Machine Learning
What is machine learning?
Machine learning is a field of artificial intelligence where algorithms are trained on datasets to identify patterns and make predictions or classifications without being explicitly programmed for each specific task. Instead of following hard-coded rules, machine learning systems learn from examples and improve their performance over time.
How does machine learning work in product development?
In product development, machine learning typically involves collecting user preference data over time to feed algorithms that can personalize experiences, make recommendations, or classify inputs. For example, a machine learning classifier might determine whether an image contains a dog or a cat by learning from thousands of labeled examples.
The process follows a clear progression: algorithms are trained on training datasets, tested against separate test datasets to measure accuracy, and then deployed to production. Teams use evaluation techniques to score how well the model performs—if a classifier achieves 97% accuracy on a representative test dataset, teams can be confident it will perform similarly in production.
How does machine learning relate to AI product development?
Many evaluation techniques used for AI products today originated in the machine learning world. Practices like error analysis, code-based evaluations, and systematic testing with test datasets all come from machine learning's data science roots.
These techniques typically use tools like Python and Jupyter Notebooks, which have become standard for both machine learning and AI product development. Product teams building with LLMs often adapt machine learning evaluation approaches to measure and improve their AI products.
Learn more:
- The Ethics of the Data We Collect
- Behind the Scenes: Building the Product Talk Interview Coach
- Building AI Products - All Things Product Podcast with Teresa Torres & Petra Wille
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