Business intelligence applications are of vital importance as they help organizations manage, develop, and communicate intangible assets such as information and knowledge.
This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software.
This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code.
Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health.
This interdisciplinary book incorporates various aspects of environment, ecology, and natural disaster management including cognitive informatics and computing.
This book systematically elaborates on the intelligent information processing technology for a bioinspired polarization compass and inertial integrated navigation system.
The PC era is giving way to a new form of popular computing in which smart, globally-connected objects and environments are the new computational ground.
This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models.
Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value.
While Robotic Process Automation (RPA) has been around for about 20 years, it has hit an inflection point because of the convergence of cloud computing, big data and AI.
Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.
Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.
Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python.
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure.
So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles.
Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's Autonomous Database cloud offering.