Este curso forma parte de Programa especializado: Building Cloud Computing Solutions at Scale

4.6
estrellas
54 calificaciones

Noah Gift

4945 ya inscrito

Ofrecido Por

Programa especializado: Building Cloud Computing Solutions at ScaleUniversidad Duke

Acerca de este Curso

10.332 vistas recientes

Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs. 
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a Flask web application that serves out Machine Learning predictions.

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Curso 4 de 4 en

Programa especializado: Building Cloud Computing Solutions at Scale

Nivel intermedio

Beginner level Linux and Python skills

Aprox. 12 horas para completar

Inglés (English)

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: Building Cloud Computing Solutions at Scale

Nivel intermedio

Beginner level Linux and Python skills

Aprox. 12 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,64/5 (15 calificaciones)

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

36.856 alumnos

10 cursos

Ofrecido por

Universidad Duke

Programa - Qué aprenderás en este curso

Semana1

Semana 1

4 horas para completar

Getting Started with Machine Learning Engineering

4 horas para completar

15 videos (Total 47 minutos), 5 lecturas, 1 cuestionario

15 videos

Instructor Introduction1mCourse Introduction2mLab Onboarding1mCourse 4 Project Overview1mIntroduction to Machine Learning Engineering30sMachine Learning Engineering Overview1mMachine Learning Engineering Architecture3mIntroduction to Machine Learning Microservices40sMachine Learning Microservices Overview1mMonolithic versus Microservice2mIntroduction to Continuous Delivery for Machine Learning39sContinuous Delivery for Machine Learning Overview1mWhat is Data Drift?2mContinuously Deploy Flask ML Application4mAWS App Runner: High-Level PaaS Continuous Delivery21m

5 lecturas

Specialization Project Roadmap: Course 410mCourse Structure and Discussion Etiquette10mJupyter Notebook Workflow for Machine Learning10mK-Means Clustering Sample Dataset10mHigh Level MLOps Continuous Deployment10m

1 ejercicio de práctica

Week 1 Quiz30m

Semana2

Semana 2

4 horas para completar

Using AutoML

4 horas para completar

21 videos (Total 138 minutos), 2 lecturas, 1 cuestionario

21 videos

Introduction to AutoML50sWhat is AutoML?1mAutoML Computer Vision3mIntroduction to No Code/Low Code4mNo Code/Low Code AutoML: Part 134mNo Code/Low Code AutoML: Part 218mApple Create ML AutoML19mIntroduction to Ludwig AutoML1mWhat is Ludwig AutoML?1mLudwig AutoML Deep Dive2mLudwig AutoML By Example5mIntroduction to Cloud AutoML46sWhat is Cloud AutoML?1mCloud AutoML Deep Dive1mGuest Speaker: Alfredo Deza1mIntroduction to Azure Machine Learning Studio3mCreate a Dataset in Azure Machine Learning Studio10mAutomated ML Run in Azure Machine Learning Studio12mExperiments in Azure Machine Learning Studio3mDeploy a Module in Azure Machine Learning Studio5mTest Endpoints in Azure Machine Learning Studio4m

2 lecturas

Managed Machine Learning Systems10mUse Apple's AutoML Computer Vision10m

1 ejercicio de práctica

Week 2 Quiz30m

Semana3

Semana 3

5 horas para completar

Emerging Topics in Machine Learning

5 horas para completar

22 videos (Total 158 minutos), 2 lecturas, 1 cuestionario

22 videos

Introduction to MLOps38sWhat is MLOps?1mMLOps Deep Dive3mIntroduction to Edge Machine Learning49sWhat is Edge Machine Learning?3mEdge Machine Learning Vision in Action6mHardware Inference Model Solutions in Edge Machine Learning23mEdge Machine Learning in Google29mEdge Machine Learning in AWS16mIntroduction to AI APIs57sHow to Use AI APIs?2mCore Components of a Cloud Application4mAWS Comprehend for Natural Language Processing7mAWS Rekognition for Computer Vision2mGCP AutoML for Natural Language Processing10mGCP AutoML for Computer Vision4mAzure AutoML for AI Predictions16mAzure AutoML for Computer Vision1mCore Components of a Cloud Application Recap34sSteps to Developing an API9mFlask Machine Learning Backend4mChecklist for Building Professional Web Services7m

2 lecturas

Deep Dive: Use a Low Code or No Code Cloud AI API to Solve a Problem10mDeploy a Flask Machine Learning Model That You Didn't Build10m

1 ejercicio de práctica

Week 3 Quiz30m

Reseñas

4.6

11 reseña

5 stars
72,22 %
4 stars
16,66 %
3 stars
9,25 %
2 stars
1,85 %

Principales reseñas sobre CLOUD MACHINE LEARNING ENGINEERING AND MLOPS

por YA7 de feb. de 2022

Amazing teacher and perfect mixture of necessary informations. It was a privilage to learn from him, i recommend this course for every ML Engineer.

por SG8 de jul. de 2021

Excellent course, very concise but complete, if possible a second version would be ideal

por AD31 de oct. de 2022

Great Intro into DevOps and MLOps for beginners, Also good explanation and practical application examples

por JY1 de jun. de 2022

Great course to know practical ideas and concepts.

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Acerca de Programa especializado: Building Cloud Computing Solutions at Scale

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies.

In the first course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines, using the best practices of DevOps. The second course will teach you how to build effective Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP). The third course begins to put together all of the Cloud concepts introduced in the first two courses to tackle more complex data engineering solutions. And finally, in the fourth course you will apply Machine Learning Engineering to build a Flask web application that serves out Machine Learning predictions.

Building Cloud Computing Solutions at Scale

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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Cloud Machine Learning Engineering and MLOps

Acerca de este Curso