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MLOps Platforms: Amazon SageMaker and Azure ML · LearnSpace
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MLOps Platforms: Amazon SageMaker and Azure ML

Курс от Duke University
Продвинутый≈ 31.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning. Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology. Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation. Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient. Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models. Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model. Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML.

Навыки, которые вы освоите

MLOps (Machine Learning Operations)Amazon Web ServicesAWS SageMakerExploratory Data AnalysisModel DeploymentArtificial Intelligence and Machine Learning (AI/ML)Data EngineeringData PreprocessingData AnalysisMachine LearningFeature EngineeringModel TrainingCloud DeploymentPython ProgrammingApplied Machine LearningData PipelinesMachine Learning MethodsCloud SolutionsMicrosoft AzureModel Evaluation

Программа курса

5 модулей · 147 учебных материалов

01Data Engineering with AWS Technology38 материалов

About the Course

Meet your Course Instructor: Noah GiftВидеоMeet your Supporting Instructor: Alfredo DezaЧтениеCourse Structure and Discussion EtiquetteЧтениеMeet and Greet (optional)Обсуждение

Учитесь у экспертов

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Alfredo Deza

Adjunct Assistant Professor in the Pratt School of Engineering

MLOps Platforms: Amazon SageMaker and Azure ML
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Обучение на Coursera

≈ 31.2 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Getting Started and Course GotchasЧтение
Report a problem with the course Чтение

Getting Started with AWS Machine Learning Technology

Key TermsЧтениеWelcome to AWS Academy Machine Learning FoundationsЧтениеStudio Lab ExamplesЧтениеUsing Sagemaker Studio LabВидеоGetting Started with AWS CloudShellВидеоAdvantages of Using Cloud Developer WorkspacesВидеоPrototyping AI APIs in CloudShellВидеоCloud9 with AWS Codewhisperer AI Pair Programming ToolВидеоAWS Academy Onboard (Optional)ЧтениеLesson ReflectionЧтениеQuiz-Getting Started with AWS Machine Learning TechnologyЗадание

Creating Data Repositories for Machine Learning

Key TermsЧтениеDeveloping AWS Storage SolutionsЧтениеIntroduction to Data StorageВидеоDetermining the Correct Storage MediumВидеоWorking with Amazon S3ВидеоData Lakes with Amazon S3ЧтениеLesson ReflectionЧтениеQuiz-Create Data Repository for Machine LearningЗадание

Identifying and Implementing Data Ingestion and Transformation Solutions

Key TermsЧтениеBatch vs. Streaming Job StylesВидеоIntroduction to Data Ingestion and Processing PipelinesВидеоWorking with AWS BatchВидеоWorking with AWS Step FunctionsВидеоTransforming Data in TransitВидеоHandling Map Reduce for Machine LearningВидеоWorking with EMR ServerlessВидеоInteractive Marco Polo Pipeline Programming ChallengeЧтениеBuild and Deploy a Marco Polo AWS Step FunctionЛабораторнаяData Engineering with AWS Machine Learning TechnologyЗаданиеLesson ReflectionЧтениеQuiz-Identifying and Implementing Data Ingestion and Transformation SolutionsЗадание
02Exploratory Data Analysis with AWS Technology23 материалов

Sanitizing and Preparing Data for Modeling

Key TermsЧтениеAWS Academy Introduction to Machine LearningЧтениеCleaning Up DataВидеоScaling DataВидеоLabeling DataВидеоAWS Resources for Exploratory Data AnalysisЧтениеLesson ReflectionЧтениеQuiz-Sanitizing and Preparing Data for ModelingЗаданиеJupyter SandboxЛабораторная

Performing Feature Engineering

Key TermsЧтениеIdentifying and Extracting FeaturesВидеоFeature Engineering ConceptsВидеоFeature engineering with scikit-learn on DatabricksЧтениеFeature Engineering-Creating a Winning SeasonЛабораторнаяLesson ReflectionЧтение

Analyzing and Visualizing Data for Machine Learning

Key TermsЧтениеGraphing DataВидеоClustering DataВидеоCovid19 Exploratory Data AnalysisЛабораторнаяClustering and Plotting Clusters in Housing PricesЛабораторнаяExploratory Data AnalysisЗадание
03Modeling with AWS Technology30 материалов

Selecting the Appropriate Model(s) for a Given Machine Learning Problem

Key TermsЧтениеWhen to Use Machine Learning?ВидеоSupervised vs. Unsupervised Machine LearningВидеоIntroduction to Implementing a Machine Learning Pipeline with Amazon SageMakerЧтениеSelecting a Machine Learning SolutionВидеоLesson ReflectionЧтениеQuiz-Selecting the Appropriate Model(s) for a Given Machine Learning ProblemЗадание

Training Machine Learning Models

Key TermsЧтениеIntroducing Forecasting on SagemakerЧтениеSelecting a Machine Learning ModelВидеоModeling Demo with Sagemaker CanvasВидеоUsing Train, Test and SplitВидеоSolving Optimization ProblemsВидео

Evaluating Machine Learning Problems

Key TermsЧтениеIntroducing Computer VisionЧтениеOverfitting vs. UnderfittingВидеоSelecting MetricsВидеоComparing Models using Experiment TrackingВидеоBuilding a Linear Regression ModelЛабораторная
04MLOps with AWS Technology30 материалов

Building Machine Learning Solutions for Performance, Availability, Scalability, Resilience and Fault

Key TermsЧтениеIntroducing Natural Language ProcessingЧтениеMonitoring and LoggingВидеоInteractive Python LoggingЧтениеPython Logging LabЛабораторнаяMultiple RegionsВидеоReproducible WorkflowsВидеоAWS-Flavored DevOpsВидеоLesson ReflectionЧтениеQuiz-Building Machine Learning SolutionsЗадание

Recommending and Implementing Appropriate Machine Learning Services

Key TermsЧтениеReviewing Compute ChoicesВидеоProvisioning EC2ВидеоProvisioning EBSВидеоAWS AI ML ServicesВидеоMore Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMakerЧтение

Deploying and Operationalizing Secure Machine Learning Solutions

Key TermsЧтениеPrinciple of Least Privilege AWS LambdaВидеоIntegrated SecurityВидеоOverview of Sagemaker Studio WorkflowВидеоModel Predictions with Sagemaker CanvasВидеоData Drift and Model MonitoringВидео
05Machine Learning Certifications26 материалов

Azure AI Fundamentals and other Azure Certifications

Key TermsЧтениеIntroduction to Azure CertificationsВидеоLearning Resources for Azure CertificationsВидеоMicrosoft Learning Paths and Study NotesВидеоCreating an Azure ML WorkspaceВидеоCreating an Azure Auto ML JobВидеоLesson ReflectionЧтениеQuiz-Azure AI Fundamentals and other Azure CertificationsЗадание

Introductory Azure ML and MLOps Concepts

Key TermsЧтениеIntroductory Azure ML and MLOps ConceptsВидеоPrerequisite TechnologyВидеоReal Time and Batch DeploymentВидеоAzure Open DatasetsВидеоExploring Open Datasets SDKВидео

More Advanced Azure ML and MLOps Concepts

Key TermsЧтениеMore Advanced Azure ML and MLOps ConceptsВидеоExploring Azure ML Command LineВидеоTriggering Azure ML with GitHubВидеоUsing HyperparametersВидеоTrain a Model using the Python SDKВидео
Quiz-Feature EngineeringЗадание
Lesson ReflectionЧтение
Selecting GPU vs. CPUВидео
Neural Network ArchitectureВидео
Interactive Gradient Descent Чтение
Gradient Descent SandboxЛабораторная
Lesson ReflectionЧтение
Quiz-Training Machine Learning ModelsЗадание
More Practice: Train an Image Classification Model with PyTorchЧтение
Machine Learning ModelingЗадание
Underfitting vs OverfittingЛабораторная
Lesson ReflectionЧтение
Quiz-Evaluating Machine Learning ProblemsЗадание
Lesson ReflectionЧтение
Quiz-Recommending and Implementing Appropriate Machine Learning ServicesЗадание
Running PyTorch with AWS App RunnerВидео
More Practice: Deploy Models for InferenceЧтение
Getting Started with MLOpsЗадание
AWS Certified Machine Learning – SpecialtyЧтение
External Lab: MLOps Template GitHubЧтение
Lesson ReflectionЧтение
Lesson ReflectionЧтение
Quiz-Introductory Azure ML and MLOps ConceptsЗадание
Tutorial: Azure Machine Learning in a DayЗадание
Lesson ReflectionЧтение
Next StepsЧтение
Share your learning experienceЧтение