sourabh03agr

Yes, general observability tools don't work well for ML applications as they lack support for ML specific use cases such as attaching Ground Truth label, data drift, model bias, etc.

Beyond that, requirements for Deep learning models is even more nuanced. Say, for language models, we provide two key features to effectively monitor them:

1. We represent the text by an embedding (e.g. BERT) which are much more informative from a statistical distribution perspective to find out edge cases, low density regions, etc. Further we use Earth Moving Distance to quantify data drift in the multi-dimensional space.

2. We allow user-defined smart signals to be written on top of your model inputs/outputs. You can classify a certain prediction to be wrong if it doesn't follow grammar rules or have occurrence of certain keywords or the prediction is followed by a certain user behaviour pattern (if an user is not satisfied with the response of ChatGPT, it is expected that they will ask the same question again and again in different ways). All these customisations serve as a good proxy to actually observe the model's performance and find avenues to improve them.

Completely agree! We have also seen our users more concerned about comparing the real-world distribution against the training data as compared to previous month's data (we found latter is more useful for PMs and setting alerts).

We currently allow users to specify their training data in the config which is used to initialise the UpTrain framework (in form of json file but are planning to support pytorch/tf data-loaders). In the background, the tool does all the binning and clustering to convert these continuous variables into discrete buckets to later calculate divergence, which is then used to quantify drift.

Thanks a lot! Yes, we are big believers of open-source :)

Also, happy to share our learnings of working with a social media customer. For them, key motivation was to understand where the model is failing and hence, understand how to improve it. They started with offline experiments with focus on improving AUC but the graph saturates pretty quickly. While an incremental model improvement didn't lead to a sizeable change in offline metrics, it can impact retention of a certain user group and hence, overall revenue. They are using us to get insights on online experimentation. They have defined custom measures to monitor the distribution of model outputs and are able to determine the difference in model's performance much more effectively as compared to relying on changes in business metrics like retention, revenue, etc. This helps them to find out poor-performing cohorts and roll-out model improvements in weeks, not months. Would also love to hear what issues you ran into?

Being open-source is a key differentiator. With us being open-source, UpTrain can be easily customized for any specific use-case.

With UpTrain, one can define custom measures to monitor upon, add custom algorithms for model stability or drift detection as well as fill in any integration gaps in terms of using us in production

There are two ways:

1. We use model-inferred embeddings. Say, for the instance segmentation task, we use deep learning networks to transform the input image into a dense embeddings representation, on top of which we run clustering and density estimation to find if the given embedding/image/feature combination is an outlier (or belongs to low-density region)

2. We allow users to define custom signals to identify edge-cases, specific to their use-case. A very simple example could be calculate brightness or Hue properties on the input image and see if that is an outlier compared to the training distribution.

Thanks! So, providing ground-truth is optional (and can be added at a later time). For each logged input, the tool returns an identifier which you can use to attach the ground truth (or any other relevant information which your custom monitors need). Once provided, the tool runs the relevant checks on those cases and alerts if any issues are found.

Thanks for the question! Our initial focus is more on how to find the most relevant data-points from the hundred gigabytes of data to retrain the model on. Our current data management strategy is pretty primitive, either local files or we connect back to your data warehouse for persistence.

Soon, we plan to add data management features too but primarily on the production side so that data scientists can safely and securely version the data which their AI application came across in production as well as use it to refine their model (if allowed)

At the end of the day, the so-called AI generated art is a result of millions of artworks which were consumed by the AI model while training. I always like to draw parallels between AI copyrights and NFTs in the Web3 world. While minting NFT == having a digital copyright on the art piece, you pay a gas fees for doing so which gets distributed to the community as a reward to enabling it. I would love to see similar mechanisms in the AI world where the community get rewards for someone using Stable Diffusion (and not just Stability AI).

Looks good but this works well only for gamey, sci-fi kind of themes. Any suggestions for prompts which can yield interesting flowcharts to explain technical concepts?