Machine Learning Frameworks and Tools Questions

Comprehensive practical knowledge of major machine learning and deep learning frameworks and their surrounding tooling. Includes hands on experience with TensorFlow and PyTorch for building neural networks using high level interfaces such as Keras, defining custom layers, implementing custom training loops, understanding tensors and automatic differentiation, and performing model saving, loading, and inference. Covers scikit learn and ensemble libraries such as XGBoost and LightGBM for traditional machine learning tasks and guidance on when to use each tool versus deep learning frameworks. Encompasses production and operational considerations including model serialization, serving and deployment patterns, performance profiling and optimization, reproducibility and versioning, monitoring and logging, and integration with cloud machine learning platforms and machine learning operations tools such as MLflow, Kubeflow, and Data Version Control. Candidates should be able to compare framework trade offs, discuss ecosystem differences and constraints, demonstrate end to end model training and evaluation workflows, and explain deployment and monitoring strategies.