Building and leading a successful AI practice for your organization

Machine learning is an exciting field with incredible untapped potential to create value and strengthen the organization’s competitive advantage. Creating and leading a successful ML strategy requires an elegant orchestration of many components: master key ML concepts to make sound decisions that align with the company’s vision and mission, operationalize ML workflow, prioritize highest-value projects, know what to look for in ML talents (and how to develop it), build and motivate high-performing teams, nurture strategic partnerships (internal and external), etc.

Rossella Blatt Vital empowers you with fundamental concepts, key insights, and lessons learned to drive a successful machine learning strategy. You’ll get a primer on machine learning, arming you with an understanding of the key ML concepts relevant to drive a high-value ML roadmap and strategic insights to build and lead a flourishing ML practice.

Outline

A primer on machine learning

• What is and isn’t machine learning: The difference between artificial intelligence and machine learning, how it relates to data science, big data, programming, and traditional statistical methods, and a brief history of AI, current trends, and where it’s going
• What problems AI can solve (an ML approaches overview): The 5 groups (symbolists, Bayesians, connectionists, evolutionarists, and analogizers) and their methods (respectively, decisions trees and random forests, Bayesian inference and Bayesian networks, artificial neural networks and deep learning, and k-NN, support vector machines, and recommender systems)
• Machine learning and statistics pitfalls and how to prevent them

How to develop a successful ML strategy

• Operationalize AI (workflows and processes): The iterative process of building, training, and deploying to production a machine learning product and how to manage an AI project
• Talents and team: How to create a high-performing machine learning team—skills you need, recognizing unicorns, effectively investing in development, and insights from I/O studies
• The critical role of internal and external partnerships: Executive buy in, partnership with business experts, the criticality of data strategy and partnership with other tech team, and ML as a key player in an open innovation ecosystem

You’ll walk away with insights and lessons to empower other leaders to accelerate the success of their organization by harnessing the power of ML and driving a successful ML strategy.