NumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning
Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.
Machine learning presents transformative opportunities for businesses and organizations across various industries. From improving customer experiences to optimizing operations and driving innovation, the applications of machine learning are vast. However, adopting machine learning solutions is not without challenges. These challenges span across data quality, technical complexities, infrastructure requirements, and cost…
Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as a result, the need for effective standards for deploying and maintaining machine learning systems has become critical.
MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.