Empowering AI innovations: The fusion of microservices and MLKey Features● Microservices and ML fundamentals, advancements, and practical applications in various industries. ● Simplify complex ML development with distributed and scalable microservices architectures.

● Discover real-world scenarios illustrating the fusion of microservices and ML, showcasing AI's impact across industries. DescriptionExplore the link between microservices and ML in Microservices for Machine Learning.

Through this book, you will learn to build scalable systems by understanding modular software construction principles. You will also discover ML algorithms and tools like TensorFlow and PyTorch for developing advanced models.

It equips you with the technical know-how to design, implement, and manage high-performance ML applications using microservices architecture. It establishes a foundation in microservices principles and core ML concepts before diving into practical aspects.

You will learn how to design ML-specific microservices, implement them using frameworks like Flask, and containerize them with Docker for scalability. Data management strategies for ML are explored, including techniques for real-time data ingestion and data versioning.

This book also addresses crucial aspects of securing ML microservices and using CI/CD practices to streamline development and deployment. Finally, you will discover real-world use cases showcasing how ML microservices are revolutionizing various industries, alongside a glimpse into the exciting future trends shaping this evolving field.

Additionally, you will learn how to implement ML microservices with practical examples in Java and Python. This book merges software engineering and AI, guiding readers through modern development challenges.

It is a guide for innovators, boosting efficiency and leading the way to a future of impactful technology solutions. What you will learn● Master the principles of microservices architecture for scalable software design.

● Deploy ML microservices using cloud platforms like AWS and Azure for scalability. ● Ensure ML microservices security with best practices in data encryption and access control.

● Utilize Docker and Kubernetes for efficient microservice containerization and orchestration. ● Implement CI/CD pipelines for automated, reliable ML model deployments.

Who this book is forThis book is for data scientists, ML engineers, data engineers, DevOps team, and cloud engineers who are responsible for delivering real-time, accurate, and reliable ML models into production. Table of Contents1.

Introducing Microservices and Machine Learning2. Foundation of Microservices3.

Fundamentals of Machine Learning4. Designing Microservices for Machine Learning5.

Implementing Microservices for Machine Learning6. Data Management in Machine Learning Microservices7.

Scaling and Load Balancing Machine Learning Microservices8. Securing Machine Learning Microservices9.

Monitoring and Logging in Machine Learning Microservices10. Deployment for Machine Learning Microservices11.

Real World Use Cases12. Challenges and Future Trends.