Challenges and Opportunities in Applying Machine Learning - Alex Jaimes - ODSC East 2018

Explore the challenges and opportunities in applying machine learning in this insightful conference talk from ODSC East 2018. Gain a clear overview separating fact from fiction and learn processes for identifying ML opportunities. Understand where machine learning can have the biggest impact while avoiding common pitfalls. Discover how improvements in processes can often outweigh algorithmic advancements. Examine key aspects such as data collection and quality, labeling definitions, metrics, objective functions, overfitting, and the cost of different error types. Acquire practical knowledge for applying ML in real-world scenarios, including algorithm selection for specific tasks. Learn to identify data sources and quality issues, develop appropriate metrics, manage different error types and their impacts, and improve processes affecting ML applications. Gain valuable insights into data strategy, business strategy, infrastructure considerations, cloud service and deployment models, deep neural networks, and human-centered computing design impact.

Intro
Questions
Challenges
New Styles of Art
Machine Learning
Ideal Scenario?
Data Strategy
Business Strategy
Infrastructure
Cloud Service Model
Cloud Deployment Model
Data Quality
Deep Neural Network
Training vs Inference
Deep Leaming
Distributed Deep Learning
Human-Centered Computing
Design Impact