Step into the world of Ethereum, a decentralized network reshaping how we interact with technology.

At its core lies the Ethereum Virtual Machine (EVM), a powerful runtime environment for smart contracts, maintained by a network of nodes. Explore how accounts, states, and transactions come together to create a seamless ecosystem, powered by gas to measure computational effort.

But Ethereum isn’t just technology—it’s an ecosystem. From wallets and exchanges to dApps and essential tools like Solidity and Etherscan, discover how it all connects.

Through hands-on assignments, you’ll navigate Etherscan, verify smart contracts, and investigate token and address details, gaining practical insight into the mechanics of decentralization.

This is the story of Ethereum, where code meets trust, and decentralization fuels the future.

Imagine a world where trust doesn’t rely on a single authority.

Decentralized systems, powered by blockchain, have rewritten the rules—offering resilience, transparency, and security like never before. The journey begins with understanding how centralized systems evolved into decentralized and distributed models, each with its unique role.

At the heart of this revolution lie consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS), each telling a story of trade-offs—security, energy use, and efficiency.

This is not just a lesson in technology; it’s the narrative of how trust became distributed and systems became truly global. Dive in to explore the strengths, weaknesses, and innovations shaping our decentralized future.

Discover a clear, five-step process for implementing Generative AI effectively in your business. Start by identifying impactful problems that AI can solve, ensuring the technology addresses real needs. Next, learn how to choose a secure and scalable data platform and build a robust, well-governed data foundation to support your AI initiatives.

The chapter highlights the power of team collaboration, bringing together diverse skills to drive innovation. Finally, it emphasizes the importance of measuring success, learning from experiments, and celebrating achievements to foster ongoing growth.

This practical guide helps you take actionable steps toward successfully adopting generative AI in your organization.

Explore the critical security and ethical considerations surrounding Large Language Models (LLMs) in this insightful chapter. Learn why data privacy is essential, as LLMs are trained on massive datasets that may contain sensitive information, and discover the importance of strong security measures and data governance.

The chapter also dives into the risks of algorithmic bias and how careful data selection and model training can help mitigate it. It emphasizes the need for responsible deployment, covering issues like copyright compliance, preventing misuse, and avoiding harmful outputs such as misleading content.

Finally, it examines the legal implications of using copyrighted material for training LLMs, with real-world examples like lawsuits against OpenAI. This guide provides a clear understanding of how to navigate the ethical and legal challenges of deploying LLMs responsibly.

Learn how to successfully deploy Large Language Model (LLM) applications in this practical chapter. Discover key techniques for adapting data pipelines, like semantic caching and feature injection, and explore strategies to optimize inference for faster processing and lower latency while managing cloud platform costs.

The chapter also covers important aspects of user interface design and explains how to orchestrate AI agents for complex tasks using methods like prompt splitting and chaining to interact efficiently with external data.

Finally, see how user-friendly platforms simplify the development and deployment process, helping you bring LLM-powered applications to production faster and more effectively.

Discover how to build AI applications using Large Language Models (LLMs) in this practical chapter. Learn how to choose the right LLM based on factors like task alignment and model size and explore techniques such as prompt engineering, fine-tuning, and reinforcement learning to customize models for your needs.

The chapter highlights the role of a strong cloud data platform in managing the data and infrastructure needed to deploy and scale AI applications. You’ll also see how cloud platforms make it easier to integrate LLMs into existing workflows, helping businesses streamline their AI solutions.

This guide gives you a clear roadmap to effectively use LLMs to power real-world AI applications.

Learn the basics of Large Language Models (LLMs) in this easy-to-follow chapter. Discover the different types of LLMs, including general-purpose, task-specific, and domain-specific models, and how they work using transformer architecture and vector databases.

Key concepts like prompts and completions are explained, along with the importance of data governance and security to manage access and reduce risks. You'll also explore popular models like GPT, BERT, Llama, and Code Llama, as well as essential tools and frameworks for developers.

This chapter is a practical guide to understanding LLMs and how to use them safely and effectively.

Discover how Generative AI (Gen AI) and Large Language Models (LLMs) are changing the way we create and use technology. Unlike traditional AI, which only predicts outcomes, Gen AI creates entirely new content.

Learn why data, especially unstructured data like text and images, is so important for Gen AI and how cloud platforms help businesses manage and protect this data effectively. The excerpt also explains how AI has evolved over time, leading to today’s powerful LLMs, and explores their real-world uses.

This guide will show you how secure, accessible data can help businesses unlock the full potential of Gen AI to drive innovation and growth.