The Machine Learning Lessons I’ve Learned Last Month
Summary
The article discusses key insights on machine learning gained over the past month, focusing on deadlines, downtimes, and flow times. The authors share valuable lessons that can enhance understanding and application in the field of data science.
Key Insights
Is machine learning the same as artificial intelligence?
No, machine learning and artificial intelligence are related but distinct concepts. Artificial intelligence is a broad discipline encompassing various technologies designed to mimic human intelligence, while machine learning is a specific subset of AI focused on developing algorithms that learn from data and make predictions based on patterns. AI represents the overall problem of creating machines with cognitive abilities, whereas machine learning is one solution to achieve that goal through data-driven algorithms.
Why do machine learning models need continuous monitoring and updates after deployment?
Machine learning models require ongoing monitoring and retraining because the data patterns they were trained on may no longer reflect current trends and conditions. Changes in data patterns, shifts in user behavior, or external factors can all necessitate model adjustments. For example, in fraud detection systems, new tactics are constantly evolving, and if models aren't updated with the latest fraud patterns, they become ineffective. Even small changes in data inputs can lead to significant performance drops, making regular evaluation essential to ensure models continue delivering accurate results.