Description
ML in Production: From Data Scientist to ML Engineer, a training course on converting any ML model in a Jupyter notebook into a production-ready microservice, has been published by Udemy Academy. This comprehensive course is designed to equip you with the essential skills and knowledge needed to transform machine learning (ML) models developed in Jupyter notebooks into fully operational, production-ready microservices. As a student in this course, you will delve deeply into the intricacies of an ML model from a mere concept in a notebook to a scalable and efficient application that thrives in a real-world production environment. During the course you will learn to bridge the gap between data science and software engineering and advance your ML capabilities from theoretical models to practical applications. This course begins with an introduction to the basics of microservices architecture and lays the groundwork for understanding how ML models fit into larger software systems.
You will then learn how to effectively containerize your ML models using Docker, a critical skill in today’s software development landscape. It includes hands-on training on creating Docker images, managing containers, and understanding the basics of container orchestration. API development is another cornerstone of this course. You will be taught the process of designing and implementing robust APIs that allow your ML models to seamlessly communicate with other applications. It includes hands-on training on handling API requests and responses, along with ensuring your APIs are secure and scalable. This course covers deployment strategies with a focus on practical and real-world challenges. You will be involved in setting up continuous integration and delivery (CI/CD) pipelines, managing version control and learning best practices for monitoring and maintaining your models after deployment.
What you will learn
- Transforming ML models from Jupyter notebooks into production-ready microservices, with a focus on deployment, dependency management, and maintainability.
- Learn to create robust APIs for ML models, covering API design, request handling, and ensuring scalability and security.
- Containerization for deploying ML models, including container management and best practices for ML applications.
- Gain hands-on experience with real-world deployment strategies, including CI/CD pipelines, version control, MLOps frameworks, and maintaining live models.
Who is this course suitable for?
- Junior and mid-career data scientists
Details of the ML in Production course: From Data Scientist to ML Engineer
- Publisher: Udemy
- teacher : Andrew Wolf , Ilya Fursov
- English language
- Education level: all levels
- Number of courses: 11
- Training duration: 2 hours and 43 minutes
At the beginning of the course seasons in 2024-2
Course prerequisites
- Knowledge and practical experience of Python syntax
- Experience in model development in Python (train-test split, tuning hyperparameters, evaluating model performance, making predictions)
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
0.99 GB
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