Machine Learning is Evolving!
Machine-learning (ML) has grown in the past years to be the key method of approaching complex computing and analysis problems. Instead of trying to manually program the now available algorithm-rich datasets, these can be used to train machine learning models to face challenges directly.
The Challenges of ML Observability
Data Scientists require three types of behavioral data from their ML models:
- Input and output examples for the ML model
- Peripheral data: related to the problem space but not yet available to the model. This is the data scientists may consider for retraining the model.
- Training behavior data: data on how the training process has been executed.
However, getting that data is challenging. Due to the complex nature of modern systems, observability of machine learning models in the lab is extremely difficult — both in training and during testing. Even simple systems such as Jupyter notebook do not provide sufficient observability.
The behavior and performance of ML models in production and other live environments are hidden from data scientists. They can only glimpse into their models through data provided by backend engineers. The lack of alternatives makes data scientists dependant on R&D teams and worse yet, on the software deployment cycles which often take weeks and even months. This process can impact and slow down the core of data science work.
Rookout for ML / AI
Rookout empowers data scientists by enabling direct and frictionless access to ML models and the environments embedded within them. You can add non-breaking breakpoints to enable on-the-fly code-level collection capabilities, without modifying or hindering the application flow.
You can then view how the model behaves in real-time and quickly collect any arbitrary data-point (variables, full stack, metrics). That data point can be then quickly pipelined to any platform of your choice (e.g. logs, monitoring, databases, files, etc.).
Using our platform, data scientists can create more accurate models and make better, faster iterations to improve them. With zero overhead, no risks, and zero friction, we liberate data from the strict process of code development and deployment. By eliminating the need to write more code to access model data, you save countless work hours and release the unneeded lock on R&D labor and deployment cycles.
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