MLOps Streamlining Machine Learning Operations for CTOs and Software Developers

Crack the MLOps Code: Master Streamlined Machine Learning from Lab to Live
Tired of AI models stuck in limbo? Unlock their full potential with this comprehensive MLOps blueprint. "MLOps" empowers engineering leaders, CTOs, and developers to navigate the complexities of operationalizing machine learning and streamline model workflows from data pipelines to explainability and beyond.
Ditch deployment delays and wasted resources. Discover:Step-by-step frameworks: Master every stage of the ML lifecycle, from data collection to model deployment.Visual guides and best practices: Learn proven strategies for data engineering, model development, explainability, reproducibility, and more.Customizable templates and architectures: Adapt to your unique tech stack and accelerate model rollouts.Debugging advice and integration strategies: Overcome common pitfalls and seamlessly integrate ML into your existing systems.Explore MLOps applications across diverse domains: NLP, recommender systems, fraud detection, time series forecasting, and computer vision. Whether embarking on your first MLOps implementation or pushing towards next-generation AI, this battle-tested guide equips you to:Accelerate ML-powered innovation.Conquer deployment hurdles.Build a robust ML infrastructure.Whether embarking on first implementations or pushing towards next-generation systems, unlock MLOps success with The DX Review's battle-tested playbook.
The DX Review is a leading publication catering to technology executives and engineering leaders across sectors. With their finger on the pulse of emerging tech and changing customer expectations, The DX Review provides actionable insights to help companies adapt quickly. Their in-depth guides draw from cutting-edge use cases, expert perspectives, and original research to map out practical blueprints for digital adoption.
This book covers: MLOps, machine learning operations, model deployment, model monitoring, ML pipelines, ML workflows, AI production, best practices, MLOps templates, debugging, integration strategies, C-suite guide, developer guide.
- machine learning deployment strategies
- putting machine learning into production
- managing machine learning lifecycle
- MLOps for NLP models
- NLP model deployment
- NLP model monitoring
- recommender system MLOps
- recommender system deployment
- fraud detection MLOps
- fraud detection model training
- fraud detection model monitoring
- time series analysis MLOps
- time series forecasting MLOps
- computer vision MLOps
- image classification MLOps
- model reproducibility in MLOps
- MLOps on AWS
- MLOps on Google Cloud
- MLOps on Azure
- CI/CD for machine learning
- DevOps for machine learning
- automated machine learning workflows
- scaling machine learning models
- machine learning in production environments
- machine learning model governance
- machine learning model auditing
- machine learning technical debt
- machine learning system reliability engineering
- end-to-end machine learning pipelines
- machine learning model performance monitoring
- continuous integration machine learning
- continuous delivery machine learning
- DevOps culture for MLOps
- applying site reliability engineering to MLOps
- building high performance machine learning apps
- optimizing machine learning inference
- reducing machine learning technical debt
- creating reproducible machine learning workflows
- CI/CD for deep learning systems
- MLOps on Kubernetes
- anomaly detection in machine learning systems
- securing the machine learning lifecycle