Share your experience using OpenVINO

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.

It provides the most valuable external analytics. Whether I'm searching for patterns or conducting keyword-based analytics, I consistently achieve better accuracy with Vertex compared to OpenAI from Azure.

While it employs chat activity for answering queries, the available options might be somewhat limited. I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process.

We have been using it for the past three months.

I find it easy to both scale out and scale up. I don't foresee any issues when the user volumes increase; the Google Platform automatically handles it, which is helpful.

Google's support is impressive. They are highly responsive and go the extra mile to assist users in trying out and ensuring they stand out in the market. I would rate it ten out of ten.

Positive

Previously we were using Azure OpenAI. In certain aspects, Azure outshines Vertex, particularly in total text analytics and, more notably, in multi-language capabilities. However, when it comes to generating content from a robust knowledge base, OpenAI is my go-to because of its extensive knowledge repository. The choice between platforms depends on the specific use case at hand.

The initial setup was easy, especially for someone with a technical background like myself. It didn't require much effort, and the abundance of documentation made the process smooth.

Our current activities involve proof of concept and piloting. Currently, our pilot phase appears to be successful, and we are examining volume metrics to determine the next steps for transitioning into actual production deployment.

As a product company with implementations spanning eight different platforms, we plan to offer this specific feature selectively to enterprise-grade customers based on their needs. I'm confident that there's substantial value addition for end customers using it, leading to a significant return on investment.

Overall, I would rate it eight out of ten.

The primary use case for Amazon SageMaker is leveraging its compute power, particularly for tasks like securing LMM notebooks using node instances. Additionally, its GPU capabilities are valuable for executing large language models. Users can create endpoints and access them from anywhere as needed.

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed.

The main function I prefer in Amazon SageMaker is the ability to create endpoints for large models. I haven't explored features like Studio Lab yet, but I've found the tutorials very helpful. The platform is user-friendly, with documentation attached to everything, making it easy to navigate and learn. Overall, I especially like the Studio Lab feature.

In the Studio Lab, tutorials provide direct snippets for tasks like connecting to S3 from Amazon SageMaker. These standard snippets make implementation straightforward and simplify the development process for me.

In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints. 

In the three months I've been using it, I've noticed that higher GPU instances can be quite costly. To mitigate this cost impact, serverless GPUs would be beneficial.

I have been working with the product for three months. 

I rate the solution's stability a nine out of ten. 

I rate the tool's scalability an eight out of ten. No issues with scalability as long as we ensure we have the necessary quotas in place before implementing a scalable process. I needed to request quota increases for certain services beforehand, and once those were provided, I could adjust the main and max nodes accordingly based on our planned requirements. My company has 25 users. 

We can schedule a direct call with the support team. 

Amazon SageMaker's Studio Lab feature differentiates it from products like Azure ML Studio. With Studio Lab, I can directly interact with the environment, making navigating and accessing documentation easier. In contrast, finding documentation and navigating Azure ML Studio was challenging.

However, we also use Azure for the Azure OpenEdge service, which operates on a pay-per-minute token basis. This payment model is not available in Amazon SageMaker. 

The initial setup and deployment process for Amazon SageMaker is straightforward. The only complexity I encountered was gaining access to the needed resources, which relied on coordination with the DevOps team. Once I had access sorted out, implementing my ideas for large language models and other models was comfortable. 

The tool's pricing is reasonable. 

I rate the overall solution an eight out of ten.