Implementing Enterprise AI – Online workshop

Implementing Enterprise AI – Online workshop

By

Ajit Jaokar and Cheuk Ting Ho

 

Early bird discounted rate $599 USD

 

 

Introduction

Launched for the first time, jointly delivered by Ajit Jaokar and Cheuk Ting Ho – Implementing Enterprise AI workshop is an online workshop targeting developers and strategists.

 The workshop enables you to develop a personal strategic case study for implementing Enterprise AI.

Some knowledge of Python is good, but it is not mandatory.

The workshop focuses on the professional deployment of AI in the Enterprise along with the underlying business case

The professional deployment of AI in Enterprises differs from the content in a typical training course. In larger organisations, the Data Science function typically spans three distinct roles: The Data Engineer, the Data Scientist and the DevOps Engineer. The Data Scientist is primarily responsible for developing the Machine Learning and Deep Learning algorithms. The Data Engineer and The DevOps Engineer roles work in conjunction with the Data Scientist to manage the product/service lifecycle.

The workshop is based on the following considerations

a)      Emphasis on the full AI pipeline

b)      Understanding the business case for Enterprise AI

c)      Understanding the practical implementation considerations for Enterprise AI

d)      Adopting a pragmatic approach to balance against the media hype

e)      Developing a case study as part of the workshop

f)       Adopting a Use Case approach

 

Modules

Module One: Concepts

In this module, we cover the following:

  • What is Enterprise AI
  • Why use AI in enterprises
  • Industry landscape of enterprise AI
  • Understanding different issues and strategies in Enterprise AI
  • Architectural design considerations for AI in enterprises
  • Understand what problems Enterprise AI solves
  • Understand the issues behind deploying Enterprise AI applications in scale for Enterprises
  • Study the strategies of large scale Enterprise AI vendors (Azure, Amazon and Google)
  • Use cases

 

Module Two: AI models used in the Enterprise

In this section, we study AI / Deep learning algorithms. These include:

  • Overview of Machine learning and Deep Learning
  • Deep-learning techniques. MLP, CNN, RNN, Autoencoders, Variational Autoencoders, GANs

 

Module Three: Enterprise AI Business case

Broadly, we can consider an AI process as one which improves with experience

The business case for Enterprise AI is a moving goalpost. It is driven by a number of considerations which we cover in this module

 

Module Four: Unique considerations for Enterprise AI

Here, we cover specific drivers for Enterprise AI

  • Explainable AI
  • Automatic Machine Learning
  • New regulatory structures responsible for AI ex GDPR, Payment regulation etc

 

Module Five: AI and the Cloud – what does it mean

In this module, we look at the issues of deploying AI models in the Cloud including

  • Accessibility to large amount of compute in the cloud
  • Pay-as-you-go – leverage GPU machines when you need them without upfront costs
  • Containerisation of models means ability to train and package in the cloud but deploy anywhere
  • Model management for version control and fresh relevant models when needed
  • Inference and devices
  • Access to services in the streaming, big data and ML space that can help you build complex architectures without worrying about the underlying infrastructure

 

Module Six: Code walkthrough – Credit approval

Code walkthrough with an end to end example for Enterprise AI (Credit approval)

 

Module Seven: Code walkthrough – NLP

Code walkthrough with an end to end example for Enterprise AI (Natural Language Processing)

 

Module Eight: AI – Spark and Streaming

An understanding of the Streaming architecture including Kafka, Spark (PySpark)and MongoDB for large scale deployment of AI in Enterprises

 

Module Nine: End to End Pipeline for AI with CICD

In this module, we explore the real world / large scale deployment of AI including Continuous Innovation and Continuous Delivery(CICD). DevOps creates a culture of increased collaboration, ownership and accountability enabling teams to create large-scale applications. CICD (Continuous Improvement/Continuous Delivery). CI/CD can be seen as an evolution of Waterfall and Agile methodologies.

 

Module Ten: Case study exercise review

Review of class exercise and case study (online and in group)

 

Questions and contact details

  • The workshop is delivered through pre-recorded videos which you can view at any time
  • Code is used to demonstrate the application as a walkthrough. This is not a coding/hands-on workshop and the code is for illustrative purposes (and not supported in the workshop). You will have access to the code in Apache v2 license on an as-is basis.
  • Exercise Case study – developed in the group based on the material. You can choose the theme for the case study related to Enterprise AI covering the material in the course. Note that the case study does not cover the code.
  • Duration – you have up to three months to complete the case study exercise in the group. Modules will be posted once a week.
  • The course is industry led. It is not affiliated with an academic institution
  • The course includes a certificate of completion
  • Please contact ajit.jaokar at futuretext.com for signup and any other questions

 

 

Tutors

 

Based in London, Ajit’s work spans research, entrepreneurship and academia relating to Artificial Intelligence (AI) and Internet of Things (IoT).  Ajit works as a Data Scientist (Bioinformatics and IoT domains). He is the course director at Oxford University on “Data Science for Internet of Things”. Besides Oxford University, Ajit has also conducted AI courses in LSE, UPM and part of the Harvard Kennedy Future society research on AI.

Cheuk Ting Ho is a Data Scientist in Machine Learning and Deep Learning. She contributes regularly to the Data Science community by being public speaker, encouraging women in technology, and actively contributes to Python open source projects.