Machine Learning Engineering for Production (MLOps) Specialization - Course 1, Week 1, Lesson 5
This article is a summary of a YouTube video "#5 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 5]" by DeepLearningAI
TLDR Deploying a machine learning model involves addressing both machine learning/statistical issues and software engineering issues, and it requires continuous work to adapt and maintain the model in the face of changes to the data.
Changes in data, such as language variations or new technology, can lead to degradation in the performance of speech recognition systems, highlighting the importance of adapting learning algorithms accordingly.
The COVID-19 pandemic caused a sudden shift in consumer behavior, resulting in a change in the way people use credit cards, which highlighted the need for machine learning teams to collect new data and retrain systems to adapt to this new data distribution.
The need for real-time predictions in certain systems, such as speech recognition, requires software that can respond within hundreds of milliseconds.
Deploying visual inspection systems in factories at the edge is crucial to avoid disruptions caused by rare internet connection issues, allowing the factory to continue operating smoothly.
The required levels of security and privacy in machine learning systems can vary greatly depending on the application, with sensitive data like patient records requiring high levels of protection.
Deploying a machine learning model is not the finish line, but rather the beginning of continuous work to feed data back, update the model, and maintain it in the face of changes to the data.