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

4.3
estrellas
460 calificaciones

85 %

Robert Crowe

23.418 ya inscrito

ofrecido por

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

Acerca de este Curso

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In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
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: Collecting, Labeling, and Validating data
Week 2: Feature Engineering, Transformation, and Selection
Week 3: Data Journey and Data Storage
Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

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Curso 2 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. 22 horas para completar

Inglés (English)

Subtítulos: Inglés (English), Francés (French)

Qué aprenderás

Identify responsible data collection for building a fair ML production system.
Implement feature engineering, transformation, and selection with TensorFlow Extended
Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

Habilidades que obtendrás

ML Metadata
Convolutional Neural Network
TensorFlow Extended (TFX)
Data Validation
Data transformation

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 2 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. 22 horas para completar

Inglés (English)

Subtítulos: Inglés (English), Francés (French)

Instructor

Calificación del instructor

4,21/5 (145 calificaciones)

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

28.919 alumnos

3 cursos

ofrecido por

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Programa - Qué aprenderás en este curso

Calificación del contenido85%(2,428 calificaciones)

Semana

Semana 1

7 horas para completar

Week 1: Collecting, Labeling and Validating Data

This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.

7 horas para completar

12 videos (Total 95 minutos), 3 lecturas, 7 cuestionarios

12 videos

Specialization overview6m

Course Overview2m

Overview11m

ML Pipelines6m

Importance of Data8m

Example Application: Suggesting Runs8m

Responsible Data: Security, Privacy & Fairness11m

Case Study: Degraded Model Performance9m

Data and Concept Change in Production ML5m

Process Feedback and Human Labeling11m

Detecting Data Issues7m

TensorFlow Data Validation6m

3 lecturas

Week 1 Optional References3m

(Optional)Downloading your Notebook and Refreshing your Workspace

Partial Grading for Assignments2m

4 ejercicios de práctica

Intro to MLEP12m

Data Collection15m

Data Labeling5m

Issues in Training Data10m

Semana

Semana 2

7 horas para completar

Week 2: Feature Engineering, Transformation and Selection

Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances

7 horas para completar

12 videos (Total 86 minutos), 1 lectura, 5 cuestionarios

12 videos

Introduction to Preprocessing5m

Preprocessing Operations6m

Feature Engineering Techniques10m

Feature Crosses3m

Preprocessing Data at Scale12m

TensorFlow Transform14m

Hello World with tf.Transform7m

Feature Spaces5m

Feature Selection4m

Filter Methods6m

Wrapper Methods5m

Embedded Methods5m

1 lectura

Week 2 Optional References3m

3 ejercicios de práctica

Feature Engineering and Preprocessing15m

Feature Transformation15m

Feature Selection8m

Semana

Semana 3

5 horas para completar

Week 3: Data Journey and Data Storage

Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

5 horas para completar

8 videos (Total 42 minutos), 1 lectura, 5 cuestionarios

Semana

Semana 4

4 horas para completar

Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing

Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.

4 horas para completar

6 videos (Total 31 minutos), 5 lecturas, 4 cuestionarios

Reseñas

4.3

85 reseña

5 stars
62,22 %
4 stars
20,47 %
3 stars
9,94 %
2 stars
5,16 %
1 star
2,18 %

Principales reseñas sobre MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION

por FF8 de sep. de 2021

Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!

por HB15 de ene. de 2022

instruction on debugging jupyter and submission issue is important for learners

por SS12 de oct. de 2021

The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.

por BP28 de may. de 2021

excellent course. Nice to see how we can detect data drift and skew drift

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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Machine Learning Data Lifecycle in Production