Ebooks

Accelerating enterprise agentic AI from pilot to production

While 60% of firms struggle to move AI beyond the pilot stage, Google Cloud and Weights & Biases have partnered to bridge this chasm. Download this ebook to learn how to eliminate model randomness and establish the observability foundation needed to scale your agentic AI applications with confidence.

Gartner® thought leadership report: 2026 Market Guide for AI Evaluation and Observability Platforms

As AI systems grow more complex, ensuring performance, safety, and alignment becomes increasingly difficult. Learn how to move beyond subjective guesswork using a four-step framework to build a robust evaluation and observability strategy—and turn AI reliability into a strategic advantage.

Advancing Physical AI: From learning to embodied intelligence

Physical environments are non-deterministic, hardware varies across deployments, control loops are latency-sensitive, and experimentation is expensive. Overcoming these hurdles is essential because iteration speed and system visibility are now strategic advantages that will define the next era of intelligent machines operating in the real world.

AI model and compute speed: the new alpha for quantitative trading

For modern quant trading firms, AI model and compute speed is the alpha. With hundreds of millions of dollars often riding on fractions of a percentage point in accuracy, developing high-performing AI models that run on reliable compute is essential. Traditionally, quant researchers focused on building the best-performing models and then handed them off to platform engineers to optimize for latency and cost in production. But at today’s scale of AI, that separation no longer works. Modern quant firms need to move faster, enabling teams to build the best models and optimize them on the fastest infrastructure simultaneously.

Practitioner’s guide to reinforcement learning

Reinforcement learning (RL) is transforming how reliable AI agents are trained and deployed. Discover real-world use cases, efficiency techniques like LoRA, and practical patterns you can apply today.

Reinventing the contact center: The role of AI agents in customer experience

Rising labor costs, unpredictable demand, and rapid expansion of customer engagement channels make it tough for contact centers to maintain high customer satisfaction. This eBook explores how multi-agent AI systems are reshaping customer engagement at contact centers so companies can exceed ever-increasing customer expectations in a safe and scalable way.

Executive guide to AI inference

AI inference isn’t one-size-fits-all, and choosing the right approach is critical for success. Executives face rising challenges: escalating token costs, latency bottlenecks, and the need to balance speed, accuracy, and security in production-scale AI. This Executive guide to AI inference equips leaders with a clear framework to navigate these challenges and make smarter investments. You’ll see how inference differs from training workloads, why it demands always-on performance, and how the right hosting service can unlock efficiency and scale.

A primer on building successful AI agents

AI agents can dramatically boost productivity and innovation, but getting them into the real world takes a lot of iteration. Quality, governance, and security need to be baked into your development process from the start. Having the right tools and development workflows makes all these iterations possible. Whether you’re exploring agents for the first time or refining your current approach, this primer delivers actionable insights to help your team succeed and thrive in the AI era.

Navigating the EU AI Act: Compliance through governance and observability

The EU’s AI Act establishes a comprehensive framework to ensure the safe and ethical deployment of AI across industries. However, its requirements for strong governance, documentation, and transparency can be difficult to navigate. Discover how organizations from around the world are turning regulatory compliance into an advantage, driving innovation while maintaining global AI safety and ethics standards.

Evaluating AI agent applications

AI agents offer unprecedented capabilities, but pushing AI agent applications into production without rigorous evaluation risks inconsistent performance and a negative customer experience. Download this whitepaper to learn how AI app development differs from traditional software development, as well as the three components needed for a rigorous evaluation and a five-step recipe for running successful evaluations.

3 proven strategies to fast track AI projects

ML leaders are being asked to deliver higher quality models faster. Their teams need better tools to train, fine-tune, evaluate, deploy, and monitor models efficiently. In this whitepaper, we explain the top three strategies our customers use to accelerate experiment velocity, centralize model management, and improve governance.

Deliver AI agents with confidence: Strategies, ops, tooling, and best practices

AI agents can enhance productivity, efficiency, and decision-making—but only if you can securely deploy them to production. Download this whitepaper to learn how these autonomous systems differ from other AI technologies and explore top use cases. Learn how to overcome challenges and receive guidelines to ensure your AI agents are deployed and managed efficiently.

Best Practices for Fine-Tuning and Prompt Engineering LLMs

It’s become much more common to either fine-tune or prompt engineer existing LLMs for unique business needs. In this guide, you’ll learn fundamentals ranging from how to choose between fine-tuning and prompting, to tips and current best practices for prompt engineering.

Current Best Practices for Training LLMs from Scratch

In this whitepaper, we’ll share what we’ve learned from an insider’s perspective. You’ll read about how much data you need to train a competitive LLM, balancing memory and compute efficiency, how to mitigate bias and toxicity in your modeling, and much more.

MLOps: A Holistic Approach

In this whitepaper, we dig into operationalizing ML so your organization can spin up the right models that create real business value, faster. We go beyond just suggesting a tech stack and dig deep into three vital areas–people, processes, and platform–to uncover what the most successful organizations do.