Lo que aprenderás
- How to gather data effectively for training machine learning models.
- How to use Python to train and deploy tiny machine learning models.
- How to optimize machine learning models for resource-constrained devices.
- How to conceive and design your own tiny machine learning application.
- How to program in TensorFlow Lite for Microcontrollers.
- How to automate a MLOps life cycle.
- Real-world examples and case studies of MLOps Platforms targeting tiny devices.
Tiny Machine Learning (TinyML) is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of tiny devices and embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software.
In this unique Professional Certificate program offered by Harvard University and Google ML, Data and AI Subject Matter experts, you will enhance your knowledge in the emerging field of TinyML, start applying the skills you have developed into real-world applications, and build the future possibilities of this transformative technology at scale.
In the first course of the program, Applications of TinyML, you will see how tools like voice recognition work in practice on small devices and you learn how common algorithms such as neural networks are implemented.
In Deploying TinyML, you will experience an open source hardware and prototyping platform to build your own tiny device. The program features projects based on an Arduino board (the TinyML Program Kit) and emphasizes hands-on experience with training and deploying machine learning into tiny embedded devices. The TinyML Program Kit has everything you need to unlock your imagination and build applications based on image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application of your own design.
The final course of this series (MLOps for Scaling TinyML) focuses on operational concerns for Machine Learning deployment, such as automating the deployment and maintenance of a (tiny) Machine Learning application at scale. Through real-world examples spanning the complete product life cycle, you will learn how tiny devices, such as Google Homes or smartphones, are deployed and updated once they’re with the end consumer.
For learners just getting started with TinyML, we recommend beginning with Fundamentals of TinyML.
This program is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team. Taught by Harvard Professor Vijay Janapa Reddi along with Lead AI Advocate at Google, Laurence Moroney, Technical Lead of Google’s TensorFlow and Micro team, Pete Warden, and Head of Data/AI Practice, Larissa Suzuki, this program offers you the unique opportunity to learn from leaders and subject matter experts in the AI, Data and ML space and how to apply the emerging world of TinyML at scale.
Cursos en este programa
Certificación Profesional en Applied Tiny Machine Learning (TinyML) for Scale de HarvardX
- 2–4 horas por semana durante 6 semanas
Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.
- 2–4 horas por semana durante 5 semanas
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
- 2–4 horas por semana durante 7 semanas
This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.
- There are hundreds of billions of microcontrollers today, and an increasing desire to deploy machine learning models on these devices through TinyML. Learners who complete this program will be prepared to dive into this fast-growing field.
- Learners will have a fundamental understanding of TinyML applications and use cases and gain hands-on experience in programming with TensorFlow Micro and deploying TinyML models to an embedded microprocessor and system.
- Job postings in the United States requiring knowledge and skill working with Embedded Systems rose 71% in the last year according to Economic Modeling Systems Incorporated (2022).
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