Tools and frameworks for machine learning and deep learning: A review
Nitin Liladhar Rane, Suraj Kumar Mallick, Ömer Kaya, Jayesh Rane
Abstract
The fast progress of Artificial Intelligence (AI) has resulted in major advancements in tools and structures for machine learning (ML) and deep learning (DL), changing numerous industries. This study offers a thorough examination of the most recent tools and frameworks that aid in the creation, implementation, and expansion of ML and DL models. The capabilities of key frameworks like TensorFlow, PyTorch, and Keras are assessed in their ability to facilitate the construction of complex neural networks by researchers and practitioners. Furthermore, cutting-edge resources such as Hugging Face Transformers for simplifying natural language processing duties and NVIDIA's RAPIDS suite for speeding up data science processes are praised for their inventive impacts. The research delves into incorporating these tools with cloud services such as Google Cloud AI, Amazon SageMaker, and Microsoft Azure ML, highlighting how they help make high-performance computing resources more accessible. The discussion revolves around how automated machine learning (AutoML) frameworks like Google's AutoML and H2O.ai ease the process of model selection and hyperparameter tuning, making it more accessible to non-experts. In addition, the study explores the significance of MLOps tools such as MLflow and Kubeflow, which support the continuous integration and deployment of ML workflows, guaranteeing scalability and reproducibility. This research offers valuable insights into the current ML and DL frameworks landscape by examining the strengths and limitations of these tools, helping researchers and industry professionals choose the right tools for their AI projects.