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

4.6
estrellas
236 calificaciones

Laurence Moroney +1 instructores más

19.539 ya inscrito

Ofrecido Por

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

Acerca de este Curso

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In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally,  you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.  
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: Model Serving Introduction
Week 2: Model Serving Patterns and Infrastructures
Week 3: Model Management and Delivery
Week 4: Model Monitoring and Logging

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 4 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. 31 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Habilidades que obtendrás

TensorFlow Serving
Model Monitoring
Model Registries
Machine Learning Operations (MLOps)
Generate Data Protection Regulation (GDPR)

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 4 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. 31 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,49/5 (64 calificaciones)

Laurence Moroney

Instructor

Lead AI Advocate, Google

408.637 alumnos

15 cursos

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

36.697 alumnos

3 cursos

Ofrecido por

deeplearning.ai

Mira cómo los empleados de las mejores empresas dominan las habilidades más demandadas

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Programa - Qué aprenderás en este curso

Semana

Semana 1

4 horas para completar

Week 1: Model Serving: Introduction

4 horas para completar

7 videos (Total 34 minutos), 5 lecturas, 5 cuestionarios

7 videos

Course Overview4m

Introduction to Model Serving6m

Introduction to Model Serving Infrastructure5m

Deployment Options3m

Improving Prediction Latency and Reducing Resource Costs5m

Creating and deploying models to AI Prediction Platform2m

Installing TensorFlow Serving6m

5 lecturas

Ungraded Labs - Best Practices5m

Ungraded Lab - Introduction to Docker20m

Optional: Build, train, and deploy an XGBoost model on Cloud AI Platform45m

Ungraded Lab - Tensorflow Serving with Docker20m

Ungraded Lab - Serve a model with TensorFlow Serving30m

3 ejercicios de práctica

Introduction to Model Serving30m

Introduction to Model Serving Infrastructure30m

TensorFlow Serving30m

Semana

Semana 2

10 horas para completar

Week 2: Model Serving: Patterns and Infrastructure

10 horas para completar

8 videos (Total 44 minutos), 8 lecturas, 8 cuestionarios

8 videos

Model Serving Architecture4m

Model Servers: TensorFlow Serving3m

Model Servers: Other Providers5m

Scaling Infrastructure10m

Online Inference6m

Data Preprocessing4m

Batch Inference Scenarios5m

Batch Processing with ETL3m

8 lecturas

Documentation on model servers10m

Ungraded Lab - Deploy a ML model with FastAPI and Docker1h

Learn about scaling with boy bands10m

Explore Kubernetes and KubeFlow10m

(Optional) Ungraded Lab: Intro to Kubernetes1h 10m

Ungraded Lab - Latency testing with Docker Compose and Locust45m

Data preprocessing10m

Ungraded Lab (Optional): Machine Learning with Apache Beam and TensorFlow45m

6 ejercicios de práctica

Model serving architecture30m

Scaling Infrastructure30m

Online Inference30m

Data Preprocessing30m

Batch inference scenarios30m

Batch Processing with ETL30m

Semana

Semana 3

11 horas para completar

Week 3: Model Management and Delivery

11 horas para completar

9 videos (Total 82 minutos), 10 lecturas, 6 cuestionarios

9 videos

Experiment Tracking8m

Tools for Experiment Tracking7m

Introduction to MLOps11m

MLOps Level 0 5m

MLOps Levels 1&213m

Developing Components for an Orchestrated Workflow13m

Managing Model Versions7m

Continuous Delivery7m

Progressive Delivery7m

10 lecturas

Experiment Tracking10m

MLOps Resources10m

Ungraded Lab: Intro to Kubeflow Pipelines2h

Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build10m

Ungraded Lab: Developing TFX Custom Components45m

Ungraded Lab - Model Versioning with TF Serving40m

ML Model Management10m

Ungraded Lab - CI/CD pipelines with GitHub Actions1h

Continuous Delivery10m

Progressive Delivery10m

3 ejercicios de práctica

ML Experiments Management and Workflow Automation30m

MLOps Methodology30m

Model Management and Deployment Infrastructure30m

Semana

Semana 4

6 horas para completar

Week 4: Model Monitoring and Logging

6 horas para completar

13 videos (Total 66 minutos), 7 lecturas, 5 cuestionarios

13 videos

Why Monitoring Matters6m

Observability in ML4m

Monitoring Targets in ML4m

Logging for ML Monitoring7m

Tracing for ML Systems3m

What is Model Decay?3m

Model Decay Detection2m

Ways to Mitigate Model Decay5m

Responsible AI5m

Legal Requirements for Secure and Private AI9m

Anonymization and Pseudonymisation 4m

Right to be Forgotten8m

Specialization recap and farewell1m

7 lecturas

Monitoring Machine Learning Models in Production10m

Addressing Model Decay10m

Responsible AI10m

GDPR and CCPA10m

Course 4 Optional References10m

Acknowledgements10m

(Optional) Opportunity to Mentor Other Learners10m

3 ejercicios de práctica

Model Monitoring and Logging30m

Model Decay30m

GDPR and Privacy30m

Reseñas

4.6

35 reseña

5 stars
71,72 %
4 stars
20,25 %
3 stars
3,37 %
2 stars
2,53 %
1 star
2,10 %

Principales reseñas sobre DEPLOYING MACHINE LEARNING MODELS IN PRODUCTION

por VL16 de may. de 2022

It's intense, applied, concrete and to the point. A very good course.

por KM19 de dic. de 2022

The part I enjoyed most about this course is its real-life projects which one can apply directly in business scenarios

por WH10 de sep. de 2021

The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

por EB5 de feb. de 2022

Really enjoyed it however to get he most out of it, the time commitment is large

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

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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.
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Deploying Machine Learning Models in Production

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Habilidades que obtendrás