ML Model Deployment Crash Course 2023. All you need to know
ML Model Deployment Crash Course 2023. All you need to know
Published 5/2023
Created by Dima Havrylenko
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 5 Lectures ( 1h 3m ) | Size: 612 MB
Machine Learning Model Deployment Course.
What you'll learn
The basics of ML Model Deployment
Best Practices of Deploying ML Models
Common mistakes people make when deploying ML Models
Monitoring and Maintenance of ML Models
Requirements
It's a crash course that is intended to inform you not he topic, provide you with basic knowledge and understanding.
Description
Chapter 1: the Basics of ML Model DeploymentChapter 2: Preparing Your ML Model for DeploymentChapter 3: Deployment OptionsChapter 4: Deployment Tools and FrameworksChapter 5: Monitoring and Maintaining Your Deployed ModelChapter 6: Best Practices for Ml Model DeploymentChapter 7: Continuous Integration and Continuous Deployment (Ci/cd) for MLChapter 8: Data and Feature Engineering for DeploymentChapter 9: Experiment Tracking and Hyperparameter OptimisationChapter 10: Ethical Considerations in Ml Model DeploymentChapter 11: Model Interpretation and DebuggingChapter 12: Collaboration and CommunicationChapter 13: Cost-effective DeploymentChapter 14: Post-deployment StrategiesChapter 15: Deployment in Multi-cloud and Hybrid EnvironmentsChapter 16: Deploying ML Models With MLOpsChapter 17: Ml Model Deployment in Industry-specific ContextsChapter 18: Deploying ML Models on Edge DevicesChapter 19: Open-source and Commercial Deployment ToolsChapter 20: Model Monitoring and MaintenanceChapter 21: Ethics and Responsibility in ML DeploymentChapter 22: Future Trends in ML DeploymentChapter 23: Deploying Machine Learning Models in Low-resource EnvironmentsChapter 24: Deploying ML Models in Multi-tenant EnvironmentsChapter 25: Model Interpretation and DebuggingChapter 26: Collaboration and Teamwork in ML DeploymentChapter 27: Post-deployment Evaluation and FeedbackChapter 28: Training and Updating ML Models in ProductionChapter 29: Deploying Reinforcement Learning ModelsChapter 30: ML Deployment and the Internet of Things (Iot)Chapter 31: ML Deployment in HealthcareChapter 32: ML Deployment in FinanceChapter 33: ML Deployment in Retail and E-commerceChapter 34: ML Deployment in Manufacturing and Industrial Applications
HOMEPAGE
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