From Training to Production: How to Fit neptune.ai in Your Machine Learning Model Lifecycle?
In this webinar, Parth Tiwary (neptune.ai’s Product team) and Kamil Kaczmarek (neptune.ai’s DevRel team) share what they’ve learned when talking to hundreds of teams looking for a solution to organize the model development lifecycle and track metadata from each stage of the process.
The agenda:
- A high-level overview of MLOps landscape
- What is the ML model lifecycle?
- Major challenges/complexities in ML model lifecycle
- How you can use neptune.ai for:
- Experiment tracking
- Model fine-tuning
- Logging metadata from monitoring and production environment
- Setting up automatic retaining of models using monitoring metadata
- Tips and tricks on using neptune.ai in your MLOps lifecycle
- Q&A session
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Intro
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Major challenges/complexities in ML model lifecycle
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Product demo
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Q&A session
Other useful resources
If you want to try Neptune out, check the documentation.
If you want to learn more about the product first, head over to the neptune.ai homepage.
You can also read case studies showing how others incorporated Neptune into their workflows.
More about From Training to Production: How to Fit neptune.ai in Your Machine Learning Model Lifecycle?
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
See also
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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