Machine Learning Engineering for Production (MLOps) Specialization is a training course to become a machine learning specialist. Understanding the concepts of machine learning and deep learning is essential, but if you are looking to build an effective artificial intelligence specialty, you also need production engineering facilities.
Effective development of machine learning models requires competencies that are more commonly found in areas such as software engineering and DevOps. Machine learning engineering for production is the result of combining the basic concepts of machine learning with the practical skills of modern software development and engineering roles.
In this course, you will become familiar with the possibilities, challenges, and results of machine learning engineering in production. At the end of the course, you can apply your newly acquired skills in the development of pioneering artificial intelligence technologies to solve real-world problems.
Skills to learn in Machine Learning Engineering for Production (MLOps) Specialization:
- Manage_machine learning systems
- Development, model, and Pipelines data
- Machine_learning engineering for production
- Doing work at the human level
- Concept Drift
- Baseline model
- Challenges of machine_learning development
- Machine_learning metadata
- Convolutional neural network
Instructors: Andrew Ng , Cristian Bartolomé Arámburu , Robert Crowe and Laurence Moroney
Number of Courses: 4
Duration: Assuming 5 Hours a Week, 4 Months
Courses in the Engineering Project Management Specialization series:
Introduction to Machine_Learning in Production
Machine_Learning Data Lifecycle in Production
Machine_Learning Modeling Pipelines in Production
Deploying Machine_Learning Models in Production
Learners should have a working knowledge of AI and deep learning.
Learners should have intermediate Python skills and experience with any deep learning framework (TensorFlow, Keras, or PyTorch).
Learners should be proficient in basic calculus, linear algebra, and statistics.
We highly recommend that you complete the updated Deep Learning Specialization before starting this Specialization.
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This collection includes 4 different courses.