MLOps for Scaling TinyML
Are you ready to scale your (tiny) machine learning application? Do you have the infrastructure in place to grow? Do you know what resources you need to take your product from a proof-of-concept algorithm on a device to a substantial business?
Machine Learning (ML) is more than just technology and an algorithm; it's about deployment, consistent feedback, and optimization. Today, more than 87% of data science projects never make it into production. To support organizations in coming up to speed faster in this critical domain it is essential to understand Machine Learning Operations (MLOps). This course introduces you to MLOps through the lens of TinyML (Tiny Machine Learning) to help you deploy and monitor your applications responsibly at scale.
MLOps is a systematic way of approaching Machine Learning from a business perspective. This course will teach you to consider the operational concerns around Machine Learning deployment, such as automating the deployment and maintenance of a (tiny) Machine Learning application at scale. In addition, you’ll learn about relevant advanced concepts including neural architecture search, allowing you to optimize your models' architectures automatically; federated learning, allowing your devices to learn from each other; and benchmarking, enabling you to performance test your hardware before pushing the models into production.
Upcoming start dates
- Self-paced Online
Who should attend?
Course delivery details
This course is offered through Harvard University, a partner institute of EdX.
2-4 hours per week
- Verified Track -$199
- Audit Track - Free
Certification / Credits
What you'll learn
- Know why and when deploying MLOps can help your (tiny) product or business
- Key MLOps platform features that you can deploy for your data science project
- How to automate a MLOps life cycle
- Real-world examples and case studies of MLOps Platforms targeting tiny devices
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