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Building and Scaling ML Pipelines · LearnSpace
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courseraПрограммирование

Building and Scaling ML Pipelines

Курс от Edureka
Средний≈ 7.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Feast, Great Expectations, Kubernetes, Kubeflow, Optuna, Ray, MLflow, BentoML, KServe, ONNX, and autoscaling. By the end of this course, you will be able to: - Build and validate production-ready feature engineering pipelines - Manage batch, streaming, online, and offline features using Feast - Run scalable training workflows with Kubernetes and Kubeflow Pipelines - Serve models using BentoML, Seldon, and KServe - Apply canary releases and A/B testing to deploy new model versions safely This course is designed for ML engineers, AI engineers, data scientists, DevOps professionals, and software developers who want to scale machine learning operations and deliver dependable models in cloud-native environments. A foundational understanding of MLOps, Python, machine learning, Docker, and deployment concepts is recommended.

Навыки, которые вы освоите

KubernetesData ValidationFeature EngineeringMLOps (Machine Learning Operations)Machine LearningRandom Forest AlgorithmPython ProgrammingArtificial Intelligence and Machine Learning (AI/ML)DevOpsScalabilityDistributed ComputingData PipelinesCloud-Native ComputingDocker (Software)Data ScienceCI/CDMachine Learning AlgorithmsAI WorkflowsModel TrainingArtificial Intelligence

Программа курса

4 модулей · 59 учебных материалов

01Feature Engineering Pipelines and Feature Stores20 материалов

Feature Engineering Pipelines for Production

Specialization OverviewВидеоCourse IntroductionВидеоCourse SyllabusЧтениеFeature Engineering for ML SystemsВидео

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Edureka

Преподаватель курса

Building and Scaling ML Pipelines
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 7.4 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Demonstration: Build a Feature Engineering Pipeline for Fraud DetectionВидео
Demonstration: Validate Feature Pipelines with Great ExpectationsВидео
Designing Reliable Feature Pipelines for Production ML SystemsЧтение
Practice Quiz: Feature Engineering Pipelines for ProductionЗадание

Introduction to Feature Stores with Feast

Understanding Feature Stores and Their Role in MLOpsВидеоDemonstration: Set Up Feast Feature Store for Fraud DetectionВидеоDemonstration: Register and Serve Features with FeastВидеоManaging Offline and Online Features in Production ML SystemsЧтениеPractice Quiz: Introduction to Feature Stores with FeastЗадание

Streaming and Real-Time Features

Event-Driven Feature Engineering for ML ApplicationsЧтениеBatch Features and Streaming FeaturesВидеоDemonstration: Build Real Time Features for Fraud DetectionВидеоDemonstration: Serve Real Time Features to a Model APIВидеоPractice Quiz: Streaming and Real-Time FeaturesЗадание

Module Wrap-Up and Assessment

Module Summary: Feature Engineering Pipelines and Feature StoresЧтениеKnowledge Check: Feature Engineering Pipelines and Feature StoresЗадание
02Scalable Training and Workflow Orchestration on Kubernetes18 материалов

Kubernetes Fundamentals for MLOps

Introduction to KubernetesВидеоDemonstration:Set Up a Kubernetes Cluster for ML WorkflowsВидеоDemonstration: Running a Training Job on KubernetesВидеоKubernetes Resource Management for ML TrainingЧтениеPractice Quiz: Kubernetes Fundamentals for MLOpsЗадание

Building Pipelines with Kubeflow

Kubeflow Pipelines and Cloud-Native ML OrchestrationВидеоDemonstration: Building a Kubeflow Pipeline for Fraud Detection SystemВидеоDemonstration: Manage Kubeflow Pipeline RunsВидеоPipeline Components and Reusability in MLOpsЧтениеKnowledge Check: Building Pipelines with KubeflowЗадание

Distributed Training and Hyperparameter Optimization at Scale

Distributed Training and Large-Scale Hyperparameter TuningВидеоDemonstration: Scale Hyperparameter Tuning with Optuna and RayВидеоDemonstration: Track Distributed Tuning Experiments with MLflowВидеоManaging Experiment Scale and Reproducibility in Distributed ML WorkflowsЧтениеPractice Quiz: Distributed Training and TuningЗадание

Module Wrap-Up and Assessment

Building Scalable Feature Pipelines and Kubernetes Based ML WorkflowsDIALOGUEModule Summary: Scalable Training and Workflow Orchestration on KubernetesЧтениеKnowledge Check: Scalable Training and Workflow Orchestration on KubernetesЗадание
03Advanced Model Serving and Deployment Patterns17 материалов

Production-Grade Model Serving

Model Serving Frameworks – BentoML, Seldon, and KserveВидеоDemonstration: Serve a Fraud Model with BentoMLВидеоDemonstration: Deploy a Model with KServeВидеоModel Serving Architecture: From Model Artifact to Prediction APIЧтениеPractice Quiz: Production-Grade Model ServingЗадание

Advanced Deployment Strategies

Deployment Patterns for ML ModelsВидеоDemonstration: Implement Canary Deployment for a Fraud ModelВидеоDemonstration: Running A/B Tests for Model VersionsВидеоModel Rollback Strategies in Production MLЧтениеPractice Quiz: Advanced Deployment StrategiesЗадание

Inference Optimization and Autoscaling

Latency, Throughput, and Cost Optimization for ML InferenceВидеоDemonstration: Optimize Model Inference with ONNXВидеоDemonstration: AutoScale a Model Service on Kubernetes ВидеоBalancing Speed, Cost, and Accuracy in ML InferenceЧтениеPractice Quiz: Inference Optimization and AutoscalingЗадание

Module Wrap-Up and Assessment

Module Summary: Advanced Model Serving and Deployment PatternsЧтениеKnowledge Check: Advanced Model Serving and Deployment PatternsЗадание
04Course Wrap-Up and Assessment4 материалов

Course Conclusion

Practice Project: Building a Production-Ready LLMOps Pipeline ЧтениеGuiding an ML Engineer Struggling to Productionize an ML SystemDIALOGUEEnd Course Knowledge Check: Building and Scaling ML PipelinesЗаданиеCourse SummaryВидео