Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

In-Person Training
AI for managers

Nijma Khan (Faculty ai), Alberto Favaro (Faculty)
Monday, 29 April & Tuesday, 30 April, 9:00 - 17:00
Strata Business Summit
Location: Capital Suite 16
Secondary topics:  AI and machine learning in the enterprise
Average rating: **...
(2.00, 6 ratings)

Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Nijma Khan and Alberto Favaro offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

What you'll learn, and how you can apply it

  • Explore key AI and machine learning concepts and techniques

Nijma Khan and Alberto Favaro offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn’t) possible with these exciting new tools and how they can benefit your organization. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.

Outline

Introduction to AI

  • Course objectives: AI for everyone
  • Definitions: AI, machine learning (including deep learning, reinforcement learning), data analytics, customer science, etc.
  • Historical context: AI past and present
  • Present-day usage
  • AI within the corporate context
  • Significance and urgency: Why now, and why you?

AI in industry today: Practical applications and benchmark leaders

  • A selection of practical applications
  • Strategic approaches: Commissioning versus procurement (build or buy?)
  • AI maturity models and the enabling preconditions within your organization to use AI well (data, skills, tools, etc.)

Data in your organization: What are your raw materials?

  • What data assets do you have, where does it live, who owns those systems, where are the gaps, and how could you fill them (e.g., by buying data or installing sensors)?
  • What skills do you have access to from your part of the business, and what gaps might there be?
  • What tools do you use, where are the gaps, what other options are there?

Project selection: What makes a good AI use case?

  • Overview of the four key criteria for successful project selection and how to assess them
  • Group exercise: Create a project longlist (often categorized broadly within marketing, operational efficiency, operational effectiveness, and commercial optimization)
  • Scoring longlist against key criteria and plenary discussion

Managing an AI project: How is it is different from other projects?

  • Similarities and differences
  • Project lifecycle and timescales
  • Project delivery methodology
  • Typical staffing profiles
  • Cost estimates for external suppliers (for data science as a service and for consulting)
  • Performance metrics
  • Operations and maintenance
  • Governance and risk management

Leading or supporting a data transformation

  • What is data transformation (as opposed to gradual reform), and when might it be necessary?
  • The ABCs of data transformation
  • Common pitfalls facing successful data transformation initiatives
  • Different operational models for data science within a modern organization
  • Next steps

About your instructors

Photo of Nijma Khan

Nijma Khan is a Principal at faculty. She has fifteen years strategy experience. Over her career she has focused on helping organisations combine commercial, social and environmental value across the retail, consumer goods and telecoms sectors, as well as working with organisations like the World Economic Forum and the United Nations.

Prior to joining faculty, Nijma was responsible for Strategy, Insights and Innovation at Accenture with a particular focus on the impact of automation on learning and work, and the practical application of AI and emerging technologies for good.

Twitter for nijmakhan
Photo of Alberto Favaro

Dr Alberto Favaro is a senior data scientist at faculty. He has led data science projects in the energy, financial services and retail sectors. His areas of expertise include distributed computing for big data, deep learning, and Bayesian statistics. He has extensive experience using TensorFlow, Dask, and MongoDB. He was previously a theoretical physicist, and held research posts in the UK, at Imperial College London, and in Germany, at the Universities of Oldenburg and Cologne. His research was included among the ‘Top 10 breakthroughs of 2011’ by the magazine Physics World.

Twitter for faculty_ai

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)