Explore the challenges and strategies for implementing machine learning in small to medium-sized organizations through this webinar. Learn about the gap between large tech companies and smaller businesses in leveraging ML algorithms, and discover practical approaches to overcome hurdles in product definition, data collection, training with limited data, tracking, operations, deployment, and ethical considerations. Gain insights into assessing ML readiness, developing a data-centric approach, and balancing development and production tensions. Understand the importance of responsible AI and how to stay updated in the rapidly evolving field of machine learning. Ideal for professionals seeking to realize the full potential of ML in real-world applications within resource-constrained environments.
Overview
Syllabus
Introduction
State of AI
Cost of Training
Talent Shortage
Investments
AI Investments
Summary
Machine Learning Maturity
Machine Learning Product
Culture Data Infrastructure
Tech Unicorns
Culture
Training vs Reality
The Fine Step
Do You Need Machine Learning
The Production Problem
When to Stop
Stay Up to Date
Team Sport
Ethical ML
Example
Ethical AI
Responsible AI
Data Centric
Good Data Set
DataCentric Approach
Model Diagnostic
Active Learning
Improvement
Infrastructure
Enemies
Infrastructure Match Readiness
Development Production Tension
Recap
Resources
Taught by
Open Data Science
