Better picture of your data πŸ” , images on every epoch 🌁, a data science competition with Layer πŸš€

We have added support for summary stats for your dataset columns on the Layer web interface. We have added percentiles on the data summary statistics to make it easier for you to see a quick description of your datasets. It's now much easier to get a picture of your training data profile.

Better picture of your data πŸ” , images on every epoch 🌁, a data science competition with Layer πŸš€

Hello ,
I hope you had a great week! As we walk into the weekend, I want to take the moment to give you a quick update about Layer. Let's begin!

⭐ A better picture of your training data

We have added support for summary stats for your dataset columns on the Layer web interface. We have added percentiles on the data summary statistics to make it easier for you to see a quick description of your datasets. It's now much easier to get a picture of your training data profile.

View Sample | Book a Demo

⭐ Faster datasets with Layer

We have been working hard to improve the user experience while using Layer. As a result of that, we are happy to announce that loading datasets from Layer is now up to 4x faster and dataset build throughput is up to 3x faster. You can try now with this 1GB training dataset:

import layer

df = layer.get_dataset("layer/Zindi-Laduma-Analytics/datasets/train_game_statistics:1.1").to_pandas()

 

πŸ’« We partnered with Zindi to host a data science competition

We sponsored a data science competition on ZindiπŸš€. The objective of this competition is to predict the outcome of a football match, based on historical match and player data. As part of this competition, the participants get a chance to use Layer for free to train their machine learning models. Check out how to get started with Layer in this competition

 

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🌁 You can now log images on every epoch

We want to ensure that you can log anything in your experiment tracking with Layer. Today we are happy to announce that you can now log images at every epoch. For example, you can log the prediction of a computer vision model at each epoch to see how the model performs as you train it. 

Read More | Book a Demo

🀝 Layer & Censius partnership

Layer and Censius have come together to offer the best in machine learning observability and collaborative machine learning. Here’s how the partnership will benefit you as a machine learning practitioner:

- Model training with free Layer GPUs and CPUs

- Model monitoring with Censius 

- Experiment tracking with Layer 

- Model explainability with Censius 

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πŸ’» Training models at scale with high-quality labeled data

We have partnered with Ango AI to ensure that you have a platform for training your deep learning models after labeling data on Ango Hub. Once the data is labeled and annotated, you are ready to start training models on that data. Ango and Layer are joining hands to enable the use of data from Ango to train models on Layer. You can now use the data from Ango Hub to train your machine learning projects on Layer infrastructure.

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For more machine learning news, tutorials, code, and discussions join us on SlackTwitter, LinkedIn, and GitHub.

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