MLOps (Machine Learning Operations)

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Data Science Foundations: Building Scalable, Production-Ready Solutions

★ 4.8 (813)

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MLOps Foundations: Build and Deploy Machine Learning Pipelines

★ 4.2 (610)

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3 700 ֏ →

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MLOps Foundations: Build, Deploy, and Monitor Production ML Pipelines

★ 4.0 (485)

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3 700 ֏ →

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Machine Learning Model Deployment with Flask

★ 4.3 (456)

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3 700 ֏ →

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Cloud Machine Learning with TensorFlow: End-to-End Pipelines

★ 4.6 (448)

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3 700 ֏ →

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Building Machine Learning Models in Azure

★ 4.5 (331)

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3 700 ֏ →

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AI and Machine Learning on Cloud Platform: Foundations and Solutions

★ 4.6 (328)

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Azure Machine Learning: No-Code AI Fundamentals

★ 4.4 (296)

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3 700 ֏ →

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AWS SageMaker Fundamentals for Machine Learning

★ 4.4 (264)

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3 700 ֏ →

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DevOps, DataOps, and MLOps for AI Applications

★ 4.1 (222)

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3 700 ֏ →

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Fundamentals of Machine Learning Pipelines

★ 4.5 (221)

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3 700 ֏ →

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MLOps Fundamentals: Building Reliable Machine Learning Pipelines

★ 4.5 (190)

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3 700 ֏ →

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Azure Machine Learning Foundations for Data Scientists

★ 4.3 (178)

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Machine Learning with TensorFlow on Cloud Platforms

★ 4.6 (159)

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3 700 ֏ →

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Machine Learning with TensorFlow on the Cloud

★ 4.4 (152)

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3 700 ֏ →

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MLOps and Big Data Foundations: Deploying Machine Learning Models

★ 4.7 (150)

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3 700 ֏ →

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Cloud Data Engineering: Big Data and Machine Learning Fundamentals

★ 4.7 (126)

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3 700 ֏ →

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AWS Machine Learning Solutions: From Lifecycle to MLOps

★ 4.5 (119)

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3 700 ֏ →

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Machine Learning with Apache Spark for Data Pipelines

★ 4.5 (114)

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3 700 ֏ →

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Machine Learning Engineering with SAS

★ 4.7 (105)

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3 700 ֏ →

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Cloud Machine Learning Engineering and MLOps Fundamentals

★ 4.5 (92)

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3 700 ֏ →

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Machine Learning with TensorFlow on the Cloud

★ 4.7 (92)

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3 700 ֏ →

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Data Science Practitioner Fundamentals

★ 4.7 (83)

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3 700 ֏ →

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Azure Machine Learning: Building and Managing Solutions

★ 3.7 (71)

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3 700 ֏ →