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Engineering MLOps
Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you wit...
Mastering Azure Machine Learning
Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services Key Features Implement end-to-end machine learning pipelines on Azure Train deep learning models using Azure compute infrastructure Deploy machine learning models using MLOps Book Description Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, de...
Data Modelling and Analytics for the Internet of Medical Things
Describes the evolution of Internet of Medical Things, smart medical devices and evaluating IoMT from human perspective Discusses IoMT connected smart medical devices and its applications to improve health outcomes and the existing architecture models to implement IoMT Examines the integration of IoMT with Quantum and IoMT use cases and applications Explores methodologies, technologies, and solutions for big medical data analytics in healthcare systems Discusses security and privacy issues can be dealt effectively for IoMT
MLOps with Red Hat OpenShift
Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, thi...
Fundamentals of Data Engineering
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, ...
Survive to Thrive: Journey of Dr. Rajesh Soin
Today, Dr Rajesh Soin is the epitome of Indian-American success. He is an inspiring figure, much honored and much-touted when it comes to entrepreneurial vision and success. He is well-known as the founder of Modern Technologies Corporation (MTC), a multinational company which he started with a capital of $1700. He grew up in straitened circumstances. In his childhood, he faced a devastating tragedy. He arrived in America with only 75 cents in his pocket and no place to stay. When he started college, he had to subsist on $30 for a whole month, and in grad school, he got robbed at gunpoint. When he started his company, there was a time when all his credit cards were maxed out to make payroll,...
Machine Learning Engineering with MLflow
Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features...
Safety and Security of Cyber-Physical Systems
Cyber-physical systems (CPSs) consist of software-controlled computing devices communicating with each other and interacting with the physical world through sensors and actuators. Because most of the functionality of a CPS is implemented in software, the software is of crucial importance for the safety and security of the CPS. This book presents principle-based engineering for the development and operation of dependable software. The knowledge in this book addresses organizations that want to strengthen their methodologies to build safe and secure software for mission-critical cyber-physical systems. The book: • Presents a successful strategy for the management of vulnerabilities, threats, and failures in mission-critical cyber-physical systems; • Offers deep practical insight into principle-based software development (62 principles are introduced and cataloged into five categories: Business & organization, general principles, safety, security, and risk management principles); • Provides direct guidance on architecting and operating dependable cyber-physical systems for software managers and architects.
Practical Deep Learning at Scale with MLflow
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow Key Features • Focus on deep learning models and MLflow to develop practical business AI solutions at scale • Ship deep learning pipelines from experimentation to production with provenance tracking • Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility Book Description The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainabili...
Multi-disciplinary Trends in Artificial Intelligence
The 47 full papers and 24 short papers included in this book were carefully reviewed and selected from 245 submissions. These articles cater to the most contemporary and happening topics in the fields of AI that range from Intelligent Recommendation Systems, Game Theory, Computer Vision, Reinforcement Learning, Social Networks, and Generative AI to Conversational and Large Language Models. They are organized into four areas of research: Theoretical contributions, Cognitive Computing models, Computational Intelligence based algorithms, and AI Applications.
