Publisher's Synopsis
This toolkit for ML and AI researchers covers essential libraries, frameworks, and tools across various domains, including NLP, computer vision, and reinforcement learning. Emphasizing the importance of experiment tracking with platforms like MLflow and Weights & Biases, it highlights ethical considerations through tools like AIF360 and Fairlearn to ensure fairness in models. The chapter concludes with a call for continuous learning and adaptation in a rapidly evolving field, stressing the need for ethical responsibility. Together, researchers can navigate the AI frontier, creating impactful, transparent, and equitable innovations for society.
