Azure OpenAI Reviews

I was freelancing for a company that wanted me to make tutorials on how the platform can be used. So, here are just a few model-building video tutorials I made from the platform. That's pretty much it.

It's very easy and convenient to use compared to others. It has good documentation, and it's very easy to follow. So somebody using it for the first time finds it very convenient.

The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice.

Maybe Azure OpenAI could provide a few video tutorials, in addition to the documentation. If they want to make it easier for somebody to do it for the very first time, providing video tutorials might be a good idea.

So, I would like to have a tutorial added for new users. 

I have only worked for around a month or so.

I would rate the stability a nine out of ten. It is very stable. 

I would rate the scalability a seven out of ten. 

I took up a course that gave me access to Amazon. But when I compare OpenAI with Google and Amazon because I work with both Google and Amazon, I would put OpenAI, then Google, then Amazon.

So, Azure OpenAI is on top of my list. They've got a very user-friendly platform, so that works best. Amazon is slightly complex. Google provides video tutorials, but somehow Azure has a better UI.

I would rate my experience with the initial setup a seven out of ten, where one is difficult, and ten is easy. 

Deployment was slightly complex for me to understand. So, my senior was working on it, but I did not directly deploy it. The instructions are very clear on how to deploy it, so it is fine, and it doesn't take a lot of time. It hardly takes a few minutes, I think, d depending on the data. If the dataset is very big and if the model is complex, then maybe deployment will take more time. But if it's something very simple and basic, deployment was fine.

I would suggest you should give it a try. Overall, I would rate the solution an eight out of ten. 

We used the model to produce the dashboards. They created them, but we weren't satisfied because they weren't interactive for decision-making and governance. We integrated Azure with SharePoint and AI to create an interactive model. That's what we did with Azure for our specific project.

We use Azure, but what we put in place is not just Azure. We created interactive dashboards. These allow people to instantly understand the situation when they see a red code, for instance. This enables governance to make a strong diagnosis of the situation and resolve it. 

It also helps integrate all the digital elements that affect decision-making in project resolution. This allows for evaluation and restructuring of project scope with an agile approach, and to put in place solutions to integrate stabilizing elements. 

The project I completed for this specific issue last year was a big success and is now being used by the entire department.

I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers.

Our approach is stronger due to the algorithm we use. The system manages the equilibrium between different project environments. Many projects are executed in a stable environment, but when you create reorganizations, you destabilize the environment. Agile methodologies are necessary in such cases, but managing projects with MS Project in the field involves a stable environment. 

When these two environments interact, it creates resistance and digitalizing elements that hinder project realization. My mandate was to eliminate these obstacles and integrate the stabilizing and unstable environments. This ensures the establishment of a stable environment for the projects being realized. We created a significant part of the project to achieve this and replanned to ensure we could restabilize the project environment. We developed new management techniques and integrated them with various AI models.

For my needs, when working with interactive dashboards, it's expensive. I would prefer a system that provides alternative dashboard options or allows me to go directly into the program and pinpoint problems for decision-makers.

I'm an IT employee implementing solutions for clients who have specific requirements. As a Guidewire PolicyCenter status at Guidewire, I can manage teams with up to 200 professionals. 

This allows me to integrate many specialists and multi-disciplinary specialists into my teams and create strong solutions for clients.

It's good for a regular user, but for someone like me who creates and implements solutions, it is okay. 

The technical team is very helpful and easy to work with.

Positive

Throughout my career, I've been an expert in IT integration with a Guidewire approach. I work with strategies and implement them using Guidewire techniques and IT solutions. I've integrated solutions both vertically and horizontally in projects for clients. I've also created interactive dashboards with AI, using it as an expert system. This allows for a fully integrated solution within multi-project environments with complex issues. I began this in 1988.

The first project I worked on after my master's was presented by the federal government, Environment Canada, for the St. Lawrence River and the Great Lakes. They had a big budget and asked me to conceptualize all the programs that integrated many departments.

They asked me to reorganize and restructure the project to manage quality and ensure continuity after projects were completed. They wanted to ensure technology and budget were used effectively.

We have recently put in place SharePoint. We restructured the system with Microsoft 365 and integrated it with budget and document management. We organized all the applications. 

Before I arrived, many directors had uncontrolled access to the budget. We implemented a governance system similar to the Business Development Bank to ensure budget control. We integrated everything into Microsoft 365, including MS Project and SharePoint. We used insights to facilitate Kubernetes assessment and the assessment of projects in the field.

If I compare it with other IT I've worked with, I like to work with Appian. I think it's very strong, and for me, it's a benchmark to compare others. I also like to work with MuleSoft. It's another approach, but very interesting for me. When I compare with Microsoft 365, it's good but doesn't necessarily allow me to resolve all the issues I have. With Appian, we can find the solution we need; any kind of requirement we have, we're able to find an approach or solution within the system.

I have used Azure. I have forty years of experience in reorganization and business transformation. When IT can't directly meet my needs, I ask my technicians and analysts to examine the specific case for the project. In this instance, we used Azure to create interactive dashboards. They reprogrammed and worked with SharePoint to integrate Azure into the AI, the internal artificial intelligence.

The integration and the solution modeling can be complex. 

The pricing really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-making and governance, then the cost might be justified. 

In a recent project, I achieved strong results using only 60% of the allocated budget. The client was impressed. They were curious about my approach, but I assured them it was simply my way of working.

I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers. I use any kind of IT that I can, depending on the needs and the strategy we want to implement. IT can offer some services, but they have a suite of services that they don't offer, and we have to create and integrate with the IT. 

I work with my team to upgrade the IT we use. We integrate it with, for example, artificial intelligence like OpenAI to resolve or address the specific issue I want to solve. That's my way of working. IT can't stop me from putting a solution in place. I prefer to add to it or create a completely operational solution that can satisfy the client's exact needs.

For example, the problems in a specific project were major. When I finished, I had implemented a solution that answered the project's/client's exact needs. We reorganized the entire project structure, which allowed the company to use the IT we adapted. We put in place specific applications for governance and project management in the field. 

As a program manager, I communicate the needs and the desired results and evaluate what technology can offer based on the requirements. People offer me solutions. If it's on Azure, that's okay. If it's on Microsoft 365, that's fine too. 

I have techs who work for me and present solutions that I assess with them, considering the complexity of integrating all the necessary applications. If the solution satisfies my requirements, I authorize it, and we structure the project. We integrate all the issues and stabilizing elements into the project scope and manage it like any other project.

Azure OpenAI, for me, it's a component I use in my solution to ensure the application I want is realized. That's my approach. I'm a program manager, a person who manages IT architecture, project management, and change management. The requirements of the clients are my guide. Based on that, I will organize the solution.

I've always modified Azure to create interactive solutions. But it depends on the kind of application you want. I can recommend it for standard documentation, but not for developing innovative solutions. My requirements are more advanced.

Overall, I would rate it a seven out of ten. 

We use OpenAI for the insurance process to analyze documents and insurer-to-client requests in the public parts of our process. 

We plan to use it for confidential parts as well. This is the main solution we're aiming for.

We have a case from a company where we need to generate a complex report for a customer, comparing multiple documents. We plan to use OpenAI for this.

The most valuable features include analyzing comments and preparing requests for customers, making emails easier and faster.

Sometimes, it gives answers in English, even when the request is in Polish. That's the main reason it's not a perfect ten.

So, the language support could be better.

We started a few months ago. It was a good first choice but not the best.

I would rate the stability a nine out of ten. It is quite good. 

We haven't had any problems with scalability. We have around 40 end users.

We will increase the number of users.

We're in touch with customer service and support because we plan to implement Azure and Azure OpenAI. We also have a dedicated contact person at Microsoft, so we haven't had any issues getting support.

We currently use OpenAI, but we've decided to use Azure in the future.

My colleagues from the programming team handled the setup. I don't know the specifics, but they didn't have any issues using it.

We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution.

Overall, I would rate the solution an eight out of ten.

I chose Azure OpenAI and would recommend it to others because it's easy to set up, and I plan to use the cloud, which eliminates concerns about equipment and other infrastructure.

We are assisting our customers in deploying a commercial universal AI solution aimed at aiding them in researching and managing their internal company policies and regulations. To do this, I've extracted all the relevant documents from the HR department and created conversational interfaces for our clients. These interfaces are integrated into various platforms like Microsoft Teams, allowing everyone within the company to interact with the AI.

Its main use for indexing documents and assembling information is highly effective. Previously, we had to meticulously map out each process and step, essentially creating a chatbot for the task.

The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment.

We encountered challenges related to question understanding. These instances occur when questions are not phrased precisely, resulting in problematic answers. Microsoft is actively addressing this issue and working diligently on improving it.

I have been working with it for six months now.

We have nearly thirty customers using our system, and I can't recall any instances where they've encountered stability issues.

I would rate its scalability capabilities seven out of ten.

We have a direct connection with all the technical support staff in the support area. I would rate it nine out of ten.

Positive

We tried integrating Google in the past, but it didn't proceed as planned so we just stopped it.

The initial setup was straightforward.

The pricing is acceptable, and it's delivering good value for the results and outcomes we need.

My advice is to pay close attention to the content's quality before indexing it within OpenAI. If the documents provided lack good quality, they'll end up with incorrect answers. This is particularly important because the initial setup is not inexpensive and it involves significant investments. Overall, I would rate it nine out of ten.

We're implementing an assistant using Azure OpenAI. The challenge is grounding OpenAI responses to our specific data. 

We can only offer users basic querying, like for documents they're stuck on. It handles the request. It's primarily the question-answering feature.

It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search. 

It's focused on information retrieval and question-answering, which suits our needs perfectly. It is more like a natural language query tool we leverage.

We use Azure OpenAI alongside Azure Cognitive Search. These are both new services we've deployed. There's a process where we need to ask Microsoft to create private endpoints to link OpenAI to Azure as a connectivity service.

Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources. 

As a governance department, accuracy and control are crucial. We're trying to tune the system to stick with our content, but it's an ongoing challenge. 

We've been working on fine-tuning prompts and parameters for about four weeks now.

I've been using Azure OpenAI as a creative source for the past six months.

We've noticed some issues with scaling. It takes time for the service to adapt when we increase the load. We're still in the pre-production phase, and we're seeing this even during testing. 

Also, there's limited capacity in our region (Canada East), which makes it difficult to accommodate the expected load. We've submitted capacity increase requests, but we're not sure if they'll be approved.

The main challenge we've faced is around capacity. Even after running extensive load tests, we don't have sufficient capacity to handle our projected volume.

We have a consultant from Microsoft working with us. They've been very helpful.

However, they're very busy. We could use more of their time if they were available. But they're very competent and helpful. We just wish we could have more access to their expertise.

Positive

We have an alternative search engine that indexes our document base. We use Azure OpenAI's question-answering feature to query that index, generating answers from relevant documents.

We don't use GPT-4 specifically, nor are we training any models. Our IT group leverages Azure OpenAI for its existing capabilities.

It is our first implementation of this kind.

There are some limitations right now. For our specific use case, where we need a traditional information retrieval system, it's not an ideal fit. 

Azure OpenAI is a question-answering system built on top of information retrieval, and that distinction is important for us. Given our use case, I don't think it's well-suited.

Our management team requires accurate and complete results, with precision that matches our existing keyword search tools. It's difficult to evaluate and prove that Azure OpenAI consistently meets that standard. 

We're still early in our adoption, so the rating could change as we deploy it to a larger audience.

For now, I would rate the solution a five out of ten. 

One of the tasks for which I found the use of Azure OpenAI to be useful for my business is related to the area of annotations in images.

Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required.

I would like Azure Open AI to provide more integrations with other platforms.

The cost of the product should be lowered.

I have been using Azure OpenAI for six to seven months.

It is a stable solution.

The scalability part of the product depends on whether you have declared the product on an on-premises model and what kind of configurations you are keeping with your back-end servers. I cannot talk about the product's scalability since the tool has more areas like outcomes, precision, and accuracy.

Conversational AI is used across hospitals. The hospital runs Azure OpenAI for EMRs. Businesses have started using AI components for various applications.

The technical support part is documented, and my business works together with Azure OpenAI.

The technical support required by our business depends on the algorithms and the models being developed, which is not what Azure OpenAI provides. It basically lies with the user to solve a problem.

My company works not only with Azure OpenAI but with foundation models, too.

The product's initial setup phase was pretty easy. Installation is not an issue in the tool, but achieving the outcomes matters to our company, which is dependent on algorithms, models, and how much data you use to train your models.

The solution is deployed majorly on the cloud and then on an on-premises model.

The steps that can be deployed in Azure OpenAI include areas like integration with your applications.

Accessibility from your applications and browser is required to deploy the product.

My company has a team of several solution providers who work together. My company has partnered with some of the startups in our ecosystems, so they work with us.

There are around 30 to 40 percent cost-saving outcomes in our company from the use of the solution.

According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution.

With Azure OpenAI, there are a number of alignments that my business is into.

My company works with Azure OpenAI and our own private LLMs.

Though Azure OpenAI is not optimized, it is one of the best when it comes to text generation.

Azure OpenAI is regarded as a foundation model on which our company plans to use our private LLMs.

The natural language understanding capability of Azure OpenAI has improved our company's data analysis since we use the product's integration capabilities for areas like translations and conversational AI.

I recommend the solution to those who plan to use it, but there are also other products that are available on the market.

I rate the overall tool a nine out of ten.

Implementing Azure OpenAI has notably streamlined our document creation process, increasing efficiency and productivity.

It aligns with our organization's compliance policies and data security requirements, assuring regulatory compliance.

It enhances our AI-driven projects by seamlessly integrating with tools like GitHub CoPilot, improving real-time coding capabilities, and facilitating development workflows.

In the next release, they could enhance the product's features for even greater usability and efficiency.

I have been working with Azure OpenAI for approximately one year.

I rate the platform's stability a seven. 

Currently, over 1000 users within our organization utilize Azure OpenAI.
I rate the platform's scalability an eight. 

There can be delays in receiving responses from the technical support team. 

Neutral

The initial setup has been relatively straightforward, although it may present challenges for beginners, particularly when deploying with infrastructure as code.

Depending on the backend infrastructure, the deployment typically takes just a few minutes, ranging from two to five minutes. Two executives are required to handle the operations. 

I rate the process around a seven.

I rate the product pricing six out of ten.

The product is integrated into our business workflows, particularly within our application development platforms.

The writing capabilities have been particularly crucial for generating descriptive content, such as case studies and product descriptions.

The document intelligence feature has significantly aided in our operations, facilitating the creation of descriptive content.

I recommend it to others, particularly those already utilizing Microsoft products or seeking a robust AI solution.

I rate the product a nine.

Our team has developed virtual assistants for healthcare organizations, also published in Azure Marketplace. This can be used for a personal assistant perspective. We have also developed an application for one of the fertilizer companies. Here, a farmer can go to their application, click a photo of any disease or progress in the plant, and it will identify what type of fungus or disease that plant has. Accordingly, it will recommend what kind of fertilizers and how to use them. These are a couple of use cases we have worked on.

It is easy to integrate and develop a solution. Most customers are concerned about the security of their data and how cost-effective it is. We have developed some methodologies so that our customers will not be charged too much for these OpenAI services but will still get the same kind of performance and results. It's all developed on Azure, so customers also see its benefit.

I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator. 

Sometimes, customers check the prices independently, but the details of the pricing parameter and the documentation sheet are inconsistent. This confuses customers, making them unsure if the service is live or how to test it.

I have been working with the product for a couple of days. 

I haven't faced any issues with the tool's stability. 

The tool is highly scalable. I don't think any of my customers have faced any issues from a performance or scalability perspective when the load on their website has increased. We designed the solution to be highly scalable and reliable so they don't face challenges at the application or performance level. I have not seen any customers complaining about issues or performance problems. 

My company has seven to eight clients using Azure OpenAI. Because of data concerns, all features should be available in at least one data center in each region. For example, there are three data centers in India, and until recently, Azure OpenAI was available in only four regions. Now, it is also available in one of the data centers in India. If Microsoft introduces a new product, it should ensure it is available in at least one data center per region so that customers from that region can validate it.

Azure OpenAI's deployment is easy. It depends on a couple of things, including the solution we are developing. If it is a normal chatbot, it shouldn't take more than two to three days to complete the project. However, it might take longer if the customer has a very complex environment.

You can enjoy cost savings because you need only less expertise. Traditionally, IT services or SLM models require a lot of computing power to train models, but Microsoft has already invested in them. Customers can directly use this high-efficiency model available on the Microsoft platform. Microsoft has already done that, they don't have to build anything from scratch or use high computing resources. So, there is a good ROI.

I rate the overall product an eight out of ten. You should start by researching and experimenting with Azure OpenAI. You can create a simple chatbot or a computer vision model that identifies objects in images. This will help you understand how it works and develop use cases based on your requirements. I would also suggest testing those use cases.

In India, I'm seeing that customers across various industries, whether manufacturing, BFSI, or healthcare, are all starting to use AI in some form. Each customer develops solutions for cross-selling and processing products based on specific use cases. They design user stories for their customers, create offers to grab attention, integrate interactive chatbots into their applications or websites, and develop AI-based visitor and document management systems. Everyone wants to use AI differently, but no two use cases are the same.