, whether it is positive, negative, or neutral.

- Language Detection: Recognizes which language a text is written in.

Note: NLP is often called Natural Language Understanding (NLU) when the focus is on extracting meaning and intent from human language.

Real-World Applications

NLP plays a vital role in many industries and services, such as:

- Analyzing documents, transcripts, or customer interactions to extract useful information

- Monitoring social media, reviews, and feedback to gauge sentiment and public opinion

- Powering chatbots and virtual assistants for automated customer support

- Classifying and organizing large volumes of unstructured text data

In Short

Natural Language Processing gives AI the ability to read and understand language. It transforms written or spoken words into actionable insights and intelligent responses.

Using AI to Extract Data and Insights

AI-powered data and insight extraction uses artificial intelligence to automatically identify, read, and interpret information from various sources, such as documents, images, or even audio and video. This technology transforms unstructured data into useful insights that can guide decisions and automate workflows.

Many document analysis solutions rely on Optical Character Recognition (OCR), a type of computer vision that allows AI to detect and extract text from images or scanned documents.

How It Works

Optical Character Recognition (OCR): Detects and converts text from images or scanned pages into editable, machine-readable text.

Field Extraction: Advanced models do more than just recognize text; they identify and pull specific fields or values, such as names, dates, or totals, from forms and structured documents.

Multi-Modal Extraction: Modern AI systems can extract insights not only from text but also from audio recordings, images, and videos, which broadens the range of information analysis.

Real-World Applications

AI-driven data and insight extraction is widely used in many industries for:

- Automating document and form processing, such as invoices and expense claims.

- Large-scale digitization of paper records, including census data or archives.

- Indexing and organizing documents for quick and efficient searches.

- Extracting action points or summaries from meeting transcripts or recordings.

In Short

AI data extraction converts unstructured content from documents, images, or audio into structured, actionable insights that improve efficiency and decision-making.

Tokenization : Text prompts are chunked into tokens which helps model predicting the next token for completion. Models have max token lengths. Models price typically based on tokens used in inputs and outputs.

Tokenization is the process of breaking down text into smaller units called tokens (which can be words, subwords, or even characters). These tokens are then converted into numerical IDs that the AI model understands.

Text: "What is a tokenizer?"
Tokens: 5
Characters: 20
Token IDs: [3923, 374, 264, 47058, 30]




Each number in that array represents a token’s ID in the model’s vocabulary.
So, the model doesn’t read words directly — it reads these token IDs, processes them, and generates new token IDs as output (which are later converted back into text).

Token = smallest text unit AI can process
Tokenizer = converts text ↔ tokens (numbers)

Microsoft Azure Machine Learning :

Step by Step process to create Azure Machine Learning:

Step 1 : Go to Microsoft Azure Portal and sign in using your credentials.

Step 2 : Go to the existing resource group or create new resource group.

Step 3 : Click on Create and Search for Azure Machine Learning and click create.

Step : 4 Fill the details as shown in below given image and rest keep as default. Click Review+Create.

Step : 5 Click on Create. And After your deployment is successful click on go to resource button.

Step : 7 After going to overview page click on launch studio button.

Step : 8 Click on Automate ML from sidebar. And click on Create a new ML job.

Step : 9 Fill the details. and click next.

Select type as regression and click on create button.

Add the name and description and click next.

Select from local files and click next

Select the workspace blobstorage and click next.

Upload the default file and click next.

Do the additional configuration and click save.

Click on enable early termination and select the validation type and click Next.

Click on submit.

Step : 10 It will redirect you to overview page.

Click on voting assemble.

Check the details and metrics.

Step : 11 Click on Deploy and select real time endpoint. 

Click on Submit.

Step : 12 Go to the endpoints section from sidebar.

Check the details and go to the test section. And Click on Test.