Microsoft Azure Artificial Intelligence (AI) - AZ-900 Notes

The foundation of all successful AI is high-quality, findable, secure data!

The FAIR principles are guidelines for ensuring that data is 

1. Findable, 
2. Accessible, 
3. Interoperable, and 
4. Reusable.
   
   I think it should be FAIRS for:

1. Findable, 
2. Accessible, 
3. Interoperable, 
4. Reusable, and
5. Secure.
   
   Tip: If your organisation's data is in a good FAIRS state, AI projects are far more likely to succeed.

1. Artificial Intelligence (AI) 

• AI is attempting to make computers use and behave using human intelligence.  
• Teach PC to do tasks for us - automation.  
• PC predicts using patterns and can act.  And good at looking for anomalies.
• PC uses camera/photos to look for patterns.
• Engage in valuable conversations, use multiple sources of knowledge.

2. Machine Learning (ML)

• Train PC's to see patterns and see patterns, and look for anomalies.
• Example. Predict stock prices by looking at factors that affect stock prices.
• Anomaly Detection - Detects unusual patterns, e.g. CC used in Asia when typically in Europe, but transactions 10 min apart.  Therefore, it is likely to be fraudulent.  Sort rubbish.
• Predictive models by finding relationships.  Give model data and train the model.  
• Example: flowers have features/characteristics, e.g. colour, size, no petals, ...
• Using data to teach a machine
• Supervise ML - need quality data, including labels.  Avg humidity, hrs sunshine, rainfall, temp, month of year (features), ice creams sold(label/class), so we feed in temp is Regression ML.  Patient has features (weight, sex, age, bmi,...) give value btwn 0-1 of the person developing diabetes.  is Classification ML
• Unsupervised ML - data is not labelled.  Just the feature provided will group into clusters.  Pulls data out and figures out its own criteria is Clustering ML.  Useful for fraud detection.
• Training - good data based on a training set and a validation set.  Train model, with most data, then check with the remaining - allows us to see how close to what happened.  Service tries to figure out relationships.  Model is used by test data - see how close/useful the model is.

3. Compute Vision 

• Self-driving cars, sorting. Sort rubbish.
• Facial recognition, object recognition,..
• How do computers see?  The picture is cut up into pixels, data is pulled and used to find possible answers. 
• Some types on Azure:  object detection, i.e. car, bike, bus.  Image classification, i.e., horse, car.  Semantic segmentation, i.e. Teams blur background.   Image analysis context by bringing various tougher.  Face detection.  OCR - reads the image and converts it to text.

4. Natural Language Processing (NLP) 

•     interpret, e.g Grammarly, Spam Check, Alexia, 
•     Knowledge Mining - Extract info from knowledge and gain insights, e.g. social media marketing.

Principals:

1. Fairness - ensure bias is excluded, e.g., based on gender.
2. Reliable & Safety - need high confidence and in specific systems cannot fail, e.g. health systems, autonomous cars.
3. Privacy & Security - Ensure data is protected and not giving away sensitive data.
4. Inclusiveness - should be fair, i.e. VI users
5. Transparency - what is the model based on, what could be an issue?
6. Accountability - who is liable for AI decisions

Azure:

1. Scalability & Reliability
2. AI Resources: sit in an Azure Resource Group

AI Services in Azure:

1. Azure Machine Learning - Developers to train, test and deploy ML models.  Within a subscription, create a Azure ML Workspace (consists of: compute, data, jobs, models) can then publish as a service.  Azure ML Designer, used for creating an ML pipeline, data to train the model.  Automated Machine Learning user only needs to provide the data and select the model to use; the service figures it out. 
2. Cognitive Services - vision, speak, language, decision.  REST API endpoints have already been trained; choose the model.  Can deploy multiple parts individually or together.

    3. Azure Bot Service - developing and managing intelligent bots like chatbots
    4. Azure Cognitive Search - Data extraction & enrichment for indexing.  Makes data searchable.

Anomaly detector resource - wizard to set up - Add Keys and endpoints to allow access.

Create a new Azure Machine Learning Service, which will create a Workspace.  Use multiple Azure services such as Key Vault, AI, and storage accounts.   

• Launch Studio
• Add Compute Cluster
• Add Data (csv, spreadsheets, nearly any form,...)
• AutomateML (figure it out without me) and run the job
• Will show trends
• Deploy the model (i.e. to a web service)
• Shows "Endpoints" - get URL and a test rig.