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1
Introduction
2
State of AI
3
Cost of Training
4
Talent Shortage
5
Investments
6
AI Investments
7
Summary
8
Machine Learning Maturity
9
Machine Learning Product
10
Culture Data Infrastructure
11
Tech Unicorns
12
Culture
13
Training vs Reality
14
The Fine Step
15
Do You Need Machine Learning
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The Production Problem
17
When to Stop
18
Stay Up to Date
19
Team Sport
20
Ethical ML
21
Example
22
Ethical AI
23
Responsible AI
24
Data Centric
25
Good Data Set
26
DataCentric Approach
27
Model Diagnostic
28
Active Learning
29
Improvement
30
Infrastructure
31
Enemies
32
Infrastructure Match Readiness
33
Development Production Tension
34
Recap
35
Resources
Description:
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.

Machine Learning for the 99%

Open Data Science
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