wandb VS Incoming

Weights & Biases — The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management. Free tier for personal projects only, with 100 GB of storage included.

HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"

Wandb is the best! https://wandb.ai/

To track stuff I've used wandb.ai in a company in the past, as someone else pointed out. Regarding metrics... This is really specific to your domain, and it is such a broad question. You could count color pixels, the distribution of intensity histograms, etc etc.

Step 1: The colab notebook uses wandb.ai, so you need to register for a wandb.ai account beforehand if you want to use the colab notebook. After registering you need to go to your homepage and copy the API key and paste/keep it somewhere.

I want to take a look at tools like https://wandb.ai/ and they would integrate into some of the pipelines I'm playing with.

does current cgi and ai tech can bring back leslie nielsen? might use unreal engine and https://www.resemble.ai/ or https://wandb.ai/?

I'm currently working for a MLOps company so I'm heavily using their tools (Weights & Biases) but I've used custom C++ for deployment, Pytorch + fastai for quick experimentation, Weights & Biases for experiment tracking, hyper-parameter tuning + model versioning (hence why I went to work for them), custom database + data pipeline, HoloViz for data visualisation (really nice dashboarding tool), Jenkins for CI/CD, I also love Github Actions.

This article from HoneyBadger explains most relevant topics about Rails DB transactions.

Honeybadger is a powerful error-monitoring tool for Python applications. Integrating an error monitoring service like Honeybadger into your development workflow provides numerous benefits for effectively managing exceptions. From real-time notifications and error grouping to rich diagnostics and trend analysis, Honeybadger equips you with the tools you need to quickly identify, investigate, and resolve errors and ultimately enhance the overall quality and reliability of your applications. To demo this, let's now explore some features and examples of integrating Honeybadger into your Python code.

honeybadger.io - Exception, uptime, and cron monitoring. Free for small teams and open-source projects (12,000 errors/month).

It sounds like you want to implement an exception monitoring tool like Honeybadger (my company), Sentry, or similar. They will tell you when someone encounters an error with your app, where the error occurred, and what the state of the app was (parameters, etc.) at the time of the error.

You may wonder why this is a problem. In the case of this code, we're sending the user's username to a third-party service. While username isn't inherently sensitive data, it certainly has to potential to be and should be treated as such. It's better to use IDs that can't identify the user if the third party—in this case, honeybadger—is breached. You can see the full list of supported data types, sorted by category, on the docs.

Sign up on the Honeybadger website and click on ‘start free trial’, as shown in the following image.