Artificial Intelligence (AI)

Training Objectives

• How to define an AI & data strategy assessing people, process, and technologies in order to build an AI-driven organization and to produce trusted data as-a-service in a distributed environment of multiple data stores and data sources?
• How to build a data architecture and AI business use cases
• Measuring the ROI from AI
• How data standardisation and business glossaries can help define the data to make sure it is understood .
• Understand the role of data producers and data consumers in an AI-driven environment.
• Technologies and implementation methodologies need to get your data under control
• Building your first AI Project and Best practices.
• Understanding relevant AI algorithms used in the telecoms industry
• Understanding data quality challenges, dimensions, and metrics.

Module Outlines

Module 1: Introduction to AI?

• Introduction to Machine Learning, Deep Learning and Reinforcement Learning
• What is the difference between data science, AI, and machine learning?
• The Impact of AI on jobs worldwide.
• Evolution of Human Experience and behavior with AI
• What it takes to become a data-driven AI company?

We’ll introduce the best practices of choosing among algorithms and techniques like: Descriptive statistics, feature selection, PCA, hypothesis testing, missingness, imputation & KNN, simple linear regression, multiple linear regression, generalized linear models, decision trees, random forests, support vector machines, neural networks.

Module 2: AI Strategy & Planning

This module introduces the needs for business-driven AI strategy together while working on their data lakes. It looks at the components of your AI strategy, the methodology to be taken and setting a maturity assessment to become AI-driven. It also looks at the big picture of having full interoperability at organizations which include policies and processes needed to bring your data under control.

• Maturity Assessment to become AI-driven.
• Introducing the Extended Data Architecture
• The siloed approach to managing and governing data
• Dealing with new data sources, cloud data, sensor data, social media data, IoT
• Defining an enterprise AI strategy
• Reviewing the vital components for an end-to-end extended big data architecture.
• Introducing the data lake and data refinery
• Differing Data lake configurations
• Data Discovery – Internal & External sources.
• Information catalog & Metadata
• Integrating a data lake into your enterprise data architecture
• Stakeholder matrix and gap analysis
• What is a data refinery?

Methodology & Tools Selection (Vendor Agnostic)

• Data lake use cases
• The role of data management technology platforms, in managing data across multiple data stores
• Technology components in the new world of big data & AI
• AI templates and applications
• How to select the right tools in your data architecture.
• Technology Selection: HDFS
• HBase
• Kudu
• Relational Database Management Systems
• Spark, including streaming, SparkSQL and SparkML
• Hive
• Impala
• Nifi
• Data Sets and Formats

Module 3: How to start an AI project?

• What are the key steps of an AI project?
• Difference between Machine learning project and Data science project initiation.
• How to choose an AI project? (The business and the technical process)
• Introducing the AI canvas for prototyping ideas using design thinking.
• AI skill scoping session to assess skills inside the organization and working in tandem to train teammates.
• AI projects pitfalls
• How to select the right data and how to train and test it for your AI project.
• Why AI sandboxes are not enough to build AI algorithms?

Module 4: Data Culture

This module supports efforts to develop and support data-socialisation skills within groups. This module aims to answer the question: How do we build an AI-friendly culture? This includes the questions:

• Why does data culture matter?
• How can we start an AI Task Force?
• How can build AI skills within groups?
• What does AI mean to each one of us?
• Data Modeling and understanding our core vocabulary for communication.

Module 5: AI: Privacy & Security

How can we build AI use cases while ensuring privacy? How can we build algorithms without accessing data from source systems?

• Designing a Federated Learning framework to build privacy-by-design Machine Learning Apps.
• Data Virtualization and its role in integrating secure data from myriad sources.

Module 6: AI Analytical thinking

We notice that logical thinking is the core of many skills that are required in AI and Data science fields, such as: critical thinking, problem solving and analytical ability. However, issues like cognitive bias, models thinking and rapid learning skills hinders the growth of these skills. In this module, we’ll introduce interactive sessions to help attendees understand the mindset of a modern data scientist.

Module 7: Building an AI Use Case

• Predicting Churn using AI algorithms like graph analytics.
• Telecoms Data Monetization by mashing up foot traffic data etc..
• Building a credit scoring system using AI.
• 360 Degree customer experience view including satisfaction and experience.
• Social influencer analytics.
• Customer experience journey analytics.
• Local sales steering and route planning.

Customized Learning

Leap To Success is offering a variety of learning options to meet current realities and can be adapted to suit your business needs. These options include variants of online, blended and on-site course formats.

Face To Face Learning

Enabling you to have a face to face interactive and engaging learning experiences led by renowned industry experts and thought leaders with extensive practical experience who will employ a variety of interactive learning techniques, including short high-impact videos, case studies, assessments, role plays, in addition to on-going support.

Virtual Learning Labs

Interactive online learning held in real-time using Zoom and are led by international subject matter experts who incorporate case studies, breakout rooms, guided practice, simulations and discussions to maximise your learning experience.

General Methodology

Similar to any L2S training program, this program offers an interactive learning experience in which will allow the delegates to reflect on their learning through an informative and indulging at different training stages. In Addition to that, this course is technical by nature. Therefore, to ensure the assimilation of the techniques introduced, the training will be given in a computer lab, providing each attendee with a PC. The training will also contain a practical part that puts each training in practical situations, engaging them in day-to-day examples and case studies.

Specific Methodology

To effectively execute this program and to ensure that the end result is being achieved L2S specific training methodology in Sales Artificial Intelligence (AI) is as explained in the below stages:

Stage 1: Designing the Training Event

During this stage L2S will be assessing the learning needs from the determined objectives. Simultaneously consider practicality. In addition L2S will consider the group’s learning style in order to identify which style of learning is most suitable.

Stage 2: During the training

This course is designed for the full involvement of every participant through the use of:

• The practice and the concept knowledge of AI technologies
• Skills practice sessions
• Group discussions
• Personal reflection
• Team-work on case studies
• Participant presentations