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Why should you monitor TensorFlow?
TensorFlow is an open-source machine learning library widely employed to develop and train neural network-based deep learning models. New Relic's TensorFlow quickstart performance monitoring provides out-of-the-box observability for TensorFlow models. By using the TensorFlow quickstart, you will be able to:
Identify performance bottlenecks:
Enhance the efficiency of your TensorFlow models by pinpointing and optimizing performance-heavy areas.
Ensure model accuracy:
Monitor the output of your models in real-time to detect and correct discrepancies, thereby ensuring the desired model performance.
Error identification and resolution:
Active monitoring allows for quick detection and rectification of errors, ensuring the reliability and robustness of your TensorFlow models.
Comprehensive monitoring quickstart for TensorFlow models
With New Relic's TensorFlow quickstart, you can actively oversee the performance of your TensorFlow models, gaining insights to ensure they run effectively and efficiently, especially in real-world deep learning applications.
What’s included in the TensorFlow quickstart?
New Relic TensorFlow monitoring quickstart provides quality out-of-the-box reporting:
- Obtain insights into how your TensorFlow models are performing in real-time
- Alerts on model performance: Set up proactive notifications for any unusual behavior or degradation in the performance of your models