Darklang Development And Deployment
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Darklang Development and Deployment
"Darklang Development and Deployment" "Darklang Development and Deployment" is a comprehensive guide crafted for developers, architects, and technical leaders eager to master the capabilities and paradigms of Darklang—a groundbreaking platform designed to radically simplify the journey from code conception to production deployment. Beginning with the philosophical foundations and architectural underpinnings of Darklang, the book explores the motivations, design choices, and unique workflow that differentiate Darklang from traditional software stacks. Readers will gain a nuanced understanding of the edit-deploy-observe loop, the live coding environment, and the vibrant community that propels Darklang's continuous evolution. The text delves deeply into Darklang's functional language constructs, syntax, and semantics, offering practical perspectives on core programming idioms, robust error handling, and state management. It guides readers through in-built and external datastore integration, modern API development, authentication and authorization flows, and best practices for data privacy and regulatory compliance. Specialized chapters illuminate every aspect of the development lifecycle—from serving dynamic frontend content to orchestrating real-time systems, managing schema migrations, implementing granular security models, and measuring operational performance. Emphasizing developer productivity and operational excellence, the book showcases live collaboration tools, trace-driven debugging, automated CI/CD pipelines, graceful deployment patterns, and cost management strategies. It also addresses the challenges of scaling, disaster recovery, and integrating with broader cloud and edge ecosystems. Forward-looking readers will appreciate advanced chapters on microservice architectures, functional reactive programming, and the ongoing roadmap for serverless technologies. "Darklang Development and Deployment" equips professionals with both strategic context and actionable insights, empowering them to build and maintain robust, scalable, and secure applications on the Darklang platform.
Serverless Development on AWS
Author: Sheen Brisals
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-01-23
The adoption of serverless is on the rise, but until now, little guidance has been available for development teams that want to apply this technology on AWS. This definitive guide is packed with architectural, security, and data best practices and patterns for architects and engineers who want to build reliable enterprise-scale serverless solutions. Sheen Brisals, an AWS Serverless Hero, and Luke Hedger, an AWS Community Builder, outline the serverless adoption requirements for an enterprise, examine the development tools your team needs, and explain in depth the nuances of testing event-driven and distributed serverless services. You'll gain practical guidance for keeping up with change and learn how to build serverless solutions with sustainability in mind. Examine the serverless technology ecosystem and AWS services needed to develop serverless applications Learn the approach and preparation required for a successful serverless adoption in an enterprise Learn serverless architectures and implementation patterns Design, develop, and test distributed serverless microservices on AWS cloud Apply security best practices while building serverless solutions Identify and adapt the implementation patterns for your particular use case Incorporate the necessary measures for observable serverless applications Implement sustainable serverless applications in the cloud
Design Patterns for Large Language Models
Author: Anand Vemula
language: en
Publisher: Independently Published
Release Date: 2024-08-04
"Design Patterns for Large Language Models: From Development to Deployment" is a comprehensive guide to designing, implementing, and deploying large language models (LLMs) with a focus on best practices and practical solutions. This book offers an in-depth exploration of various design patterns crucial for the effective use of LLMs, covering their entire lifecycle from development to deployment. The book begins with foundational concepts, providing readers with a solid understanding of what large language models are, including their architecture and key components. It then delves into essential design principles, such as scalability, efficiency, and interpretability, ensuring that readers can build models that are not only powerful but also practical and ethical. One of the core sections of the book focuses on data management patterns, guiding readers through techniques for collecting, preprocessing, and augmenting data, while addressing challenges like data imbalances and privacy concerns. It also covers model training patterns, including transfer learning, fine-tuning, and continual learning, providing practical advice on how to adapt models to different tasks and domains. The book further explores optimization and efficiency patterns, offering strategies for model pruning, quantization, and knowledge distillation, as well as techniques for distributed training. In the realm of inference patterns, readers learn about real-time, batch, and edge inference methods, including solutions for handling latency and throughput issues. Additionally, the book addresses robustness and reliability patterns, emphasizing the importance of adversarial training, anomaly detection, and error handling. It also highlights user interaction patterns, focusing on natural language understanding, dialogue management, and personalization techniques. Finally, the book looks to the future, discussing emerging technologies, research frontiers, and industry trends. It provides actionable insights into how AI developments will shape various fields, preparing readers for the evolving landscape of large language models