Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

Sunday 11 February 2024

Basics to setting up an AI Transformation Program - High level

Overview: Many large organisations have started on their Artificial Intelligence (AI) journeys, many have started in weird directions and some are taking the wait and see approach.  As a general rule, I feel most organisations should try identify the most important use cases for AI and using a basic scoring system take the easy high values use cases first. 

A good option is to get a AI focused team to put the AI program into your business.  Get people seconded as they have key domain knowledge and couple with IT experts pref with strong AI skills.  

1. AI Idea Generation: Collate a detailed list of possible ideas.  I like to use SharePoint, it's a good idea to open ideas up to the business or make gamification of idea generation to get a good set of ideas.  You;ll get lots of overlap, distill into unique ideas, and get the team/stakeholders together.

2. Business Impact: how does this help our customers or generate revenue or does this improve our operations.  Can this be quantified?

3. Technological Feasibility: Can technology meet the requirement, and at what cost?  High level technical design is strongly advised as show below in figure 2.

4. Implement: If the idea holds up to Business impact, and is possible, select the highest value to lowest effort ideas.  It is a good idea to start with the simpler ideas to get the ball rolling.

Figure 2. HLD example

Note:  This post is looking at building AI to improve your business, AI also affects all internal staff, so train them.  Allow them to use AI tools like ChatGPT, and train them not to expose internal propitiatory information.  Give your users the correct tools to be the most effective.

Wednesday 15 November 2023

Ignite 2023 - Keynotes - Summary

15 & 16 November 2023

Good Keynote: AI is driving a lot of innovation.

Microsoft Fabric in GA (25k instances already).  New feature is 'Mirroring' - copy cosmos/SQL et al into Fabric.  OneLake.  Can bring lots of data from multiple sources into Fabric in near rela time.

MS Teams (320 million users):  Bring everything to the user in one place, not just communications but a canvas for apps.  Good place to build line of business applications.  New teams app - way faster, easier to use.  Teams Premium - intelligent meeting recap is working well, can integrate recap with copilot. 

Copilots - needed for nearly everything you do.  understand context of where you are.  MS has hundreds of copilots.

Copilot Studio - Custom GPT's, can add plugin's to add your own data, can hook into an enterprises unique data.

Copilot for Service - allows agents to get information to provide support, looks interesting.

Personal thoughts: AI is going to be a mega trend that will influence the world hugely, there will be lots of weird decisions on the journey. Currently, it is mainly proving useful as another tool to help improve existing processes.  AI helps me work faster and spend my time on exploration rather than bring base understanding together.

Part 2 Keynote:

Microsoft Graph gives the copilot context within an organisation.  Use plugins to add enterprise data or Open AI GPT's.

Surface Pro Hub 3 released  - looks good, rest of the hardware looks higher spec.

SharePoint Premium - improved knowledge and content management on SharePoint.

Copilot Studio - useful to build internal copilots.  1. Connect copilot to other systems using plugins or GPTs 2. create workflows 3.  Controlled by IT.

Copilot Studio overview

Mesh - Teams can join immersive experience/events, not sure what this means.  GA expected Jan 2024.

Microsoft 365 copilot release to GA 1 November. 

Why Copilot? MS are describing it as a productivity multiplier.  Allows users to be more productive and more creative.  Improves quality of work, avoids searching - as expected.  Makes mistakes but is getting better.

Microsoft Copilot - Bing chat is just MS copilot

So when logging use Entra Id (Azure AD), get contextual enterprise information.  Inherits security and privacy policies.  ACL controlled.  Includes MS graph and Apps.

Try it out: Copilot.microsoft.com  - Chat data is not saved/stored by MS.  Change from "web" to "work".  Also available in Windows taskbar

Ability to use copilot to pull in information, show more graphs, get data.  Good word example getting data from a pptx.

Great example of querying Excel using copilot, created a pivot table. Contrived but looks good, added rainfall from web to look at sales.  Powerful.

Never thought of copilots for being a participant in a meeting - might us amazing.  In teams meeting, takes real time notes, and pulls in info and summaries points for next meeting.  Add as a collaborative partner, can visualize discussions on a meeting whiteboard.

Loop - flexible collaboration, now with copilot.  Not my area but sounds impressive - i don't get it.  People, working with people, now also working with copilot.  Okay.

Copilot for Sales - looks promising.  Hooks into existing CRMs.

Copilot for Service - working with customers, get data that is correct to solve customers problems.  Concise summary, helps craft emails, updates CRM.  Looks very interesting!

Viva - Microsoft copilot dashboard powered by Viva - not sure on this topic.

Summary: People using copilot don't want to loose it.  AI is bringing big changes to many industries.  Promise is to take the grind out of work - sounds great let's see.  Copilot/AI will be a tool and shape how we work.

Thursday 14 September 2023

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

1. Artificial Intelligence (AI) 

  • AI making PC behave like human intelligence.  
  • Teach PC to do task for us.  
  • PC predicts using patterns and can act.  And good at looking for anomalies.
  • PC uses camera/photos to look for patterns.
  • Engage in useful 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 price.
  • Anomaly Detection - Detects unusual patterns e.g. CC used in Asia when normally in Europe, but transactions 10 min apart.  Therefore 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 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 feature provided, will group into clusters.  Pulls data out and figures out it's 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 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 model is.

3. Compute Vision 

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

4. Natural Language Processing (NLP) 

  •     interpret e.g grammerly, 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 certain 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 ML pipeline, data in to train model.  Automated Machine Learning user only needs to provide the data and select the model to use, 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 - develop & managing intelligent bots like chat-bots
    4. Azure Cognitive Search - Data extraction, & enrichment for indexing.  Makes data searchable.

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

Create a new Azure Machine Learning Service, will create a Workspace.  Use multiple azure services such as key-vault, AI, storage accounts.   
  • Launch Studio
  • Add Compute Cluster
  • Add Data (csv, spreadsheets, nearly any form,...)
  • AutomateML (figure it out without me) and run job
  • Will show trends
  • Deploy the model (i.e. to a web service)
  • Shows "Endpoints" - get url and a test rig.

Friday 26 May 2023

An brief introduction with two demos on OpenAI

OpenAI has a couple of service such as ChatGPT, and DALL-E.  The recording below, shows two a demos: 

  • ChatGPT to gain insight, and
  •  DALL-E to generate some artwork.

https://youtu.be/TdGjp171wAk - 3min 48 seconds

There are other suppliers of large scale AI engines such as Googles PaLM 2.

Updated Dec 2023: 

I created an Azure Open AI Service instance.  Very easy to access using the API's and nice to play with.  There is also the playground, and I generated 8 images of a train emerging from a tunnel in eight different artistic styles.  The hardest part of AI and DALL-E is framing the Prompt/Question.

OpenAI Studio on Azure AI using the DALL-E playground.

Sunday 14 May 2023

Dynatrace Product Play

 Dynatrace is pretty similar to Azure Monitor.

  • Dynatrace (really good if you use multi cloud) Dynatrace - Saas offering is on AWS.  Can be on-prem.  
  • Making workloads observable is using Logs, Traces, Events, and metrics into Dynatrace.  From these ingested events we analyse and can automate behavior. 
  • OneAgent is deployed on the Compute i.e. VM, Kubernetes.  Can import logs from other SIEMs or Azure Monitor, so you can eventually get Azure service logs such as App Service or Service Bus.  
  • Does Full stack and includes code-level and applications and infrastructure monitoring, also can show User monitoring.  
  • Dynatrace offers scalable API's that are sitting on Kubernetes.  
  • "Davis" is the AI engine used to help figure out the problems.  
  • Alerting is solid.  
  • Dynatrace can log against 1) network/Infra 2) SDK 3) DEM (User monitoring,..)  logs, traces, metrics are ingested either using OneAgent or OpenTelemetry.
  • Management Zones - user only see's information they have access to and need.
  • Define a Site Reliability Guardian (SRG) to each program/project, this allows you to identify thru RAG boards the current and recent state of the various pieces.  There are Guardian templates to use as a starting point.
  • W3C Trace Context is used - it allows for end-to-end tracing.  OpenTelemetry or Dynatrace keep the trace and provide in headers (traceparent.
  • Create documentation and tutorials for Dynatrace.  Dynatrace has a playground tenant for playing on.
High-level Architecture hosted on AWS.

High-level architecture for capturing logs et al. and then using the data.

Product Screen Shots:






Azure & Dynatrace
  • Abnormality detection using AI. shall greatly improve observability and security. 
  • End-to-end visibility is what makes it so amazing.
  • Enterprises often use Dynatrace as there central SIEM solution, shipping from Azure in Dynamics takes planning but works well, categorise and ensure the right into is pushed into Dynatrace.  
  • Dynatrace is the leader in Gartner and Forrester in it's space.
  • Grail - lake house, schema-less, allows for easy fast query.  Massive scale.  Bring all data together and query at hyperscale.  Grail is in 15 regions either on AWS, Azure, or GCP for customers to use.  UK looks like AWS only. 
  • Grail: Record level protection, masking data, support access controls (elevate privileges).  
Dynatrace architecture for Grail from Barcelona conference 5 Oct 2023.

Collect all events in Grail, automate the process of identify suspicious activity relating to security.  Faster reaction time.

Azure offers Dynatrace as a SaaS service
Updated 16 Feb 20224