Applied AI & Machine Learning | Design

COURSE OUTLINE:

Description

The developers of this course have been actively involved with the design, implementation and deployment of real-world and predictive modeling solutions in large organizations for decades. Their clients have been richly rewarded by identifying and prioritizing valid projects ahead of development and deriving real value from successful deployments.

There simply is no other vendor-neutral event in the marketplace that focuses exclusively on analytic project assessment, planning and design � let alone integrating seamlessly into an overarching series for end-to-end process implementation. The skills taught in this course are rare and highly valued in the market.

Audience

  • IT executives and big data directors: CIOs, CAOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers who desire to establish a confident vision, strategic plan and realistic expectations for the development of a value-focused AI-driven operation to arrive at measurable, accountable and automated decisioning
  • Line-of-business executives and functional managers: Risk Managers, CRM Managers, Public Sector Directors, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Medical Diagnostic Analysts, Fraud and Loss Prevention Managers, eCommerce Company Executives
  • Data scientists: Who recognize the importance of complementing their tactical proficiency with a strategic planning and design approach to AI and predictive analytics
  • Technology Planners :Who survey emerging technologies to prioritize corporate investment
  • Consultants: Whose competitive environment is intensifying and whose success requires competency with AI, predictive modeling, machine learning and related emerging information technologies

Prerequisites

Registrants will be required to view a three-hour asynchronous Core Concepts�orientation prior to attending this event. Video blocks of instruction are segmented into consumable segments of just 10 to 15 minutes each. Prior education or experience in data analytics or statistics is helpful, but not required.

Learning Objectives

  • Plan and manage your AI and predictive modeling projects effectively from the start
  • Identify, qualify and prioritize actionable analytic opportunities
  • Understand the purpose, function and impact of an analytic process model
  • Outline the implementation tasks of the Assess and Plan Phases of the Modeling Practice Framework
  • Define a project roadmap with modeling objectives that lead to measurable project gains
  • Qualify the downstream organizational and environmental requirements for model deployment and operation
  • Recognize dead-end approaches in advance that lead to wasted resources on doomed approaches
  • Broaden experience through active problem-solving and guided discussion in realistic project scenarios
  • Engage with confidence among your developers, analysts and consultants
  • Develop the rare analytic leadership traits to design and oversee actionable analytics projects
  • Leave with resources, contacts and plans to substantially reduce your project preparation time, costs and risks

Terms Used in Today�s Analytics Environment

  • Artificial Intelligence
  • Deep Learning
  • Predictive Analytics
  • Data Science
  • Business Intelligence
  • Data Analysis
  • Machine Learning
  • Dashboards
  • Big Data Analytics
  • Applied Statistics
  • Prescriptive Analytics
  • Predictive Modeling
  • Internet of Things ( IoT )
  • The Current Landscape of Analytics Software

Assess Phase

  • Assemble Team
  • Leadership, Analysts, Subject Experts, Data Support, Stakeholders, etc
  • Determine Whether External Talent is Needed
    • Examine Culture & Mindset
    • List Candidate Projects
  • Place Projects on a Benefits / Challenges Quadrant Plot
  • Guided Discussion Breakout Session
    • Define Performance Benchmarks
    • Identify Data Sources
    • Itemize Existing Analytic Resources
    • Describe Operational Environments
    • Initial Report of Overall Practice Readiness
  • What Should an Assess Phase Report Contain?
  • Exercise Breakout Session

Plan Phase

  • Pull & Recon Data
  • Explore Data & Verify Quality>
  • Do We Have Enough Data?
  • Which Data are Relevant?
  • Make a First Look at Data Quality
  • Exercise Breakout Session
    • Design Analytic Sandbox
    • Qualify Team
    • >Qualify Tools
    • Define Operational Environment(s)
    • Establish Performance Benchmarks & Targets
  • What are the current metrics (KPIs)?
  • What is the Role of Technical Metrics vs. KPIs?
  • Benchmark Demonstration
    • Consider Deployment Options
    • Prioritize Viable Projects

Wrap-up and Next Steps

  • Supplementary Materials and Resources
  • Conferences and Communities
  • Get Started on a Project!
  • Options for Implementation Oversight and Collaborative Development