We use Vertex AI for building machine learning workflows. This encompasses the entire process, from developing the workflow for training models to making predictions. Additionally, it handles the integration of diverse data types, including electronic data interchange (EDI), Salesforce (SFDC), and other formats. This aligns with the information you inquired about during our discussion last week.
We were looking for optimization for a machine learning module. Gurobi is an optimization library, but it's quite expensive. We were looking for an alternative to Gurobi and came across Google Vertex AI. We still find that Gurobi works better than Google Vertex AI. Eventually, we dropped out after six months of using Google Vertex AI.
Our use case involves leveraging it for tasks such as generating document summaries with keyword detection after scanning. Additionally, we employ image augmentation based on image descriptions. For advanced language analytics, we analyze the frequency of specific keywords and business terms within documents, ultimately ranking the documents based on these criteria.
Vertex AI is a playground for data analysts. It is for machine learning engineers and data scientists. We create, test, customize, deploy, and monitor our models on Vertex AI. It is a fully managed product of machine learning. About twenty people at our company use Vertex AI.
I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information.
We use Vertex AI for building machine learning workflows. This encompasses the entire process, from developing the workflow for training models to making predictions. Additionally, it handles the integration of diverse data types, including electronic data interchange (EDI), Salesforce (SFDC), and other formats. This aligns with the information you inquired about during our discussion last week.
We were looking for optimization for a machine learning module. Gurobi is an optimization library, but it's quite expensive. We were looking for an alternative to Gurobi and came across Google Vertex AI. We still find that Gurobi works better than Google Vertex AI. Eventually, we dropped out after six months of using Google Vertex AI.
Our use case involves leveraging it for tasks such as generating document summaries with keyword detection after scanning. Additionally, we employ image augmentation based on image descriptions. For advanced language analytics, we analyze the frequency of specific keywords and business terms within documents, ultimately ranking the documents based on these criteria.
Vertex AI is a playground for data analysts. It is for machine learning engineers and data scientists. We create, test, customize, deploy, and monitor our models on Vertex AI. It is a fully managed product of machine learning. About twenty people at our company use Vertex AI.
I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information.