Este curso forma parte de Programa especializado: Machine Learning Engineering for Production (MLOps)

4.8
estrellas
2,263 calificaciones

98 %

Andrew Ng +1 instructores más

Instructores principales

71.935 ya inscrito

Ofrecido Por

Programa especializado: Machine Learning Engineering for Production (MLOps)deeplearning.ai

Acerca de este Curso

164.178 vistas recientes

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. 

Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Selecting and Training a Model
Week 3: Data Definition and Baseline

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 4 en

Programa especializado: Machine Learning Engineering for Production (MLOps)

Nivel avanzado

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Aprox. 12 horas para completar

Inglés (English)

Qué aprenderás

Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.
Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.
Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.

Habilidades que obtendrás

Human-level Performance (HLP)
Concept Drift
Model baseline
Project Scoping and Design
ML Deployment Challenges

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 4 en

Programa especializado: Machine Learning Engineering for Production (MLOps)

Nivel avanzado

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Aprox. 12 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,96/5 (785 calificaciones)

Andrew Ng
Instructor principal

Instructor

Founder, DeepLearning.AI & Co-founder, Coursera

6.217.109 alumnos

38 cursos

Cristian Bartolomé Arámburu
Instructor principal

Curriculum Developer

71.935 alumnos

1 curso

Ofrecido por

deeplearning.ai

Programa - Qué aprenderás en este curso

Calificación del contenido98%(5,935 calificaciones)

Semana

Semana 1

5 horas para completar

Week 1: Overview of the ML Lifecycle and Deployment

5 horas para completar

9 videos (Total 81 minutos), 2 lecturas, 5 cuestionarios

Semana

Semana 2

3 horas para completar

Week 2: Select and Train a Model

3 horas para completar

16 videos (Total 107 minutos), 1 lectura, 3 cuestionarios

16 videos

Modeling overview2m

Key challenges5m

Why low average error isn't good enough10m

Establish a baseline7m

Tips for getting started6m

Error analysis example8m

Prioritizing what to work on5m

Skewed datasets12m

Performance auditing7m

Data-centric AI development2m

A useful picture of data augmentation5m

Data augmentation8m

Can adding data hurt?6m

Adding features8m

Experiment tracking4m

From big data to good data3m

1 lectura

Week 2 Optional References3m

2 ejercicios de práctica

Selecting and Training a Model10m

Modeling challenges10m

Semana

Semana 3

4 horas para completar

Week 3: Data Definition and Baseline

4 horas para completar

16 videos (Total 128 minutos), 3 lecturas, 3 cuestionarios

16 videos

Why is data definition hard?4m

More label ambiguity examples9m

Major types of data problems11m

Small data and label consistency8m

Improving label consistency9m

Human level performance (HLP)10m

Raising HLP9m

Obtaining data12m

Data pipeline5m

Meta-data, data provenance and lineage9m

Balanced train/dev/test splits4m

What is scoping?2m

Scoping process6m

Diligence on feasibility and value14m

Diligence on value7m

Milestones and resourcing2m

3 lecturas

Week 3 Optional References3m

References5m

Acknowledgments3m

2 ejercicios de práctica

Data Stage of the ML Production Lifecycle20m

Scoping (optional)10m

Reseñas

4.8

409 reseña

5 stars
84,40 %
4 stars
12,99 %
3 stars
1,89 %
2 stars
0,44 %
1 star
0,26 %

Principales reseñas sobre INTRODUCTION TO MACHINE LEARNING IN PRODUCTION

por GS25 de may. de 2021

Andew Ng is truly a world leader in the field, the way he approaches the subject and the explanations he gives are truly unparalleled. It always a pleasure taking a course he instructs.

por SA24 de jun. de 2021

I like the acknowledgement of the importance of data quality. Machine learning is much more than just training models. Real benefits can only be achieved when moving to real life data

por JF12 de jun. de 2021

The course helped both validate what I knew about the topic and update me about many new trends/tools via high quality references + first hand experences from the instructor.

por DC20 de may. de 2021

Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.

Ver todas las reseñas

Acerca de Programa especializado: Machine Learning Engineering for Production (MLOps)

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Machine Learning Engineering for Production (MLOps)

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

¿Tienes más preguntas? Visita el Centro de Ayuda al Estudiante.

Introduction to Machine Learning in Production

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás