How to visualize deep learning models + other resources
November brings a fresh batch of new (and revamped) MLOps articles. So let's have a look at some case studies, guides, and a lightning talk from the MLOps World Conference.?
Have a good read!
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Case studies & practical MLOps
>?ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It -?In the development of ML models, managing the vast amount of data from numerous experiments is a challenging task. Experiment tracking is a practical solution to this issue. The article provides a straightforward guide covering the definition of experiment tracking, best practices, various implementation options, and a step-by-step setup tutorial.
>?The Best MLflow Alternatives?-?MLflow is widely recognized as a key element in many ML platforms, valued for its adaptable and open-source design. However, as teams grow, they frequently encounter obstacles in areas like collaboration, deployment, and advanced features. This article offers an analysis of various MLflow alternatives, featuring a comparison table that highlights key experiment tracking features at its conclusion.
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Guides & tutorials
>?How to Visualize Deep Learning Models?- As we know, understanding deep learning models can be challenging due to their complex nature. However, ML visualizations providing graphical representations of models’ structures and data flows help to simplify it. In this article, Nilesh Barla?explores deep learning visualizations and discusses their applicability in different scenarios with practical examples.
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领英推荐
Tools
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MLOps World talk
> As we mentioned in a previous newsletter, Neptune participated in the MLOps World Conference. Now it's time to share some insights with you!
Catch a glimpse of our Head of Product - Aurimas Griciūnas delivering a five-minute lightning talk on building an end-to-end MLOps pipeline.
To get a notification about the new episode, follow us on Spotify, Apple Podcasts, or subscribe to our YouTube channel.
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Okay, that's it for today. If you want to talk about these recommendations, send me an email or?join the MLOps community here, and find the?#neptune-ai channel there.
Feel free to forward this newsletter to your friends and communities, if you find it useful!
Cheers!