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Cloud Machine Learning Engineering and MLOps · LearnSpace
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courseraIT и технологии

Cloud Machine Learning Engineering and MLOps

Курс от Duke University
Средний≈ 13.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Application Programming Interface (API)Continuous DeliveryApplied Machine LearningMLOps (Machine Learning Operations)Microsoft AzureAI WorkflowsModel TrainingCloud DeploymentMachine LearningGoogle Cloud PlatformCloud ApplicationsComputer VisionModel DeploymentCloud APISoftware EngineeringCloud Engineering

Программа курса

3 модулей · 86 учебных материалов

01Getting Started with Machine Learning Engineering26 материалов

Welcome to the the Course!

Instructor IntroductionВидеоCourse IntroductionВидеоLab OnboardingВидеоSpecialization Project Roadmap: Course 4Чтение

Учитесь у экспертов

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Cloud Machine Learning Engineering and MLOps
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 13.8 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Ория, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Часть программы вашего университета
Course 4 Project OverviewВидео
Course Structure and Discussion EtiquetteЧтение
IntroductionsОбсуждение
Report a problem with the courseЧтение

What is Machine Learning Engineering?

Introduction to Machine Learning EngineeringВидеоMachine Learning Engineering OverviewВидеоMachine Learning Engineering ArchitectureВидеоJupyter Notebook Workflow for Machine LearningЧтениеK-Means Clustering Sample DatasetЧтение

Build Machine Learning Microservices

Introduction to Machine Learning MicroservicesВидеоMachine Learning Microservices OverviewВидеоMonolithic versus MicroserviceВидеоMicroservices in MLOpsОбсуждение

Continuous Delivery for Machine Learning

Introduction to Continuous Delivery for Machine LearningВидеоContinuous Delivery for Machine Learning OverviewВидеоWhat is Data Drift?ВидеоContinuously Deploy Flask ML ApplicationВидеоAWS App Runner: High-Level PaaS Continuous DeliveryВидеоFlask Machine Learning MicroserviceЛабораторнаяPaaS (Platform as a Service) and MLOPsОбсуждение

Applied Practice

High Level MLOps Continuous DeploymentЧтение

Graded Assignment

QuizЗадание
02Using AutoML27 материалов

What is AutoML?

Introduction to AutoMLВидеоWhat is AutoML?ВидеоAutoML Computer VisionВидеоIntroduction to No Code/Low CodeВидеоNo Code/Low Code AutoML: Part 1ВидеоNo Code/Low Code AutoML: Part 2ВидеоApple Create ML AutoMLВидеоManaged Machine Learning SystemsЧтениеImpact of AutoML?Обсуждение

Ludwig AutoML

Introduction to Ludwig AutoMLВидеоWhat is Ludwig AutoML?ВидеоLudwig AutoML Deep DiveВидеоLudwig AutoML By ExampleВидеоOpen Source AutoMLОбсуждение

Cloud AutoML

Introduction to Cloud AutoMLВидеоWhat is Cloud AutoML?ВидеоCloud AutoML Deep DiveВидеоGuest Speaker: Alfredo DezaВидеоIntroduction to Azure Machine Learning StudioВидеоCreate a Dataset in Azure Machine Learning StudioВидео

Applied Practice

Use Apple's AutoML Computer VisionЧтение

Graded Assignment

QuizЗадание
03Emerging Topics in Machine Learning33 материалов

What is MLOps?

Introduction to MLOpsВидеоWhat is MLOps?ВидеоMLOps Deep DiveВидеоPickle an ML ModelЛабораторнаяWhy MLOps?Обсуждение

Using Edge Machine Learning

Introduction to Edge Machine LearningВидеоWhat is Edge Machine Learning?ВидеоEdge Machine Learning Vision in ActionВидеоHardware Inference Model Solutions in Edge Machine LearningВидеоEdge Machine Learning in GoogleВидеоEdge Machine Learning in AWSВидеоEdge Machine LearningОбсуждение

Using AI APIs

Introduction to AI APIsВидеоHow to Use AI APIs?ВидеоCore Components of a Cloud ApplicationВидеоAWS Comprehend for Natural Language ProcessingВидеоAWS Rekognition for Computer VisionВидеоGCP AutoML for Natural Language ProcessingВидео

Building a Professional Web Service

Steps to Developing an APIВидеоFlask Machine Learning BackendВидеоChecklist for Building Professional Web ServicesВидеоStandards of Excellence in Software EngineeringОбсуждение

Applied Practice

Deep Dive: Use a Low Code or No Code Cloud AI API to Solve a ProblemЧтение

Graded Assignment

QuizЗадание

Putting it all Together: Final Course Project

Deploy a Flask Machine Learning Model That You Didn't BuildЧтениеNext StepsЧтениеInteractive Llamafile SandboxЛабораторнаяShare your learning experienceЧтение
Automated ML Run in Azure Machine Learning StudioВидео
Experiments in Azure Machine Learning StudioВидео
Deploy a Module in Azure Machine Learning StudioВидео
Test Endpoints in Azure Machine Learning StudioВидео
ML Studio ProductsОбсуждение
GCP AutoML for Computer VisionВидео
Azure AutoML for AI PredictionsВидео
Azure AutoML for Computer VisionВидео
Core Components of a Cloud Application RecapВидео
No Code and Low Code SolutionsОбсуждение