These whitepapers are designed to help those operating at the cutting edge of machine learning. They focus on some of the core issues affecting the industry and provide actionable insights to help elevate the way your team approaches the entire model lifecycle.
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.
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.
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 comput efficiency, how to mitigate bias and toxicity in your modeling, and much more.
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.
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.
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.
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.
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 comput efficiency, how to mitigate bias and toxicity in your modeling, and much more.
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.
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.
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