Google Cloud Avis et détails du produit

Google Workspace provides a solution that's very convenient and easy access to all users. All is cloud-based and collaboration betweens users is smoother.

The only downside of Google Workspace is the the familiarity of the users esp. to those companies who just migrated from Microsoft or others. It takes some time to study and familiarize the features Google Workspace offers. After getting used to, the convenience will come after.

Google Workspace provides smooth collaboration between users for our company. It saves us time, and improves our work efficiency.

Good format,nice layout,easy to execute.serverless architecture allows for quick and scalable data processing, enabling SQL queries on terabytes in seconds and petabytes in minutes

the lack of shortcuts to move between tabs, slower error detection, limited tab title display, inefficient search pane, and a less user-friendly interface with larger icons and less distinguishable colors

Scalability, cost efficiency ,Reliability ,Real-Time Analytics, Serverless Architecture.

The best thing about Google Workspace is that it has some of the most efficient tools one have free of charge or at low cost. Compared to other software, Google Workspace is very compatible with Chrome browser with ease of use and efficiency. It can be best set of Office suite applications available apart from Microsoft Outlook features.

At the moment, I can't find anything on Google Workspace I can comment on because it provides everything as a collaborative workspace and office suite.

We are reselling Google Workspace as the Third third-party reseller to clients and customers who may need digital office suite products such as Google Workspace. And we are quite pleased that Google was able to develop such a product that benefits people especially when writing online documentation and willing or other office-related work.

1) Ease of usage - a very good simplistic user interface 2) Scalability -Can scale up to use petabytes of data 3) Speed - It has a greate speed in dealing with really massive datasets 4) Good customer support - Any query with respect to Bigquery usage is answered swiftly 5) Ease of Integration - BigQuery connectors allow easy transfer of data from one DB to the other. 6) Frequency of Use - Due to a higher frequency of use, Bigquery gives a comparatively higher edge in terms of cost management. 7) Ease of Implementation - Implementing SQL queries on Bigquery is a cakewalk

1) Data Ingress and Egress operation costs 2) Complex pricing structure 3) Data privacy and compliance

Bigquery is solving data processing challenges majorly scalable data warehousing, high performance analytics, simplified data management and so on. Its benefitting me by helping create the data lake which eases my task

I have vertex AI for NSFW image/video content detection at ShareChat. I am amazed by the ease of data processing, model training, and deployment using vertex AI vision framework. There are many pretrained models for vision which are available for exploratory experimentation. I find Vertex's generative AI framework quite appealing. There are all popular google's foundation models like Gemini,ImagGen are availabe for us to tune on our downstream task. The development and deployment is also quite easy. Particulary, the MLOps provided by Vertex AI is the best of all. It helps orchestrating workflows,track metadata to ML models, identify best model for use cases, train them, deploy and maintain all in a compatible flow. It has large number of features, which is very nice. I have used it frequently.

* Vertex AI customer support is very bad. I have had instances where I needed help with the support team but was not received on time. * Its difficult to transfer preexisting workflows to vertex AI. * Debugging in vertex AI can also be improved. * Integration is not that easy in vertex AI * Implementaion of custom models and complex workflows are not that easy as well.

Not Safe For Work Content Detection System Visual Question answering for ecommerce in fashion

Ease of use ease impementation Learning new things

Translating subtitles can present various challenges for translators

translate media on site is to be sensitive and appreciative of cultural nuances

I liked time efficiency through automation tool

In my understanding Nothing . till now

It used to train and deploy ML models

This google cloud help us for various task like sentiment analysis, entity Regocnition, text anotation, as a developer I don't to do much more code for our NLP project this API help to perform these task very easy with high accuracy.

Some time fail to define tha polarity of tipical word or other language irrelevant words.

I worked on the Course recommendation project using NLP with our team We used Google cloud NLP API and this help very well it remove all stopper and create connection between course and rating of course and help to create the model with accuracy of 94 percent.

Google Cloud BigQuery stands out for its remarkable speed and scalability. It's like having a supercharged engine for data analysis, allowing you to query massive datasets in seconds, which is a game-changer for businesses dealing with large volumes of data.

One thing that can be a bit frustrating about Google Cloud BigQuery is its pricing structure. While it offers great performance and scalability, the costs can add up, especially for larger datasets and complex queries

Google Cloud BigQuery is a lifesaver for big data analytics, solving numerous challenges and bringing immense benefits to my work. Firstly, it handles massive datasets with lightning speed, allowing me to query and analyze huge volumes of data in a fraction of the time it would take with traditional databases

Vertex AI is basically google's collection of all AI and ML services in one place. You can use it for ML models and Gen AI applications. most of the services are very easy to use and are self explanatory. And talking about frequency of use, I have used it about 3 or 4 times , all in my projects. Since it is a relatively new product, its customer support is not that great but you can get all you need to know, from online forums.

One thing which I dont like about Vertex AI is that it takes a good amount of time to set up. for example, if you want to change your model from locally deployed to production environment, you need to tweak the settings to get it to work but once it is done, it is low maintainence. So the ease of integration could be easier, but again, production environments are generally difficult to configure. Customer support was okay.

Since Vertex AI is Google's All in one solution for all ML and AI tasks, it has actually helped me and the best part is that whatever you need, it is probably already in it.