Dr. Amita Kapoor - Böcker

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainabilityPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook DescriptionAI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it’s necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you’ll be able to make existing black box models transparent.You’ll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You’ll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you’ll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You’ll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.By the end of this book, you’ll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You’ll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.What you will learnUnderstand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is forThis book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.

Master AI and IoT integration, from fundamentals to advanced techniques, and revolutionize your approach to building intelligent, data-driven solutions across industriesFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataEnhance your IoT solutions with advanced AI techniques, including deep learning, optimization, and generative adversarial networksGain practical insights through industry-specific IoT case studies in manufacturing, smart cities, and automationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTransform IoT devices into intelligent systems with this comprehensive guide by Amita Kapoor, Chief AI Officer at Tipz AI. Drawing on 25 years of expertise in developing intelligent systems across industries, she demonstrates how to harness the combined power of artificial intelligence and IoT technology. A pioneer in making AI and neuroscience education accessible worldwide, Amita guides you through creating smart, efficient systems that leverage the latest advances in both fields. This new edition is updated with various optimization techniques in IoT used for enhancing efficiency and performance. It introduces you to cloud platforms such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) for analyzing data generated using IoT devices. You’ll learn about machine learning algorithms, deep learning techniques, and practical applications in real-world IoT scenarios and advance to creating AI models that work with diverse data types, including time series, images, and audio. You’ll also harness the power of widely used Python libraries, TensorFlow and Keras, to build a variety of smart AI models. *Email sign-up and proof of purchase requiredWhat you will learnIntegrate AI and IoT for enhanced device intelligenceUnderstand how to build scalable and efficient IoT systemsMaster both supervised and unsupervised machine learning techniques for processing IoT dataExplore the full potential of deep learning in IoT applicationsDiscover AI-driven strategies to optimize IoT system efficiencyImplement real-world IoT projects that leverage AI capabilitiesImprove device performance and decision-making using AI algorithmsWho this book is forThis book is for IoT developers, engineers, and tech enthusiasts, particularly those with a background in Python, looking to integrate artificial intelligence and machine learning into IoT systems. Python developers eager to apply their knowledge in new, innovative ways will find it useful. It’s also an invaluable guide for anyone with a foundational understanding of IoT concepts ready to take their skills to the next level and shape the future of intelligent devices.