Applied Research
Despite the advantages to leveraging the Azure AI Marketplace for some use cases, there are many scenarios in which performing Fine-Tuning, Deep Learning and MLops on your local hardware is preferable to performing the same in the cloud. In my experience, this often includes one or more of the following: Now, my prediction is that…
Read More “Deploying Locally Trained LLM & ML Assets to Azure” »
Retrieval Augmented Generation(RAG) is the method of choice for increasing the output accuracy of Natural Language Processing(NLP), generation-based models. I’m not claiming that this will eliminate your generative model’s chances to hallucinate (as some do), but it does push the the model to retrieve relevant documents or pieces of information from your own corpus, based…
Though most Large Language Models(LLM) feature strong out-of-the-box capabilities with a wide spectrum of knowledge, they often fall short when asked to perform in-depth tasks within a specific subject matter — making them less that useful within specialized domains, like cyber security. There are two basic options to remedy this shortcoming while mitigating sacrifice of…
A homelab project in Dark Web content discovery, behavioral traffic analysis, and database development.
A case study and recommended Azure cloud architecture, designed to enrich data and train machine learning models with industrial IoT production data.
A case study and simple hybrid (public/private) cloud architecture, designed to join Azure capabilities with on-premises computing clusters.
