Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needsChip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.This book will help you tackle scenarios such as:Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they''re data dependent, with data varying wildly from one use case to the next. In this book, you''ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Recent breakthroughs in AI have not only increased demand for AI products, they''ve also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You''ll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She''s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O''Reilly).

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they''re data dependent, with data varying wildly from one use case to the next. In this book, you''ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Recent breakthroughs in AI have not only increased demand for AI products, they''ve also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You''ll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She''s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O''Reilly).

Los recientes avances en IA no slo han aumentado la demanda de productos de IA, sino que tambin han reducido las barreras de entrada para quienes quieren crear productos de IA. El enfoque del modelo como servicio ha transformado la IA de una disciplina esotrica en una potente herramienta de desarrollo que cualquiera puede utilizar. Todo el mundo, incluidos los que tienen una experiencia mnima o nula en IA, puede ahora aprovechar los modelos de IA para crear aplicaciones. En este libro, el autor Chip Huyen habla de la ingeniera de la IA: el proceso de creacin de aplicaciones con modelos bsicos fcilmente disponibles.El libro comienza con una visin general de la ingeniera de IA, explicando en qu se diferencia de la ingeniera de ML tradicional y hablando de la nueva pila de IA. Cuanto ms se utiliza la IA, ms oportunidades hay de que se produzcan fallos catastrficos, y por tanto, ms importante se vuelve la evaluacin. En este libro se analizan distintos enfoques para evaluar modelos abiertos, incluido el enfoque de la IA como juez, que est creciendo rpidamente.Los desarrolladores de aplicaciones de IA descubrirn cmo navegar por el panorama de la IA, incluidos los modelos, los conjuntos de datos, los puntos de referencia de evaluacin y el nmero aparentemente infinito de casos de uso y patrones de aplicacin. Aprenders un marco para desarrollar una aplicacin de IA, empezando con tcnicas sencillas y avanzando hacia mtodos ms sofisticados, y descubrirs cmo desplegar eficazmente estas aplicaciones.Comprenders qu es la ingeniera de la IA y en qu se diferencia de la ingeniera tradicional del aprendizaje automticoAprenders el proceso para desarrollar una aplicacin de IA, los retos en cada paso y los enfoques para abordarlosExplorar diversas tcnicas de adaptacin de modelos, como la ingeniera de impulsos, la GAR, el ajuste fino, los agentes y la ingeniera de conjuntos de datos, y comprender cmo y por qu funcionan.Examinar los cuellos de botella de la latencia y el coste al servir modelos de base y aprender a superarlosElige el modelo, el conjunto de datos, las referencias de evaluacin y las mtricas adecuados a tus necesidadesChip Huyen trabaja para acelerar el anlisis de datos en GPU en Voltron Data. Anteriormente, trabaj en Snorkel AI y NVIDIA, fund una startup de infraestructura de IA y ense Diseo de Sistemas de Aprendizaje Automtico en Stanford. Es autora del libro Designing Machine Learning Systems, un superventas de Amazon en IA.AI Engineering se basa en Designing Machine Learning Systems (O'Reilly) y lo complementa.

Los sistemas de aprendizaje automático, en inglés Machine Learning, implican el uso de métodos, algoritmos y procesos complejos que constan de muchos componentes diferentes; además, dependen de datos que varían considerablemente de un caso a otro. Con este libro aprenderá un método integral para diseñar sistemas de aprendizaje automático fiables, escalables, fáciles de mantener y adaptables a los entornos dinámicos y a los requisitos empresariales.La autora Chip Huyen, cofundadora de Claypot AI, considera cada decisión de diseño en su contexto para determinar la manera como este puede ayudar a su sistema. Analiza desde cómo procesar y crear datos de formación, hasta qué atributos utilizar, con qué frecuencia volver a formar los modelos y qué elementos supervisar.En el marco iterativo de este libro se utilizan estudios de casos reales respaldados por referencias amplias que le ayudarán a alcanzar sus objetivos. Así pues, gracias a esta lectura conocerá:"La ingeniería de datos y la elección de las métricas adecuadas para resolver un problema empresarial."La automatización del proceso de desarrollo, evaluación, instalación y actualización de los modelos."El desarrollo de un sistema de supervisión para detectar y resolver rápidamente los problemas que pueda encontrarse con sus modelos en funcionamiento."La arquitectura de una plataforma de aprendizaje automático que sirva para todos los casos."El desarrollo de sistemas de aprendizaje automático responsables.Chip Huyen es cofundadora de Claypot AI, una plataforma de aprendizaje automático en tiempo real. A través de su trabajo en NVIDIA, Netflix y Snorkel AI, ha ayudado a algunas de las organizaciones más grandes del mundo a desarrollar e implementar sus sistemas de aprendizaje automático. Chip basó este libro en sus apuntes para CS 329S: Diseño de Sistemas de Aprendizaje Automático, un curso que imparte en la Universidad de Stanford."Este es, sencillamente, el mejor libro que se puede leer sobre cómo construir, implementar y extender los modelos de aprendizaje automático en una empresa para lograr un impacto máximo".-Josh Wills Ingeniero de software en WeaveGrid y exdirector de ingeniería de datos, Slack"En un ecosistema floreciente pero caótico, esta visión de principios sobre el aprendizaje automático de principio a fin es tanto su mapa como su brújula: una lectura obligada para los profesionales dentro y fuera de los gigantes tecnológicos".-Jacopo TagliabueDirector de IA, Coveo

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needsChip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.This book will help you tackle scenarios such as:Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they''re data dependent, with data varying wildly from one use case to the next. In this book, you''ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Recent breakthroughs in AI have not only increased demand for AI products, they''ve also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You''ll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She''s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O''Reilly).

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they''re data dependent, with data varying wildly from one use case to the next. In this book, you''ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems

Recent breakthroughs in AI have not only increased demand for AI products, they''ve also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You''ll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She''s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O''Reilly).

Los recientes avances en IA no slo han aumentado la demanda de productos de IA, sino que tambin han reducido las barreras de entrada para quienes quieren crear productos de IA. El enfoque del modelo como servicio ha transformado la IA de una disciplina esotrica en una potente herramienta de desarrollo que cualquiera puede utilizar. Todo el mundo, incluidos los que tienen una experiencia mnima o nula en IA, puede ahora aprovechar los modelos de IA para crear aplicaciones. En este libro, el autor Chip Huyen habla de la ingeniera de la IA: el proceso de creacin de aplicaciones con modelos bsicos fcilmente disponibles.El libro comienza con una visin general de la ingeniera de IA, explicando en qu se diferencia de la ingeniera de ML tradicional y hablando de la nueva pila de IA. Cuanto ms se utiliza la IA, ms oportunidades hay de que se produzcan fallos catastrficos, y por tanto, ms importante se vuelve la evaluacin. En este libro se analizan distintos enfoques para evaluar modelos abiertos, incluido el enfoque de la IA como juez, que est creciendo rpidamente.Los desarrolladores de aplicaciones de IA descubrirn cmo navegar por el panorama de la IA, incluidos los modelos, los conjuntos de datos, los puntos de referencia de evaluacin y el nmero aparentemente infinito de casos de uso y patrones de aplicacin. Aprenders un marco para desarrollar una aplicacin de IA, empezando con tcnicas sencillas y avanzando hacia mtodos ms sofisticados, y descubrirs cmo desplegar eficazmente estas aplicaciones.Comprenders qu es la ingeniera de la IA y en qu se diferencia de la ingeniera tradicional del aprendizaje automticoAprenders el proceso para desarrollar una aplicacin de IA, los retos en cada paso y los enfoques para abordarlosExplorar diversas tcnicas de adaptacin de modelos, como la ingeniera de impulsos, la GAR, el ajuste fino, los agentes y la ingeniera de conjuntos de datos, y comprender cmo y por qu funcionan.Examinar los cuellos de botella de la latencia y el coste al servir modelos de base y aprender a superarlosElige el modelo, el conjunto de datos, las referencias de evaluacin y las mtricas adecuados a tus necesidadesChip Huyen trabaja para acelerar el anlisis de datos en GPU en Voltron Data. Anteriormente, trabaj en Snorkel AI y NVIDIA, fund una startup de infraestructura de IA y ense Diseo de Sistemas de Aprendizaje Automtico en Stanford. Es autora del libro Designing Machine Learning Systems, un superventas de Amazon en IA.AI Engineering se basa en Designing Machine Learning Systems (O'Reilly) y lo complementa.

Los sistemas de aprendizaje automático, en inglés Machine Learning, implican el uso de métodos, algoritmos y procesos complejos que constan de muchos componentes diferentes; además, dependen de datos que varían considerablemente de un caso a otro. Con este libro aprenderá un método integral para diseñar sistemas de aprendizaje automático fiables, escalables, fáciles de mantener y adaptables a los entornos dinámicos y a los requisitos empresariales.La autora Chip Huyen, cofundadora de Claypot AI, considera cada decisión de diseño en su contexto para determinar la manera como este puede ayudar a su sistema. Analiza desde cómo procesar y crear datos de formación, hasta qué atributos utilizar, con qué frecuencia volver a formar los modelos y qué elementos supervisar.En el marco iterativo de este libro se utilizan estudios de casos reales respaldados por referencias amplias que le ayudarán a alcanzar sus objetivos. Así pues, gracias a esta lectura conocerá:"La ingeniería de datos y la elección de las métricas adecuadas para resolver un problema empresarial."La automatización del proceso de desarrollo, evaluación, instalación y actualización de los modelos."El desarrollo de un sistema de supervisión para detectar y resolver rápidamente los problemas que pueda encontrarse con sus modelos en funcionamiento."La arquitectura de una plataforma de aprendizaje automático que sirva para todos los casos."El desarrollo de sistemas de aprendizaje automático responsables.Chip Huyen es cofundadora de Claypot AI, una plataforma de aprendizaje automático en tiempo real. A través de su trabajo en NVIDIA, Netflix y Snorkel AI, ha ayudado a algunas de las organizaciones más grandes del mundo a desarrollar e implementar sus sistemas de aprendizaje automático. Chip basó este libro en sus apuntes para CS 329S: Diseño de Sistemas de Aprendizaje Automático, un curso que imparte en la Universidad de Stanford."Este es, sencillamente, el mejor libro que se puede leer sobre cómo construir, implementar y extender los modelos de aprendizaje automático en una empresa para lograr un impacto máximo".-Josh Wills Ingeniero de software en WeaveGrid y exdirector de ingeniería de datos, Slack"En un ecosistema floreciente pero caótico, esta visión de principios sobre el aprendizaje automático de principio a fin es tanto su mapa como su brújula: una lectura obligada para los profesionales dentro y fuera de los gigantes tecnológicos".-Jacopo TagliabueDirector de IA, Coveo