Back in 2019, Gartner predicted that the vast majority of AI projects would continue to fail: Only 53% of projects make it from prototypes to production, and 85% of those blow up. And yet, AI adoption has only accelerated. In an IBM study, 42% organizations reported they're exploring AI, and AI adoption is growing steadily, up four points from 2021. "Very few AI products become successful in creating value for companies, even though companies invest quite a lot of manpower and resources," says Ali Riza Kuyucu, global head of data and analytics at Blue.cloud. "But driving efficiencies through artificial intelligence requires constant monitoring and improvement, or what we call continuous AI -- keeping and sustaining the business value of AI for an organization over a longer period."

Chapter 1 provides an overview of the broad and deep Amazon AI and ML stack, an enormously powerful and diverse set of services, open source libraries, and infrastructure to use for data science projects of any complexity and scale. Chapter 2 describes how to apply the Amazon AI and ML stack to real-world use cases for recommendations, computer vision, fraud detection, natural language understanding (NLU), conversational devices, cognitive search, customer support, industrial predictive maintenance, home automation, Internet of Things (IoT), healthcare, and quantum computing. Chapter 3 demonstrates how to use AutoML to implement a specific subset of these use cases with SageMaker Autopilot. Chapter 11 demonstrates real-time ML, anomaly detection, and streaming analytics on real-time data streams with Amazon Kinesis and Apache Kafka. Chapter 12 presents a comprehensive set of security best practices for data science projects and workflows, including IAM, authentication, authorization, network isolation, data encryption at rest, post-quantum network encryption in transit, governance, and auditability.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) is becoming increasingly mainstream – very quickly, and across a multitude of areas and applications. According to Jack Vernon, senior research analyst, European AI Systems, for IDC, 69% of organizations are either using AI already or plan to in the next 24 months. "But even more," Vernon said, "AI has clearly moved from the experimental phase to mission critical, with businesses realizing real value, from improved growth and revenue, to cost reduction, to operational efficiency. Now more than ever, businesses need an AI platform that is adaptive, shifting and adjusting to even the most unpredictable market conditions."

All Indian Reprints of O'Reilly are printed in Grayscale With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.

Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs" Antje Barth is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany.