The explosion of interest in AI due to the recent success of ChatGPT, the state-of-the-art natural language generation model that can write anything from essays to poems to code, is no surprise. However, now we are starting to see the excitement wane as ChatGPT usage numbers drop. This could be due to competition, concerns about privacy and security, or the overall excitement factor slowing down as users struggle to find uses of the tool. Further, to use the API available from OpenAI, you need a lot of technical skills and resources to train, fine-tune, and deploy it. You also need to be careful about the quality and safety of the generated text, as it might contain errors, biases, or harmful content.
The good news? This is just one tool in a sea of many other AI tools that are refined and purpose-built for many organizational needs. At the top of that list of tools is Microsoft’s Azure Cognitive Services tools, a collection of cloud-based APIs that provide ready-made AI solutions for various scenarios. Anyone who is familiar with Data Science and Machine Learning knows that we need troves of clean and trustworthy data to train an ML model to be able to predict results. The beauty of Cognitive Services is that Microsoft has already built these models around many categories, and even won many “human-parity” awards! Below are just a few examples of how Cognitive Services can help you with your AI needs:
• Speech Recognition: This service allows you to convert speech to text in real time or from audio files. You can use it for voice commands, transcription, dictation, captioning, and more. You can also customize it with your own vocabulary and acoustic machine learning models.
• Computer Vision: This service allows you to analyze and understand images and videos. You can use it for face detection, emotion recognition, object detection, optical character recognition, video indexing, and more. You can also create your own custom vision models using a simple interface. I recently created a video with an overview of the service here: https://youtu.be/ac8fvBWgUHg
• Text Analytics: This service allows you to extract insights from text data. You can use it for sentiment analysis, key phrase extraction, entity recognition, language detection, and more. Another example would be to use it to analyze healthcare documents and extract clinical information.
• Many more: Cognitive Services offer a wide range of services for different domains and scenarios, such as natural language understanding, conversational AI, anomaly detection, spatial analysis, personalization, and more.
You don’t need to worry about building or managing your own AI models, or if required, many of the services allow for custom models to be built as well. Once determined, you just need to connect to the API and start using it in your applications. Even better, many of the services can be containerized within a docker container where you can deploy the models locally or in other clouds for an even faster prediction for your application. Finally, you also get the benefits of Microsoft’s expertise and innovation in AI, such as high accuracy, reliability, security, and compliance.
To get started, many of the services have free service tiers for minimal transactions, and each service is billed based on consumption, so as long as you are controlling those transactions, you don’t have to worry about cost overruns, etc.
So, what are you waiting for? If you want to add AI capabilities to your applications without the hassle and complexity of ChatGPT and similar tools, Cognitive Services are the way to go! And if you really want to go deeper in understanding all the capabilities check out our recent book “Practical Guide to Azure Cognitive Services” from Packt or through other online book retailers: https://bit.ly/44NKm04
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Category Archives: AI
3 Key Differences in ChatGPT and Azure OpenAI
In this vLog I discuss some misconceptions around ChatGPT and Azure OpenAI, to include:
- Who owns ChatGPT, OpenAI, and how Microsoft got involved
- Security and privacy concerns about Azure OpenAI and ChatGPT
- How each of the services is consumed and billed
Take a look to find out more!
| ChatGPT | Azure OpenAI | |
| Ownership | Owned by OpenAI LP a for profit arm of OpenAI the non-profit who’s mission is to development of society | Part of Azure AI offerings as APIs, and investor in OpenAI for exclusive rights to technology generated |
| Security | Insecure and open to the public. LLM is trained on dataset created in GPT 3 and currently only references data from 2021 and earlier. Questions and interactions are captured and can be used for further training | Secure to an Azure tenant using GPT-4, GPT 3.5, Codex and Dall-e Requires access for tenant to reduce the chances of the AI to be used for malicious purposes Data is not stored by the prompt, and model is not trained on data added |
| Costs | Free during preview stages, or paid for better hardware availability | Based on a consumption model like other Azure Cognitive Services. Biggest expense is re-training the model you’ve deployed for your data |
Azure Cognitive Services
AI solutions are exploding, and Azure has the most complete offering of any cloud provider! Watch this video to get started with our API based Cognitive Services in Azure and a sample architecture of how to employ them with the Azure Bot Service. Azure Cognitive Services are cloud-based services with REST APIs and client library SDKs available to help you build cognitive intelligence into your applications.
You can add cognitive features to your applications without having artificial intelligence (AI) or data science skills. Azure Cognitive Services comprise various AI services that enable you to build cognitive solutions that can see, hear, speak, understand, and even make decisions. Azure Bot Service enables you to build intelligent, enterprise-grade bots with ownership and control of your data. Begin with a simple Q&A bot or build a sophisticated virtual assistant.
https://docs.microsoft.com/en-us/azure/cognitive-services/what-are-cognitive-services
https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows
https://dev.botframework.com/
#Azure #AI #CognitiveServices #ArtificialIntelligence #Bots #ReferenceArchitecture #MachineLearning #API #Cloud #Data #DataScience
Microsoft Reimagines Traditional SIEMs with Azure Sentinel
If you’re like most, security is at the forefront of your mind for your organization. You need the right tools and the right team to keep up with the balance of increasing number of sophisticated threats and with security teams being inundated with requests and alerts.
Today I’d like to tell you about Microsoft’s reimagined SIEM tool Azure Sentinel. Over the past 10 to 15 years, Security Information and Event Management (SIEM) has become extremely popular as an aggregation solution for security and events that happen in our network.
There are also software tools, hardware appliances and managed service providers that can help support your corporate needs to better understand the level of risks in real-time and over a span of time. They do things such as log aggregation, event correlation and forensic analysis and offer features for alerting, dashboarding and compliance checks.
These are great resources to help secure our environment, our users and devices. But unfortunately, the reality is security teams are being inundated with requests and alerts. Compile this with the noteworthy shortage of security professionals in the world – an estimated 3.5 million unfilled security jobs by 2021 – this is a major concern.
Microsoft decided to take a different approach with Azure Sentinel. Azure Sentinel provides intelligent security analytics at cloud scale for your entire enterprise. It makes it easy to collect data across your entire hybrid organization on any cloud, from devices to users to applications to servers. Azure Sentinel uses the power of AI to ensure you’re quickly identifying real threats.
With this tool:
- You’ll eliminate the burden of traditional SIEMs as you’re eliminating the need to spend time on setting up, maintaining and having to scale the infrastructure to support other SIEMs.
- Since it’s built on Azure, it offers virtually limitless cloud scale while addressing all your security needs.
Now let’s talk cost. Traditional SIEMs have proven to be expensive to own and operate, often requiring you to commit upfront and incur high cost for infrastructure maintenance and data ingestion. With Sentinel, you pay for what you use with no up-front costs. Even better, because of Microsoft’s relationships with so many enterprise vendors (and more partners being added) it easily connects to popular solutions, including Palo Alto networks, F5 networks, Symantec and Checkpoint offerings.
Azure Sentinel integrates with Microsoft Graph Security API, enabling you to import your own threat intelligence feeds and to customize threat detection and alert rules. There are custom dashboards that give you a view to allow you to optimize whatever your specific use case is.
Lastly, if you’d like to try this out for free, Microsoft is allowing you to connect to your Office 365 tenant to do some testing and check it out in greater detail. This product is currently in preview, so there may be some kinks but I’m looking forward to seeing how it develops in the future, as a true enterprise-class security solution for your environment, whether in the cloud, on premises, in data centers or remote users or devices.
Accelerate Your AI with Machine Learning on Azure Data Box Edge
In some past blogs I’ve discussed Azure Data Box and how the Data Box family has expanded. Today I’ll talk about Azure Data Box Edge (in preview) and elaborate on the machine learning service that it provides in your premises with the power of Azure behind it.
If you don’t know, Azure Data Box Edge is a physical hardware device that sits in your environment and collects data from environment sources like IOT data and other sources where you might take advantage of the AI features offered by the device. It then takes the data and sends it to Azure for more processing, storage or reporting purposes.
Microsoft recently announced Azure Machine Learning hardware accelerated models provided by Project Brain Wave on the Data Box Edge. Because most of our data is in real world applications and used at the edge of our networks – like image and videos collected from factories, retail stores or hospitals – it can now be used for things such as manufacturing defect analysis or inventory out of stock detection in diagnostics.
By applying machine learning models to the data on Data Box Edge, it provides lower latency (and savings on bandwidth cost) as we don’t have to send all the data to Azure for analysis. But it still offers that real time insight and speed to action for critical business decisions.
You can enable data scientists to simplify and accelerate the building, training and deployment of machine learning models using the Azure Machine Learning Service which is already generally available. They can access all these capabilities in their favorite Python environment, using the latest open source frameworks such as PyTorch, TensorFlow and sci-kit-learn.
These models can run on CPUs and GPUs, but this preview expands that out to field programmable gate array processes (FPGA), which is the processor on the Data Box Edge.
The preview is currently a bit limited but, in this case, you’re able to enhance the Azure Machine Learning Service by training a TensorFlow model for image classification scenarios. So, you would containerize that model in a docker container and then deploy it to the Data Box Edge IOT hub.
A good use case for this is if you’re using AI models for quality control purposes. Let’s say you know what a finished product should look like and what the quality specs are, and you build a model defining those parameters. Then you take an image of that product as it comes off the assembly line; now you can send those images to the Data Box Edge in your environment and more quickly capture defects.
Now you’re finding the root cause of defects quicker and throwing away fewer defective products and therefore, saving money. I’m looking forward to seeing how enterprises are going to leverage this awesome technology.