Vertex AI Reviews & Insights

We do a lot of image generation for our various areas of marketing (website, social media, graphics for conferences/booth giveaways), and we love using Vertex AI's image editor. We've used other AI image generators in the past (such as Microsoft's and Chat GPT's), but we've found Vertex to be a lot more accurate with less amount of mistakes.

Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.

We work as experts of this platform and implement it in customers environments. It can range from banks to airlines so business use can vary a lot. Not one business is like the other so flexibility is a must for us, and we can quickly and easily see what platforms are adaptable and useful.

Since we have used this platforms in multiple scenarios we can confidently say that where this excels is when you want to combine free form Q&A bots with structured responses. Gemini shines through and stands tall with it's natural language model and accurate reading of knowledge base to provide the best answers to whatever prompt you can throw at it.

I have been working on Vertex AI for quite long now, It is one stop all data science solution,I did transformed multiple POCs to projects with Vertex AI only. At the same time, it has lot many bugs google team tough fixes but still it has many bugs

It's best tech I have used so far but requires thorough testing before production stage as it has many dependencies in the form of pre-built libraries and bugs

we have used Vertex AI in machien learning models to streamline and accelerate the development. Vertex AI is help us too automate few things in our project as well and we are using google cloud already it's super easy to integrate with big query and dataflow it makes allowing smooth data pipeline and machine learning process

our sales team want to predict customer churn but we don't have enough data scientist in organisation so we need some automation tool for ML process so we have used Vertex AI for train model it required minimal configuration and it's handle data processing model selection so we are able to analyse customer engagement and purchase history

I use Vertex AI to solve the problems of finding insight from multiple pdf document using a generative AI chatbot, GCP Document AI used for OCR which is costlier we later realized then we used their Gemini model as a large language model for answer generation but it also failed in generating answer at production, overall i am not satisfied with the Vertex AI services. It is ok for POC but not for production.

I generally don't recommend Vertex AI because of some previous failure experience their large language model is not good enough. i can only recommend their document AI which is quite good but pricing is too high. They should also improve their text to speech model just like elevenlabs.com if it is done then we can use it as a call center bot.

Vertex AI is used for Generative AI features such as summarizing notes and call logs or getting email suggestions on how to improve or change the tone. It also helps extract, outline, and classify data from spreadsheets.

In terms of building Gen AI products, Vertex AI is a solid platform. For example, in an ATS, Gen AI features such as Notes, Call Logs, or Email Summarization, generating Job Descriptions, generating Candidate Summaries, Targeted Email Campaigns, etc.

We use Vertex AI on a daily basis. Vertex AI workbench forms the best kind of a platform for our daily AI/ML development. So the product that we are working on requires us to experiment our code on GPU systems and Vertex AI workbench gives us the resources that are required. Business problems addressed - Document processing platform and Scope is anything and everything under the bracket of document processing.

Vertex AI maybe well suited for healthcare where you can facilitate medical image analysis because Vertex AI capabilities can be well used for predicting diseases and other such use cases be it prescription completion or other NLP applications. Vertex AI may not be suitable for applications where it needs customization of Auto ML models since we are not aware of the parameters used there.

I used Vertex AI for one of my client who needed to combine data science and ML engineering workflows. We also tried to build own ML tool using this.

Helps developers to build gen AI applications

Vertex AI is a very useful tool for us, it simplifies automation by consolidating functionalities for engineers and data scientists, aiding in model deployment, tweaking and monitoring. It also allows gpu allocation for cost management and is compatible with Google Cloud services, plus it has a user friendly interface and robust documentation. Lastly just like any other google service, it is both scalable and ideal for team collaboration which makes it a very valuable tool for AI development.

We needed to build some ML models and Vertex AI is one such place where all the functionalities are consolidated for model deployment, tweaking and monitoring. And although it is costly, it is compatible with google cloud services and is scalable which makes it the perfect tool for us.