К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Practical Machine Learning on Databricks · LearnSpace
Назад в каталог
courseraАнализ данных

Practical Machine Learning on Databricks

Курс от Packt
Продвинутый≈ 6.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Machine learning teams today must build scalable, production-ready workflows that move seamlessly from experimentation to deployment and monitoring. This course provides a practical introduction to machine learning on Databricks, helping professionals leverage modern tools such as AutoML, MLflow, Feature Store, and serverless deployment to streamline ML operations and accelerate business outcomes. Through hands-on examples and real-world scenarios, you will learn how to create baseline models, manage feature engineering workflows, automate ML pipelines, and deploy models efficiently using Databricks. The course also explores model versioning, workflow orchestration, CI/CD automation, and model drift detection to help you maintain reliable and scalable machine learning systems in production environments. What sets this course apart is its strong focus on practical implementation using Databricks-native tools and workflows. By combining foundational ML concepts with enterprise-ready deployment and automation strategies, the course prepares you to tackle modern MLOps and machine learning engineering challenges with confidence. This course is ideal for data scientists, ML engineers, data engineers, and developers looking to transition to Databricks-based machine learning workflows. Learners should have prior experience with Python, machine learning concepts, and familiarity with Apache Spark fundamentals.

Навыки, которые вы освоите

CI/CDModel DeploymentMLOps (Machine Learning Operations)Feature EngineeringData SciencePython ProgrammingMachine Learning SoftwareAI WorkflowsModel EvaluationMachine Learning AlgorithmsStatistical Machine LearningApache SparkModel TrainingDatabricksData StoreData LakesApplied Machine LearningCloud DeploymentDevelopment EnvironmentMachine Learning

Программа курса

10 модулей · 58 учебных материалов

01ML Process and Challenges7 материалов

Navigating Enterprise ML: Roles, Platforms, and Production Challenges

OverviewВидеоIntroductionЧтениеDiscovering the Roles Associated with Machine Learning Projects in OrganizationsЧтениеUnderstanding the Requirements of an Enterprise-Grade Machine Learning PlatformЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Practical Machine Learning on Databricks
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 6.7 ч

10 модулей

Язык: Английский

Часть программы вашего университета
Exploring Databricks and the Lakehouse ArchitectureЧтение
Ease of Use Balancing Complexity and UsabilityЧтение
Data Science and Machine Learning FundamentalsЗадание
02Overview of ML on Databricks7 материалов

Launching Your ML Journey: Workspaces, Clusters, and MLOps Essentials on Databricks

OverviewВидеоIntroductionЧтениеExploring ClustersЧтениеSingle-node ClustersЧтениеExploring ExperimentsЧтениеDiscovering the Feature StoreЧтениеExploring Machine Learning on DatabricksЗадание
03Utilizing Feature Store5 материалов

Mastering Feature Management with Databricks

OverviewВидеоIntroductionЧтениеOffline StoreЧтениеRegistering Your First Feature Table in Databricks Feature StoreЧтениеExploring Feature Store FundamentalsЗадание
04Understanding MLflow Components6 материалов

Mastering Experiment Tracking and Model Management with MLflow on Databricks

OverviewВидеоIntroductionЧтениеMLflow TrackingЧтениеMLflow ModelsЧтениеExample Code Showing How to Track ML Model Training in DatabricksЧтениеMLflow Ecosystem OverviewЗадание
05Create a Baseline Model for Bank Customer Churn Prediction Using AutoML5 материалов

Building and Evaluating Baseline Churn Models with Databricks AutoML

OverviewВидеоIntroductionЧтениеImbalance Data DetectionЧтениеRunning AutoML on Our Churn Prediction DatasetЧтениеAutoML in Bank Customer Churn PredictionЗадание
06Model Versioning and Webhooks4 материалов

Automating Model Management with Versioning and Webhooks

OverviewВидеоIntroductionЧтениеDiving into the Webhooks Support in the Model RegistryЧтениеModel Lifecycle and Webhook ManagementЗадание
07Model Deployment Approaches6 материалов

Mastering Model Deployment: From Batch Processing to Real-Time Inference

OverviewВидеоIntroductionЧтениеDeploying ML Models for Batch and Streaming InferenceЧтениеDeploying ML Models for Real-Time InferenceЧтениеIncorporating Custom Python Libraries into MLflow Models for Databricks DeploymentЧтениеModel Deployment FundamentalsЗадание
08Automating ML Workflows Using the Databricks Jobs4 материалов

Streamlining Machine Learning Pipelines with Databricks Automation

OverviewВидеоIntroductionЧтениеUtilizing Databricks Workflows with Jobs to Automate Model Training and TestingЧтениеAutomating Machine Learning with DatabricksЗадание
09Model Drift Detection for Our Churn Prediction Model and Retraining8 материалов

Safeguarding Model Performance: Detecting and Addressing Drift in Production

OverviewВидеоIntroductionЧтениеIntroduction to Model DriftЧтениеIntroduction to Statistical DriftЧтениеDefining CharacteristicsЧтениеChi-squared TestЧтениеImplementing Drift Detection on DatabricksЧтениеModel Drift and Statistical AnalysisЗадание
10CI/CD to Automate Model Retraining and Re-Deployment.6 материалов

Automating the ML Lifecycle: CI/CD, MLOps, and Deployment Strategies

OverviewВидеоIntroductionЧтениеComprehensive Model Management with Databricks MLflowЧтениеFundamentals of MLOps and Deployment PatternsЧтениеUnderstanding ML Deployment PatternsЧтениеCI/CD and MLOps FundamentalsЗадание