changed 4 years ago  
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Machine Learning Pipeline

  • Data Ingestion
  • Data Cleaning & Transformation
  • Model Training # Model Selection
  • Testing & Validation # Model Selection
  • Deployment

AI Approach

Start with Business problem and not Data

  • Busines Problem
    • Definition, value, stakeholders, priority, investment
  • Data
    • Right data to solve the business problem
    • Availability
    • Privenance
    • Security
    • Coverage
    • Augmentation
    • Annotation
  • Model Building
    • Feature Extraction
    • Hypetrparameters
    • Tuning
    • Selection
    • Benchmarking
  • Deploy & Measure
    • Business value measurement
    • AB Testing
    • versioning
    • Business Process Integration
  • Active Learning & Tuning
    • Bias mitigation
    • Ground Truth & Success Monitoring
    • Version Control

Project Statement

  • Improve conversion and sales as per the example
  • Clear distinction of data that is required
  • Data specifying similarity of images

Summarised into

  • What problem are we trying to solve?
  • How does AI add value?
  • What data do we nned?

Best Bytes

  • Scoping of the Problem, such as the work context diagram and Use case Modeling
  • How do we measure success?

Example

  • When search result had a product image which was shown by iteself that had white background that converted more than other images
  • This is directly related to what is shown on the search results.

Using AI in Buisness

https://www.gartner.com/en/newsroom/press-releases/2018-12-18-gartner-survey-reveals-two-thirds-of-organizations-in

Reason: 5G edge use cases and 5D deployment to several purpose driven solutions

https://www.fieldservicenews.com/blog/before-data-analytics-think-problem-to-solve

Reason: The use cases deal with historical data and is predominantly static which makes us address the problem first from a selling perspective rather than use data in the first go. Easy things first, devising a USP, we may be speculative and we do not have the evidence and reasoning to address the problem first using data so that requires an investigation.

https://mindmajix.com/bpm-tools

Reason: A BPM is associated with data intensive processes. Management of outcome rather than tasks is the key. Data management and maintenance of data are key aspects that enable success.

Measuring Success

  • Using metrics

  • Metrics must be Easily measurable

  • Directly correlated to business performance

  • Predictive of future business outcomes

  • Isolated to factors controlled by the group

  • Comparable to competitors' metrics

Metrics Quiz

Measuring NPS

NPS is a score that is the difference between promoters and detractors, the passives are not taken into consoderation

https://www.qualtrics.com/uk/experience-management/customer/measure-nps/

https://www.qualtrics.com/uk/experience-management/customer/net-promoter-score

Using Search by ML to introduce Metrics

Do you need AI

  • An Impactful Business Problem

  • Quantify the business value

  • Does it have large volume of associated data?

  • How much data do you have?

  • Does the dataset match the problem?

  • Is the dataset complete?

  • Is the data annotated correctly for the ML Team?

Need for AI example

Production systems actively learn from humans

It is best practice to incorporate real humans into training pipelines

Things to remember

  • Start with business value

  • Use production data and academic data, matche reality and real world deployment

  • Learning is key

Key roles

Product Owner

  • Business case owner
  • Bridges frpom stakeholders to team
  • Owns maximisation of product value
  • Ensures that the team builds the right product

Designer

  • Owns human-computer interaction design
  • Visual design, information architecture, interaction design
  • Useability / accessibility

Software Engineer

  • Builds product infrastructure
  • Problem solver in software deveopment
  • Frontend/backend

Data Engineer

  • Builds data infrastructure
  • gets model into production
  • Ensures entire pipeline can support rapid development
  • Model management

Data Scientist

  • Builds & selects models
  • Guides the team on the state of the art technology
  • Structures the problem to achieve the business metrics
  • Uses data to answer business questions

Quality Assurance

  • Owns quality assurance of the product
  • Ensures product release is ready
  • Scalability testing
  • Functional testing

Development and Operations (DevOps)

  • Ensures infrastructure reliability
  • manages scalability and performance
  • Mitigates security risks
  • Ensures development and ML Team can work efficiently

Project Management

  • Business Problem
  • Data
  • Model Building
  • Deploy & Measure
  • Active Learning & Tuning

Executing an ML Project

Scrum

  • Scrum master
  • Product Owner
  • Team
  • Product backlog / Sprint Planning Meeting / Sprint Backlog
  • Finished Work
  • Sprint review
  • Sprint retrospective
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