2 min Applications

Google gives AI Hub more tools for sharing and collaboration

Google gives AI Hub more tools for sharing and collaboration

Google will give its AI Hub various updates, including a new homepage and more collaboration tools for data scientist and artificial intelligence (AI) teams.

AI Hub was made available last April as a beta version, but was announced last year. AI Hub is a cloud-hosted repository with plug-and-play AI components, making it possible to create various types of AI models.

The updates should make it easier for teams to work together, writes Silicon Angle. For example, the new homepage should make it easier for users to access their most popular and most recently shared assets.

More advanced sharing

The homepage also provides access to more advanced sharing tools. For example, it is possible to share notebooks, trained machine learning models and Kubeflow pipelines. Kubeflow pipelines are used to build and deploy scalable machine learning workflows based on Docker containers in a more flexible way.

In addition, users can share their assets directly on social media. By clicking on a social media icon, the URL is copied. The URL can then be pasted to the desired location to share the asset.

According to Google itself, this function is particularly useful in public AI projects that are looking for outside expertise to work with.

Find AI assets more easily

Google also makes it possible to find the most popular AI-assets of users in AI Hub. This makes it possible to consider certain notebooks and models as favorites.

With a latest update, Google adds over seventy new, cutting-edge assets to AI Hub. Google has created these assets for users itself, so that they can build on them further. For example, a TensorRT-optimized BERT notebook that contains examples of how the popular BERT natural language understanding model can be used.

Finally, there is a new Pulto7 Kubeflow pipelin for time series forecasting. This can be used for business plant tasks such as optimizing inventory, predicting turnover and predicting shop traffic.