Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Este curso forma parte de Programa especializado: TensorFlow: Data and Deployment
ofrecido por


Acerca de este Curso
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
Qué aprenderás
Use TensorFlow Serving to do inference over the web
Navigate TensorFlow Hub, a repository of models that you can use for transfer learning
Evaluate how your models work and share model metadata using TensorBoard
Explore federated learning and how to retrain deployed models while maintaining data privacy
Habilidades que obtendrás
- TensorFlow Serving
- Machine Learning
- federated learning
- TensorFlow Hub
- TensorBoard
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
ofrecido por

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - Qué aprenderás en este curso
TensorFlow Extended
Sharing pre-trained models with TensorFlow Hub
Tensorboard: tools for model training
Federated Learning
Reseñas
- 5 stars81,37 %
- 4 stars14,25 %
- 3 stars2,98 %
- 2 stars0,91 %
- 1 star0,45 %
Principales reseñas sobre ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW
Nice course about tensorflow deployment techniques
Excellent Content. It Will definitely Advance my career.
The first week is very useful.
Tensorhub seems not helpful in a private company setting.
I wish they talked about tf estimators which is also a common way to deploy models in SageMaker.
Awesome course for the using and application of Machine learning
Acerca de Programa especializado: TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.

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