Description: C:\Users\Chris\Desktop\Manning\Images\cover images\Kranz-MSCS-720x900.jpg

From LLMs in Production by Christopher Brousseau and Matthew Sharp

Machine Learning is experiencing a meteoric rise, transforming industries and shaping the future of technology. However, amid this transformation, one aspect of machine learning often remains in the shadows: the practical application of models in real-world scenarios. This is where our guide, LLMs in Production steps in.

This book is your compass in the vast landscape of Machine Learning models, particularly Large Language Models (LLMs). In an era where ML models are pervasive but their real-world implementation remains a challenge, this book emerges as a guiding light.


Out Now! Only at Manning.com


Who Is This Book For?

Whether you’re a seasoned Data Scientist, a budding Machine Learning Engineer, or a curious soul looking to understand the world of Large Language Models, this book is for you. Its versatile content caters to a wide audience.

To make the most of the knowledge presented within these pages, having some experience with Python and a basic understanding of ML concepts will be advantageous. However, even if you’re relatively new to the field, don’t be discouraged; this book will help you bridge the gap.

Now, let’s embark on a journey through the practical lessons and insights this book offers.


Costs: The Bottom Line

In any venture, considering costs is paramount. It’s not merely about budget management; it’s about ensuring the financial sustainability of a project or an organization. Effective cost management ensures resource allocation aligns with objectives, paving the way for success.

Moreover, thinking about costs is a critical component of risk management. By comprehending various cost aspects, you can identify potential risks and take proactive steps to mitigate them. This helps in avoiding unnecessary expenditures and makes projects more resilient to market fluctuations.

Transparency and accountability also go hand in hand with cost considerations. When organizations monitor and disclose costs, they demonstrate their commitment to ethical and efficient operations, which, in turn, builds trust among stakeholders.

This financial prudence applies squarely to the decision of building or buying LLMs (Large Language Models). Initially, buying might seem cost-effective, with services available at reasonable prices. However, building your own LLMs has witnessed significant innovation, making it more accessible and cost-efficient than ever.

Building offers not only cost control but also scalability without escalating expenses, unlike third-party services where costs can spiral upwards.

Security and Privacy: Guarding Your Fort

Security and privacy are paramount, especially when dealing with sensitive data. Imagine a scenario where a military manual needs to be distilled into a user-friendly assistant. Uploading this data to an external service is not just ill-advised; it’s reckless. Here, training a secure, private, and locally-contained model becomes imperative.

This concern isn’t limited to the military; even organizations in fields like law enforcement, healthcare, and finance grapple with it. Corporate espionage and data breaches loom large, emphasizing the need for secure, in-house solutions.

However, sensitive data isn’t the sole focus; intellectual property, trade secrets, and confidential information must also be shielded. Building and deploying models securely is a fundamental skill this book imparts.


Join our Newsletter to stay up-to-date on new releases and special offers!


Debunking Myths: The Road Ahead

LLMs aren’t mythical beasts reserved for tech giants. Contrary to the myth, they don’t always require starting from scratch. Open-source models and frameworks offer a launchpad. The difficulty of training LLMs is diminishing as automation improves, and the field evolves. You don’t need to be a tech giant to work with LLMs; you need knowledge and determination.

Moreover, don’t fall for the myth that deploying an LLM is as simple as getting an API key. Building a prototype is easy; building a robust product is challenging. LLMs are powerful tools but demand careful implementation for practical applications.


Large Language Models are transformative tools that work with, not against, humans. Their applications span countless industries, and they’re excellent problem solvers. However, they’re not one-size-fits-all; use them where they truly shine.

Buying LLMs can be a quick start, but building your own offers control, cost-effectiveness, and privacy. Remember, there’s no technical moat that bars entry; this book empowers you to take control.

In the realm of technology, the future belongs to those who adapt and innovate. By learning to harness LLMs now, you’re positioning yourself and your organization at the forefront of innovation.

As you journey through this book, remember that expertise isn’t a prerequisite; determination is. Embrace this technology, debunk the myths, and pave your path to success in the world of Large Language Models with LLMs in Production.