Title: AI-900 Azure AI Fundamentals
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Copyright Microsoft Corporation. All rights reserved.
Artificial Intelligence
# An Introduction to
# Cloud -based AI
Dr Amir Pourabdollah Outline
Cloud and cloud services
Why Cloud -based AI?
Common Workloads and Examples of Cloud -based AI
Challenges and Risks of AI
Principles of Responsible AI
Sources:
Microsoft Azure AI Fundamentals (AI -900)
https://learn.microsoft.com/en -us/credentials/certifications/exams/ai -900/
Microsoft Azure Fundamentals: Describe cloud concepts
https://learn.microsoft.com/en -us/training/paths/microsoft -azure -fundamentals -describe -cloud -concepts/ Microsoft Corporation
> Azure
What is cloud computing?
Compute Storage Networking Analytics
Cloud Computing is the delivery of computing services over the internet,
enabling faster innovation, flexible resources, and economies of scale. Copyright Microsoft Corporation. All rights reserved.
Cloud Benefits
High availability
Scalability
Global reach
Agility
Disaster recovery
Fault tolerance
Elasticity
Customer latency capabilities
Predictive cost considerations
Security Copyright Microsoft Corporation. All rights reserved.
Cloud Challenges
Connectivity
Data Privacy
Black -boxed Solution
Costs
(if not proportional to the service)
Data Security Concerns
QoS, Technology familiarity, Copyright Microsoft Corporation. All rights reserved.
Public cloud
Owned by cloud services or hosting
provider.
Provides resources and services to
multiple organizations and users.
Accessed via secure network
connection (typically over the
internet). Copyright Microsoft Corporation. All rights reserved.
Private cloud
Organizations create a cloud
environment in their datacenter.
Organization is responsible for
operating the services they provide.
Does not provide access to users
outside of the organization. Microsoft Corporation
> Azure
Hybrid cloud
Combines Public and Private clouds to allow applications to run in the
most appropriate location. Copyright Microsoft Corporation. All rights reserved.
Infrastructure as a Service (IaaS)
Build pay -as -you -go IT infrastructure by renting servers, virtual machines, storage,
networks, and operating systems from a cloud provider. Copyright Microsoft Corporation. All rights reserved.
Platform as a Service (PaaS)
Provides environment for building, testing, and deploying software applications; without
focusing on managing underlying infrastructure. Microsoft Corporation
> Azure
Software as a Service (SaaS)
Users connect to and use cloud -based apps over the internet: for example, Microsoft Office 365, email,
and calendars. Microsoft Corporation
> Azure
Why Cloud -based AI
All benefits of cloud technology, plus:
Merging of ML/AI with cloud -based computing environments
Opportunities for sharing knowledge and resources
Offloading the computation power from client -side devices
The ultimate solution with growing IoT, embedded AI,
Do not reinvent the wheel! Microsoft Corporation
> Azure
Common Cloud Service Providers for AI
Many comparison reviews, e.g., https://www.datamation.com/artificial -
intelligence/the -top -cloud -based -ai -services/ Machine Learning Predictive models based on data and statistics
the foundation for AI
Anomaly Detection Systems that detect unusual patterns or events,
enabling pre -emptive action
Computer Vision Applications that interpret visual input from
cameras, images, or videos
Natural Language Processing Applications that can interpret written or
spoken language, and engage in dialogs with
human users
Knowledge Mining Extract information from data sources to create
a searchable knowledge store
Generative AI Create contents using large trained/pre -trained
models
## Common Workloads of Cloud -based AI Examples of Cloud -based Computer Vision Services
Taxi
Image Classification
bus
car
Cyclist
Cyclist
bus
Object Detection
> bus
> car
> cyclist
Semantic Segmentation
A person with a dog on a street
Image Analysis Face Detection & Recognition
The Toronto Dominion Bank
Optical Character Recognition Example Cloud Service: Anomaly Detection
>
Anomaly detection is the act of identifying events, or observations, that differ in
a significant way from the rest of the data being evaluated. Anomalies can be
detected by learning from the data patterns. Accurate anomaly detection leads
to prompt troubleshooting.
>
The Anomaly Detector service can be used to create a cloud -based monitoring
application Machine Learning on the Cloud
Traditional machine learning model development is resource -intensive, requiring
significant domain knowledge and time to produce and compare dozens of models.
Using the cloud resources for computing -intensive tasks
- Training models
- Model optimisation
- Running models
Different approaches
- Use pre -trained model
- Visual interfaces for model training and deployment
- Server -side programming
- Hybrid (a combination of the above)
> Data Model
Example: Azure ML (reading only)
Repeating experiments
allows choosing the best
model to fit data
according to a chosen
performance metric
(supervised learning) Example: Azure ML cont. (reading only)
Once the model is deployed, an
endpoint and a key is allocated to
the deployed model.
These can be used (or shared with the
users) to be used within client -side
programs
Predictions can be the results of
repeated call to the deployed cloud
service (without using the local
resources) Example: Pipelining in Azure ML (reading only)
Visual tool for creating a
machine learning
pipelines
> Many pre -programmed modules
> + support for user -programmed
> modules
Used for supervised and
unsupervised methods
Use a training pipeline to
train and evaluate a model
Create an inference
pipeline to predict labels
from new data (not shown)
Deploy and publish the
inference pipeline as a
service for apps to use. NLP Capabilities on the Cloud
Language
Language detection
Key phrase extraction
Entity detection
Sentiment analysis
Question answering
Conversational language understanding
Speech
Text to speech
Speech to text
Speech translation
Translator Text translation
Bot Service Platform for conversational AI Example: Developing a Cloud -based Chatbot (reading only)
Define a
knowledge base of question and answer pairs:
By associating answers to [alternative] questions
From an existing FAQ document
By using built -in chit -chat
(or a combination of the above)
Train a model (if needed for answering unseen questions)
Deploy as a Cloud service
Consume the knowledge base from client apps, connect to
the required channels Lab this week
To use Azure AI service for image/ object recognition Challenges and Risks with AI
Challenge or Risk Example
Bias can affect results A loan -approval model discriminates by gender due to
bias in the data with which it was trained
Errors may cause harm An autonomous vehicle experiences a system failure
and causes a collision
Data could be exposed A medical diagnostic bot is trained using sensitive
patient data, which is stored insecurely
Solutions may not work for everyone A predictive app provides no audio output for visually
impaired users
Users must trust a complex system An AI -based financial tool makes investment
recommendations - what are they based on?
Who's liable for AI -driven decisions? An innocent person is convicted of a crime based on
evidence from facial recognition who's responsible? Principles of Responsible AI
Fairness Reliability & Safety Privacy & Security
Inclusiveness Transparency Accountability
> See resources and examples: https://www.microsoft.com/en -us/ai/principles -and -approach#resources
## Fairness
AI systems should treat all people fairly.
For example, suppose you create a machine learning model to
support a loan approval application for a bank.
The model should make predictions of whether or not the loan
should be approved without incorporating any bias based on
gender, ethnicity, or other factors that might result in an unfair
advantage or disadvantage to specific groups of applicants. Reliability and Safety
AI systems should perform reliably and safely.
For example, consider a software system for an autonomous
vehicle; or a machine learning model that diagnoses patient
symptoms and recommends prescriptions.
Unreliability in these kinds of system can result in substantial
risk to human life.
Application development must be subjected to rigorous test
and deployment management processes to ensure that they
work as expected before release. Privacy and Security
AI systems should be secure and respect privacy.
The machine learning models on which AI systems are based rely
on large volumes of data, which may contain personal details that
must be kept private.
Even after the models are trained and the system is in production,
it uses new data to make predictions or take action that may be s
ubject to privacy or security concerns. Inclusiveness
AI systems should empower everyone and engage people.
AI should bring benefits to all parts of society, regardless of physic
al ability, gender, sexual orientation, ethnicity, or other factors. Transparency
AI systems should be understandable.
Users should be made fully aware of the purpose of the system, h
ow it works, and what limitations may be expected. Accountability
People should be accountable for AI systems.
Designers and developers should work within a framework of
governance and organizational principles that ensure the solution
meets ethical and legal standards that are clearly defined.