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Machine Learning API development essentials

Track :

Computer Science

Lessons no : 13

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What will you learn in this course?
  • Design RESTful APIs for machine learning models using Flask or FastAPI frameworks
  • Implement model serialization and deserialization for seamless API integration in ML applications
  • Deploy machine learning APIs on cloud platforms ensuring scalability and high availability
  • Handle API requests efficiently, including data validation, error handling, and response formatting
  • Secure machine learning APIs with authentication, authorization, and encryption techniques
  • Optimize API performance for real-time inference and large-scale machine learning deployments
  • Manage API versioning, monitoring, and maintenance for long-term ML system stability
  • Integrate machine learning APIs into diverse applications, enhancing AI-driven decision-making

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Lessons | 13


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Sunil prabhakar damodar

Very good 2024-12-04

Priyanshu Saklani

its really good hence we do not fee any kind of fees and get certificate easily. 2024-11-20

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Related Courses

Machine Learning API development course, in this course is a comprehensive course that guides you through the process of creating, deploying, and managing APIs for machine learning models. In this course, you’ll learn how to design RESTful APIs using frameworks like Flask or FastAPI, integrate machine learning models into APIs, and enable seamless interaction between applications and ML systems. You’ll explore concepts like model serialization, deployment on cloud platforms, handling API requests, and ensuring security and scalability. This hands-on course is perfect for developers and data scientists who want to deploy machine learning solutions effectively and integrate them into real-world applications. By the end, you'll have the skills to build robust APIs that bridge the gap between machine learning models and production-ready systems.