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Databricks Machine Learning Fundamentals · LearnSpace
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Databricks Machine Learning Fundamentals

Курс от Coursera
Средний≈ 4.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In this course, you will learn the fundamentals of using Databricks for machine learning. You will tackle the challenge of disjointed tools and master production-grade machine learning on Databricks. This course guides you through the complete end-to-end ML lifecycle on a single platform, giving you the practical skills to build robust, deployable solutions. You'll start by building a solid data foundation, using Apache Spark to ingest, clean, and engineer high-quality features. Next, master MLOps by using MLflow to systematically track and compare experiments, bringing reproducibility and rigor to your workflow to identify the best model. Finally, close the loop by deploying your models into production. You will use the MLflow Model Registry for versioning and governance before deploying your model as a live, real-time REST API endpoint. Through a series of hands-on labs and a final capstone project, you'll gain the confidence to build, track, and deploy sophisticated ML models, leaving with a portfolio-ready project that makes you a more effective and valuable data professional. This course is designed for intermediate learners who are familiar with basic machine learning concepts and want to learn how to apply them in Databricks for real-world projects. Learners should have a basic understanding of Python, including Pandas and Scikit-learn, along with fundamental machine learning concepts. By the end of this course, learners will be able to apply the full ML lifecycle on the Databricks platform, from data preparation and analysis to model deployment. They will also gain the skills to track experiments and manage models using Databricks and MLflow, ensuring a streamlined, reproducible workflow. Additionally, learners will be equipped to deploy machine learning models effectively using the MLflow Model Registry and Databricks Model Serving.

Навыки, которые вы освоите

MLOps (Machine Learning Operations)DatabricksPySparkEngineeringModel DeploymentMachine LearningFeature EngineeringAI WorkflowsApplication DeploymentData PreprocessingApplied Machine LearningScikit Learn (Machine Learning Library)Apache SparkReal Time DataModel TrainingModel Evaluation

Программа курса

3 модулей · 22 учебных материалов

01Getting Started with Databricks for ML8 материалов
Data in Disarray: The Churn Prediction KickoffDIALOGUEWelcome to the Course: Course OverviewЧтениеWelcome to Databricks Machine Learning FundamentalsВидеоNavigating the Databricks ML WorkspaceВидео

Учитесь у экспертов

Ashish Mohan

Architecting AI/ML & Fintech Solutions | GenAI, Cloud & Digital Ethics Evangelist | Adobe Ex-Microsoft, JP Morgan Chase, Cisco | MS CS

Starweaver

Global Leaders in Professional & Technology Education

Databricks Machine Learning Fundamentals
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 4.6 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Ingesting Data into DatabricksВидео
EDA and Feature Engineering with SparkВидео
Hands-On-Learning: Prepare and Engineer a Churn Dataset for ConnectSphereВзаимная проверка
What is a Data LakehouseЧтение
02Building and Tracking Models with MLflow6 материалов
The Reproducibility Dilemma in ML ExperimentsDIALOGUECore Concepts of MLOps and MLflowВидеоBest Practices for ML Experiment TrackingЧтениеLogging Your First Experiment with MLflowВидеоComparing Runs and Visualizing ResultsВидеоHands-On-Learning: Track and Compare Churn Prediction Models for ConnectSphereВзаимная проверка
03Model Deployment and Management8 материалов
Balancing Act: Delivering Predictions Without DisruptionDIALOGUEIntroduction to the MLflow Model RegistryВидеоRegistering a Model and Managing Its LifecycleВидеоReal-Time Model Serving on DatabricksВидеоHands-On-Learning: Register and Deploy a Churn Prediction Model for ConnectSphereВзаимная проверкаCourse Wrap-UpВидеоProject: Develop and Deploy an End-to-End Fraud Detection System for FinSecureВзаимная проверкаDatabricks Machine Learning FundamentalsЗадание