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.
Ce cours fait partie de la Spécialisation TensorFlow: Data and Deployment
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À propos de ce cours
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
Ce que vous allez apprendre
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
Compétences que vous acquerrez
- 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.
Offert par

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programme de cours : ce que vous apprendrez dans ce cours
TensorFlow Extended
Sharing pre-trained models with TensorFlow Hub
Tensorboard: tools for model training
Federated Learning
Avis
- 5 stars81,29 %
- 4 stars14,31 %
- 3 stars3 %
- 2 stars0,92 %
- 1 star0,46 %
Meilleurs avis pour ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW
great course for utilities to enhance the training and deployment experience
The fact that there were still some problems in the Course regarding technical or exercise based, it shows that this material is relatively new in the domain.
Great work and I highly recommend this course/specialization! Good job of inserting needed edits to update what's happening in real time.
If you want to learn extra libraries of tensorflow then take this
À propos du Spécialisation 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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