The goal of experiment management with SageMaker Search is to accelerate the model's development and experimentation phase, improving the productivity of data scientists and developers, while also reducing the overall time to market machine learning solutions.
The machine learning life cycle (continuous experimentation and tuning) states that when you initiate the training of a new learning algorithm, to improve model performance, you conduct hyperparameter tuning. With each iteration of the tuning, you will need to check how the model's performance is improving.
This leads to hundreds and thousands of experiments and model versions. The whole process slows down the selection of a final optimized model. Additionally, it is critical to monitor the performance of a production model. If the predictive performance of the...
