Mighty AI Principal Data Scientist Angie Hugeback’s interview with Sam Charrington on his “This Week in Machine Learning & AI” podcast. The AI industry continues to shift, but this conversation based in data science and statistics principles remains relevant and grounded. So we’re opening the vault to resurface Angie’s secrets about training data for computer vision and natural language models.

1. A Data Scientist’s
Top 3 Tips for Training
Your AI Data

2. Secrets from the AI training data vault
Mighty AI Principal Data Scientist Angie Hugeback shared cool insights in an
interview with Sam Charrington on his “This Week in Machine Learning & AI”
podcast.
Or read on see her top tips for training AI models…
Click here to check it
out

3. TIP #1

4. Smart humans = great training data
• Generating high-quality, accurate training datasets is the most important
job in scaling AI models, particularly in computer vision and natural
language.
• Anyone experienced in the field knows your model is only as good as
your training data (and the humans training it).
4
TIP #1

5. TIP #2

6. You need good coverage
• Your training data only represents a subset of the space in which you
expect your model to function.
• Represent your input as best as possible if you want the most accurate
results.
6
TIP #2

7. TIP #3

8. Quality makes the difference
• Quality in computer vision and natural learning models is essential.
• But (surprise!) you can get away with lower-quality training data in
certain situations.
• Curious to learn more?
8
TIP #3
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9. Thanks!