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 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Data is precious – so it's been asserted; it has become the world's most valuable commodity. And when it comes to training artificial intelligence (AI) and machine learning (ML) models, it's absolutely essential. Still, due to various factors, high-quality, real-world data can be hard – sometimes even impossible – to come by. This is where synthetic data becomes so valuable.

As AI transforms global workplaces, new research shows that data literacy will be the most in-demand skill by 2030. According to research from Qlik, a little over one in five employees believe their employer is preparing them for a more data-oriented and automated workplace (21%). This is despite most business leaders predicting an upheaval in working practices due to the rapid onset of AI. The report, Data Literacy: The Upskilling Evolution, found that 35% of employees say they had changed jobs in the last 12 months because their employer wasn't offering enough upskilling and training opportunities. Developed by Qlik in partnership with The Future Labs, the report combines insights from expert interviews with surveys from over 1,200 global C-level executives and 6,000 employees.

If you've ever helped someone move furniture, you know it takes coordination -- simultaneously pushing or pulling and reacting based on what your helper is doing. That makes it an ideal problem to examine collaboration between robots, said Andrew Barth, a doctoral student in UC's College of Engineering and Applied Science. "It's a good metaphor for cooperation," Barth said. In the Intelligent Robotics and Autonomous Systems Lab of UC aerospace engineering professor Ou Ma, student researchers developed artificial intelligence to train robots to work together to move a couch -- or in this case a long rod that served as a stand-in -- around two obstacles and through a narrow door in computer simulations. "We made it a little more difficult on ourselves. We want to accomplish the task with as little communication as possible among the robots," student Barth said.

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.

For decades, the necessary technology tools have been available to generate more and more data, as it relates to various processes in construction such as project management, scheduling, job costing, and more. The challenge is, with the influx of data, in the last few years, it has become more difficult to use that information well. "It hasn't been centrally stored and there hasn't been computing capability, even if the information was centrally stored," explains Dan Patterson, chief design officer, InEight, www.ineight.com, "That information is very loosely structured; it is not a uniform format. It is very difficult for computers to then mine that historical information."