AI and Machine Learning: Today’s Implementation Realities

AI and machine learning have returned to the spotlight. This time, they’ve caught the attention of the C-Suite and stakeholders as the leading value-drivers within data science.

With this level of visibility, today’s machine learning needs to do more than uncover interesting insights.  Most organizations focus almost exclusively on algorithms, software, coding, and platforms. Although these tactics are important, they only make up one of three pillars required to run a productive AI operation.


Each day of this three-day workshop focuses on one of the pillars:

Preparing for AI and machine learning action and adoption

AI opportunity identification, project design, and preparation

The methods and mechanics of machine learning


Attend this workshop to learn how to apply a comprehensive implementation framework that not only ensures superior model performance, but prepares the operational environment for automated decision-making and the organizational team for adoption.


Delegates will learn

Identify, qualify, and prioritize viable and actionable AI opportunities

Develop a strategy for applying data-driven decisions aligned with organizational priorities

Evaluate the latest machine learning methods and approaches in view of the project design


Audience

C-Suite executives looking to set a confident vision and realistic goals for AI

Line-of-business leaders ready to move from mere analysis to measurable action

Functional managers seeking a low-risk/high-impact implementation framework

Data scientists wanting to stand out from quants and coders with broader process acumen

BI and IT leaders concerned with deploying and operationalizing models

AI consultants wishing to experience the modern complexities of organizational AI implementation

Innovation planners charged with investigating leading strategies for AI practice development

View a comprehensive implementation framework for running an AI operation

Acquire a balance of tactical and strategic skills required to stand out as a data scientist

Prevent AI project failure and understand why it’s almost never due to technology

Enhance your professional profile with unique translator skills in low supply and high demand


What is AI, Machine learning and Data Science?

What is the Organizational Value of AI & Machine Learning?

How is Data Science Different from AI?

Machine Learning

What are the Skills Needed for Machine Learning?

What Does a Data Scientist Do All Day?


Data Science Core Concepts

Orientation to Big Data

Trends within the analytically competitive organization

The advent of Data Science

What is machine learning’ role in Big Data?

ROI of data science, big data and associated analytics

The future of data science, big data and advanced analytics


How to Think Like A Data Scientist

Stats 101 in ten minutes

A / B testing and experiments

BI vs predictive analytics

IT’s role in predictive analytics

Statistics and machine learning: complementary or competitive?

Primary project types

Common analytic and machine learning algorithms

Popular tools to manage large-scale analytics complexity

Performing a data reconnaissance

Building the analytic sandbox

Preparing train / test / validation data

Defining data sufficiency and scope


The Cao’s Roadmap

The Modeling Practice Framework™

The elements of an organizational analytics assessment

Project Definition: The blueprint for prescriptive analytics

The critical combination: predictive insights & strategy

Establishing a supportive culture for goal-driven analytics

Defining performance metrics to evaluate the decision process

What is the behavior that impacts performance?

Do resources support stated objectives?

Leverage what you already have

Developing and approving the Modeling Plan

Selecting the most strategic option

Planning for deployment

Measuring finalist models against established benchmarks

Preparing a final Rollout Plan

Monitoring model performance for residual benefit


Building The Goal-Centered Analytics Operation

Attracting and hiring the right analytic talent

The roles and functions of the fully-formed analytic project team

Specialization in analytic project teams

Analytic opportunity identification, qualification and prioritization

Organizational resistance and developing a culture for change

Project failure is not the worst outcome

Staging the organizational mind shift to data-driven decisioning

Motivating adoption by domain experts, end users and leadership

Recording ongoing organizational changes

Monitoring and advancing organizational analytic performance

“Democratizing” analytics: Advantages and risks of “self-service”

Standing up an agile analytic modeling factory

Knowledge retention and skill reinforcement

The Future of AI and Advanced Analytics

From Rhetoric to Reality

Biggest Driver of AI and Machine Learning Innovation

What’s Next in Data Science, AI and Machine Learning?

Defining Your Organization’s AI Reality


Program Details
Duration 3 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom


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