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Python Programming: Machine Learning and Data Engineering for Tech Professionals
Stop building prototypes. Start architecting production systems.
Most machine learning resources focus on the "Data Science" side—algorithms, tuning, and accuracy. But in the enterprise, a model is only as valuable as the infrastructure supporting it. Python Machine Learning and Data Engineering for Tech Professionals is the definitive guide to bridging the gap between a local Jupyter Notebook and a resilient, scalable, and automated production environment.
Written for developers, data engineers, and technical architects, this book provides a deep-dive into the rigorous world of MLOps. It ignores the academic fluff and focuses on the high-performance engineering required to deploy and maintain machine learning models in the wild.
Inside This Book, You Will
● Production-Grade Data Build robust, high-throughput APIs using FastAPI and Pydantic for strict data validation and type safety.
● Containerization & Package your mathematical engines with Docker and orchestrate them at scale using Kubernetes to handle massive enterprise traffic.
● Serverless AI Deploy models with zero-infrastructure management on AWS Lambda and GCP Cloud Run, utilizing specialized adapters like Mangum.
● Automated CI/CD Implement "gatekeeper" workflows with GitHub Actions to automate testing for both code and mathematical model behavior.
● Data Engineering for Construct efficient ETL pipelines and implement Vectorization to eliminate computational bottlenecks in Python.
● Observability & Detect silent failures like Concept Drift and statistical degradation using Prometheus and Grafana.The Modern Tech Stack
● Core Python (Pandas, Scikit-Learn, NumPy).
● Kubernetes, Docker, AWS, Google Cloud Platform (GCP).
● Deployment & GitHub Actions, FastAPI, Mangum, Joblib, Prometheus.Who This Book Is
● Data Engineers who need to build the "plumbing" for scalable AI.
● Software Developers transitioning into the machine learning space.
● Data Scientists looking to gain the engineering discipline required for production.
● Tech Professionals tasked with architecting enterprise-level predictive pipelines.Don’t let your models rot in a notebook. Elevate your career by mastering the engineering behind the AI revolution. Scroll up and grab your copy today.
Most machine learning resources focus on the "Data Science" side—algorithms, tuning, and accuracy. But in the enterprise, a model is only as valuable as the infrastructure supporting it. Python Machine Learning and Data Engineering for Tech Professionals is the definitive guide to bridging the gap between a local Jupyter Notebook and a resilient, scalable, and automated production environment.
Written for developers, data engineers, and technical architects, this book provides a deep-dive into the rigorous world of MLOps. It ignores the academic fluff and focuses on the high-performance engineering required to deploy and maintain machine learning models in the wild.
Inside This Book, You Will
● Production-Grade Data Build robust, high-throughput APIs using FastAPI and Pydantic for strict data validation and type safety.
● Containerization & Package your mathematical engines with Docker and orchestrate them at scale using Kubernetes to handle massive enterprise traffic.
● Serverless AI Deploy models with zero-infrastructure management on AWS Lambda and GCP Cloud Run, utilizing specialized adapters like Mangum.
● Automated CI/CD Implement "gatekeeper" workflows with GitHub Actions to automate testing for both code and mathematical model behavior.
● Data Engineering for Construct efficient ETL pipelines and implement Vectorization to eliminate computational bottlenecks in Python.
● Observability & Detect silent failures like Concept Drift and statistical degradation using Prometheus and Grafana.The Modern Tech Stack
● Core Python (Pandas, Scikit-Learn, NumPy).
● Kubernetes, Docker, AWS, Google Cloud Platform (GCP).
● Deployment & GitHub Actions, FastAPI, Mangum, Joblib, Prometheus.Who This Book Is
● Data Engineers who need to build the "plumbing" for scalable AI.
● Software Developers transitioning into the machine learning space.
● Data Scientists looking to gain the engineering discipline required for production.
● Tech Professionals tasked with architecting enterprise-level predictive pipelines.Don’t let your models rot in a notebook. Elevate your career by mastering the engineering behind the AI revolution. Scroll up and grab your copy today.
300 pages, Kindle Edition
Published May 9, 2026
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