My research was around how well they support RAG use cases. Do they have all the components required for building the RAG use case? As in, you need a data storage part, vector storage where you can store your vector embeddings, and the model layer itself.
IT Senior Manager at a tech services company with 10,001+ employees
Real User
Top 20
2024-09-13T03:08:00Z
Sep 13, 2024
We are using Google Vertex AI for various purposes, especially various chatbots for customer engineers and customers. Additionally, we are working on projects involving AI and machine learning.
Data Scientist at Tradeindia.com-Infocom Network Ltd
Real User
Top 10
2024-07-19T05:51:44Z
Jul 19, 2024
We use the solution for the model garden. We leverage both open-source models and Google Vertex AI's embedding models and use them to deploy our machine-learning models.
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.
My research was around how well they support RAG use cases. Do they have all the components required for building the RAG use case? As in, you need a data storage part, vector storage where you can store your vector embeddings, and the model layer itself.
We are using Google Vertex AI for various purposes, especially various chatbots for customer engineers and customers. Additionally, we are working on projects involving AI and machine learning.
We use it to run AI chatbots, generative AI, and some data science and machine learning instances.
We use the solution for the model garden. We leverage both open-source models and Google Vertex AI's embedding models and use them to deploy our machine-learning models.
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.