Microservices for Machine Learning

Explore 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.

KEY 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.

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 FOR

This 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. 

1. Introducing Microservices and Machine Learning
2. Foundation of Microservices
3. Fundamentals of Machine Learning
4. Designing Microservices for Machine Learning
5. Implementing Microservices for Machine Learning
6. Data Management in Machine Learning Microservices 
7. Scaling and Load Balancing Machine Learning Microservices
8. Securing Machine Learning Microservices
9. Monitoring and Logging in Machine Learning Microservices
10. Deployment for Machine Learning Microservices 
11. Real World Use Cases
12. Challenges and Future Trends

Rohit Ranjan is a seasoned IT professional with a deep passion for technology and over 16 years of experience in the field. Starting with a Bachelor’s degree in Metallurgical and Materials Engineering from IIT Kharagpur, Rohit found his true calling in computer science and AI.

He has a strong foundation in data engineering and has developed a notable expertise in Hadoop, Spark, Kafka, Airflow, HBase, SOLR, and various databases. His skill set is not just limited to handling massive datasets but also extends to designing and implementing complex data pipeline architectures, making data flow seamlessly and efficiently from source to insight. This expertise is complemented by his deep knowledge of microservices architecture, where he excels in creating scalable, robust systems that integrate seamlessly with cloud platforms like AWS and Azure.

His expertise is not confined to data engineering or microservices, he has also ventured deeply into Machine Learning and deep learning. Through Python and Java, he has crafted intelligent models that learn from data to solve real-world problems, pushing the boundaries of what’s possible with technology today.

Throughout his career, Rohit has been a beacon of knowledge and leadership, contributing to the tech community through research and sharing his insights with others. He has a knack for making complex topics accessible and engaging, which is evident in his work and his active presence on LinkedIn, where he connects with peers and industry leaders.

As the author of Microservices for Machine Learning, Rohit draws from his extensive background to guide readers through the intricacies of integrating AI with microservices architecture. His book is a reflection of his journey in technology - a path of continuous learning, adapting, and innovating. Through his writing, Rohit aims to inspire others to explore the vast potential of AI and Machine Learning, equipping them with the knowledge to create cutting-edge solutions.