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

4.4
estrellas
232 calificaciones

85 %

Robert Crowe

15.097 ya inscrito

ofrecido por

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

Acerca de este Curso

80.526 vistas recientes

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
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: Neural Architecture Search
Week 2: Model Resource Management Techniques
Week 3: High-Performance Modeling
Week 4: Model Analysis
Week 5: Interpretability

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 3 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. 26 horas para completar

Inglés (English)

Subtítulos: Inglés (English), Francés (French)

Qué aprenderás

Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

Habilidades que obtendrás

Explainable AI
Fairness Indicators
automl
Model Performance Analysis
Precomputing Predictions

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 3 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. 26 horas para completar

Inglés (English)

Subtítulos: Inglés (English), Francés (French)

Instructor

Calificación del instructor

4,47/5 (61 calificaciones)

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

28.526 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%(1,289 calificaciones)

Semana

Semana 1

7 horas para completar

Week 1: Neural Architecture Search

Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.

7 horas para completar

10 videos (Total 40 minutos), 4 lecturas, 6 cuestionarios

10 videos

Course Overview2m

Hyperparameter Tuning3m

Keras Autotuner Demo6m

Intro to AutoML5m

Understanding Search Spaces2m

Search Strategies4m

Measuring AutoML Efficacy3m

AutoML on the Cloud8m

Assignment Setup1m

Week 1 Wrap Up19s

4 lecturas

Ungraded Lab - Intro to Keras Tuner1h

Ungraded Lab - Hyperparameter Tuning and Model Training with TFX1h

Neural Architecture Search3m

AutoML3m

2 ejercicios de práctica

Hyperparameter Tuning and Neural Architecture Search10m

AutoML12m

Semana

Semana 2

5 horas para completar

Week 2: Model Resource Management Techniques

Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.

5 horas para completar

13 videos (Total 91 minutos), 6 lecturas, 3 cuestionarios

13 videos

Dimensionality Effect on Performance8m

Curse of Dimensionality9m

Curse of Dimensionality: an Example4m

Manual Dimensionality Reduction5m

Manual Dimensionality Reduction: Case Study7m

Algorithmic Dimensionality Reduction 2m

Principal Components Analysis7m

Other Techniques7m

Mobile, IoT, and Similar Use Cases 7m

Benefits and Process of Quantization8m

Post Training Quantization4m

Quantization Aware Training4m

Pruning12m

6 lecturas

Ungraded Lab - Manual Feature Engineering30m

Dimensionality Reduction Techniques3m

Ungraded Lab - Algorithmic Dimensionality Reduction40m

Quantization3m

Pruning3m

Ungraded Lab - Quantization and Pruning1h

2 ejercicios de práctica

Dimensionality Reduction20m

Quantization and Pruning20m

Semana

Semana 3

5 horas para completar

Week 3: High-Performance Modeling

Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.

5 horas para completar

6 videos (Total 57 minutos), 4 lecturas, 4 cuestionarios

6 videos

Distributed Training10m

High-Performance Ingestion11m

Training Large Models - The Rise of Giant Neural Nets and Parallelism13m

Teacher and Student Networks3m

Knowledge Distillation Techniques9m

Case Study - How to Distill Knowledge for a Q&A Task8m

4 lecturas

Ungraded Lab - Distributed Strategies with TF and Keras1h

High-Performance Modeling5m

Ungraded Lab - Knowledge Distillation1h

Knowledge Distillation 2m

2 ejercicios de práctica

High-Performance Modeling20m

Knowledge Distillation 20m

Semana

Semana 4

6 horas para completar

Week 4: Model Analysis

Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.

6 horas para completar

12 videos (Total 69 minutos), 8 lecturas, 5 cuestionarios

12 videos

Model Performance Analysis7m

Introduction to TensorFlow Model Analysis6m

TFMA in Practice3m

Model Debugging Overview3m

Benchmark Models1m

Sensitivity Analysis and Adversarial Attacks9m

Adversarial Attack Demo4m

Residual Analysis2m

Model Remediation4m

Fairness4m

Measuring Fairness 5m

Continuous Evaluation and Monitoring14m

8 lecturas

TensorBoard3m

TensorFlow Model Analysis3m

Ungraded Lab - Tensorflow Model Analysis1h

Ungraded Lab - Model Analysis with TFX Evaluator1h

Sensitivity Analysis and Adversarial Attacks3m

Ungraded Lab - Fairness Indicators1h

Model Remediation and Fairness5m

Continuous Evaluation and Monitoring5m

3 ejercicios de práctica

Model Analysis8m

Model Analysis and Debugging18m

Continuous Evaluation and Monitoring8m

Reseñas

4.4

42 reseña

5 stars
68,54 %
4 stars
18,14 %
3 stars
6,45 %
2 stars
4,43 %
1 star
2,41 %

Principales reseñas sobre MACHINE LEARNING MODELING PIPELINES IN PRODUCTION

por MB20 de oct. de 2021

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

por A14 de jun. de 2022

There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.

por SM7 de jun. de 2022

The assignments are just quizes, and no practical programming exercise

por JZ3 de abr. de 2022

Lots of hands-on exercises accompanying knowledge learned in this course 3, but could be difficult for someone without prior working knowledge on Google Cloud platform/services.

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.

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Machine Learning Modeling Pipelines in Production