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As the potential of Generative AI and Large Language (LLM) models continue to grow at a frightening pace, it can be hard to know where to get started and get your head around all the tools needed for a successful LLM project! One key tool in any successful implementation is a vector database.

Machine learning (ML) model deployment is a critical part of the MLOps lifecycle, and it can be a challenging process. In the previous blog, we explored how Azure Functions can simplify the deployment process. However, there are many other factors to consider when deploying ML models to production environments

Deploying machine learning (ML) models into production can be challenging, as it requires careful consideration of various factors such as scalability, reliability, and maintainability. While developing an ML model is an exciting process, deploying it into production can be a daunting task.

Image classification is a standard computer vision task and involves training a model to assign a label to a given image, such as a model to classify images of different root vegetables. A big problem with classification is bias, and the models favouring a particular image class above the others. A common cause of this can be dataset imbalance, and it is often hard to spot as a model trained on an imbalanced dataset can often still have good accuracy. E.g. if there are 1000 images in the test dataset, 950 potatoes and 50 carrots and the model predicted all 1000 images to be potatoes it would still have 95% accuracy. This is also an example of why more metrics than accuracy should be considered… but let’s leave that discussion for another day.

In this example we will look at how Snorkel can be used as part of a recommender system to label the relevance of different books for a user. The dataset used will be an augmented and normalised version of the Goodreads dataset, containing user-book pairings and extensive metadata on each book.