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Django for Data Science and AI Projects: A Practical Guide to Deploying AI Models and Data Workflows on the Web
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.82 kg
0.82 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Modern AI and data science workflows don’t end in Jupyter notebooks—they’re deployed to the web. Django, a high-level Python web framework, empowers developers to transition from experimentation to real-world AI-powered applications. Its mature architecture, extensibility, and security make it a top choice for building machine learning dashboards, intelligent APIs, and data-driven platforms at scale.Whether you're building a sentiment classifier, AI-powered recommendation engine, or a real-time analytics dashboard, Django provides the backbone to turn models into products.Written by a professional engineer with experience in deploying AI systems and building scalable Django platforms, this book bridges the critical gap between data science and production-grade web applications. Drawing from industry best practices and real-world projects, every chapter delivers actionable knowledge, not just theory.You’ll learn how to combine the robustness of Django with powerful tools like scikit-learn, Hugging Face, Docker, Celery, and Django REST Framework to build full-stack AI services—faster, safer, and smarter.Django for Data Science and AI Projects is your end-to-end practical guide to building intelligent, interactive, and scalable web applications powered by machine learning models. Covering the complete AI project lifecycle—from training and inference to deployment and monitoring—this book empowers you to confidently ship data science solutions to the web using modern DevOps and MLOps practices.Whether you're integrating a model with DRF endpoints or visualizing data with Plotly, this book teaches you how to go from prototype to production using proven, scalable patterns.Setting up Django for ML and data science workflowsIngesting and cleaning datasets via user-friendly web formsVisualizing predictions and charts using Django templates and PlotlyDeploying and serving models (scikit-learn, transformers, ONNX)Creating REST APIs for AI inference using Django REST FrameworkImplementing Celery for background tasks and real-time model callsManaging secrets, Docker containers, and environment configsEnsuring data security, privacy (GDPR, HIPAA), and error handlingCI/CD pipelines, testing ML endpoints, and model version controlIntegration with OpenAI, Hugging Face, and other cloud-based AI APIsThis book is ideal for:Data scientists who want to deploy their models beyond notebooksMachine learning engineers seeking production-ready Django workflowsPython developers interested in AI-powered web appsFull-stack engineers aiming to connect ML models to user-facing dashboards or APIsAnyone moving from Flask, FastAPI, or Streamlit into the Django ecosystemSome prior experience with Python and basic ML is recommended. Django beginners will find the early chapters beginner-friendly and well-paced.Don't spend months cobbling together AI app workflows from scratch. With this practical guide, you’ll learn how to integrate, deploy, and scale ML models in Django—all in a matter of weeks, not years. The book is written for fast iteration and confident delivery, with examples and layouts you can adapt instantly to your projects.Deploy your AI models like a pro—using Django. Whether you’re shipping a proof-of-concept or launching a SaaS product, this guide gives you the tools, architecture, and confidence to bring your data science projects to life on the web. Buy now and start building intelligent Django web apps today.
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U$S 48,73
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