Solving six common ML challenges
We have written this book to provide you with the tools and techniques required to overcome six common challenges that typically become bottlenecks in the development of ML models. These six challenges are as follows:
- The typical development approach is model-centric: In traditional ML, the focus is primarily on the model – choosing the right algorithm, tuning hyperparameters, and optimizing performance metrics. This model-centric approach often involves countless iterations of tweaking the model to squeeze out a bit more accuracy. However, while models are undoubtedly important, this approach can sometimes lead to overlooking other equally crucial aspects, such as the quality and relevancy of the data feeding these models.
- The potential of any ML model is capped by data quality and quantity: No matter how sophisticated an ML model is, its performance is ultimately determined by the quality and quantity of the data it is trained on...
