Machine Learning Architecture Engineering Production Systems Through Iterative Design(1st Edition)

Book details

Book Price $0 : Machine Learning Architecture: From Development To Production - A Holistic ApproachIn Today's Landscape, Machine Learning Systems Face A Dual Challenge: They Are Inherently Complex, Consisting Of Multiple Interconnected Components, And Uniquely Data-dependent, With Data Varying Dramatically Across Use Cases. This Book Presents A Comprehensive Framework For Designing And Deploying ML Systems That Don't Just Work In Theory, But Thrive In Production.Through Real-world Case Studies And Practical Examples, You'll Master:Data Engineering: Build Robust Pipelines And Select Metrics That Align With Business ObjectivesProduction Monitoring: Design Sophisticated Systems That Detect And Address Issues ProactivelyPlatform Architecture: Build Flexible ML Platforms That Serve Diverse Use Cases While Maintaining ReliabilityMLOps Integration: Implement Continuous Development, Evaluation, And Deployment ProcessesCore Focus AreasThe Iterative Design Approach Helps You Tackle:Moving Beyond Development Accuracy To Production StabilityBuilding Systems That Scale With Growing Data And User DemandsImplementing Robust Monitoring And Maintenance StrategiesAdapting To Changing Business Requirements And Data DistributionsThis Guide Is Essential For ML Engineers, Technical Leaders, And Data Scientists Seeking To Bridge The Gap Between Model Development And Production Deployment. Each Chapter Combines Theoretical Foundations With Practical Implementation, Ensuring You Can Transform Theoretical Models Into Production-ready Systems.Transform Your ML Projects From Development Success To Production Excellence. Master The Art Of Building Systems That Are Reliable, Scalable, Maintainable, And Adaptive To Real-world Challenges. See more