Customizing generative AI models is easier than building them from scratch, but operationalizing them still poses many challenges.
Generative AI models are trained on large amounts of data to learn the patterns and relationships within that data. This allows them to generate new, realistic content that is similar to the data they were trained on. However, generative AI models can also be used to generate harmful or misleading content.
To mitigate these risks, it is important to carefully operationalize generative AI models. This means putting in place processes and procedures to ensure that the models are used safely and responsibly.
One of the key challenges in operationalizing generative AI models is tuning the tuning pipelines. This involves finding the optimal parameters for the model so that it generates the most realistic and accurate content.
Another challenge is model management. This involves storing and tracking the models so that they can be easily accessed and updated.
Evaluation is also important for ensuring that the models are working as intended. This involves testing the models with a variety of data and evaluating their performance.
Finally, safety filters can be used to prevent the models from generating harmful or misleading content. These filters can be based on a variety of criteria, such as the content's language, images, or audio.
In this video session below Speakers: Mikhail Chrestkha, Irina Sigler, Ori Goshen will discuss how to adapt MLOps to the generative AI era, covering topics such as tuning pipelines, model management, evaluation, safety filters, and all of these challenges and how to address them. It will also provide practical advice on how to adapt MLOps to the generative AI era.
