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Describe AI workloads and considerations

1. Machine Learning and Artificial Intelligence

So the fields of artificial intelligence and machine learning—these are so important in today's industry. We are using machine learning. Machine learning. We are using artificial intelligence to go ahead and train machines to do something that we, as humans, can't do. So we are trying to go ahead and expand the possibilities of what machines can actually do in today's world. Also, we are using machine learning and artificial intelligence to go ahead and reduce some mundane activities that we as users need to do and that we as human beings need to do. If you look at autonomous driving over there, we also use machine learning to go ahead and ensure that the car has the ability to go out and navigate through the roads. So, artificial intelligence and machine learning are being used a lot in today's industry. And that's why, as professionals, it's very important to go and understand all about artificial intelligence and machine learning. Now, when it comes to Azure, they have a lot of services when it comes to machine learning and artificial intelligence and a lot of features. So these services actually help us to carry out a lot of implementations when it comes to the area of machine learning and artificial intelligence. And I believe that is something that we will do in this course. So, again, I'm welcoming you on this journey of understanding the services in Azure. So we're going to cover the fundamentals of AI in Azure in this course.

2. Prediction and Forecasting workloads

Hi. Welcome back. Now, as part of the objectives of the exam, we have to go ahead and understand the different types of artificial intelligence workloads. So these are the common types of workloads that you would actually implement when you're making use of machine learning and artificial intelligence. So the first is prediction and forecasting. So let's say that a company is heavily investing in stocks. They want to go out and look at the historical data of stocks, and they want to go ahead and make predictions about the future. They want to go ahead and forecast. They want to see what the price of the stocks will be in the future. They can go ahead and make use of machine learning to learn from the historical data and then make predictions and forecasts about the future. So this is the first type of workload that you have when it comes to making use of machine learning. You take your historical data, we will apply machine learning on top of that, and then you go in and make your predictions and make your forecast. So this is actually very helpful in a lot of domains, right? So that's the first type of AI workload that I want to discuss when it comes to prediction and forecasting.

3. Anomaly Detection Workloads

Now, in the last video, it talked about prediction and forecasting. Now in this chapter, I want to talk about anomaly detection. This is another type of AI workload. So a normal detection is basically, when you look at your data, you're trying to see if there's any sort of deviation from normal usage patterns. Now, this is normally used by the banking sector. So let's say they want to go ahead and see if there is any sort of misuse of credit cards. Maybe the credit card has fallen into the wrong hands and they want to understand if this is not the normal usage pattern for that particular credit card. So this is a form of anomaly detection. Also in the medical field, if you want to go ahead and, based on the medical history of a person, see if there is any sort of anomaly behaviour in terms of the medical condition of the person, you can go ahead and make use of artificial intelligence when it comes to anomaly detection. Detection. So this is another type of AI workload that you can actually go ahead and implement. Right. So in this video, I just want to kind of give an introduction to anomaly detection.

4. Natural Language Processing Workloads

Now, another type of workload when it comes to AI is natural language processing. So over here, you are trying to interpret the language that is being used by a user. So let's say that there is a site in place wherein they have reviews for different restaurants. So over there, the review can be a positive review or a negative review. Now, when it comes to an artificial intelligence-based workload, when it comes to natural language processing, the workload should have the ability to go ahead and take the text of the review and understand whether the review is positive or negative. Remember, in artificial intelligence we are trying to ensure that we as humans don't have to go ahead and interpret those different reviews. So there could be thousands and thousands of reviews, and we as users don't want to go ahead and sit down and check if the review is positive or not. No, we want a machine. We want artificial intelligence to go out and take those reviews and tell us whether the reviews are positive or negative. And then accordingly, it can actually go ahead and define what the combined review is for that particular restaurant. So, for example, when it comes to workload, when it comes to natural language processing,

5. Computer Vision Workloads

Now, the next type of workload I want to describe is computer vision based workloads.And that's something we're going to see a lot of in this course. So computer vision is the ability of a machine to go ahead and process, let's say, images. So let's say you submit an image to a service. That service should have the ability to go ahead and process that image and give you information about the image. So the commercial vision service, which is available in Azure, has the ability to actually go ahead, take an image, and detect various objects within the image itself. So this is a very powerful service that is available in Azure. And another very common type of workload is one where you have a computer that has the ability to go ahead, take what it says in front of it, analyse it, and give you that relevant information. This is another type of artificial intelligence-based workload.

6. Conversational AI Workloads

The last type of workload that I want to discuss is conversational AI. Now we all like having conversations with another human being, but we also have the capability of having computers or machines also talk to us in a conversational way. We actually see this a lot when it comes to web chat bots, which are available for web-based applications. So over there, we'll actually be having a conversation with a machine on the other side thatis actually having a conversation with us.So in the art of artificial intelligence and machine learning over here, we are trying to train a machine to go ahead and have a healthy conversation with us, and that's the entire basis of conversation. AI right. With that, I come to the end of discussing the various types of workloads. when it comes to the exam. It is really important to go ahead and understand these different types of workloads because you can get questions when it comes to a scenario and what the right type of workload to assign for that particular scenario is.

7. Microsoft Guiding principles for response AI – Accountability

Hi and welcome back. Now, as part of the objectives of this section, you will see objectives when it comes to the Microsoft guiding principles for responsible AI. Now, this is also very important from an exam perspective. You can get quite a few questions answered when it comes to understanding responsible AI. So let's quickly go through all of those principles. The first thing is accountability. Now you are developing or making use of AI services to build an AI-enabled solution. But in the end, remember that you are accountable as a human being for that particular solution. It should not be that an AI-enabled solution is the final deciding factor. Remember, in the end, the accountability is with you as a human being. So that is the first guiding principle I want to discuss when it comes to responsible AI. Now there is no ranking process when it comes to the principles for responsible AI. But just from my perspective, the most important thing is accountability. I mean, accountability is important for every aspect. And when it comes to an AI solution, this is one of the most important principles.

8. Microsoft Guiding principles for response AI - Reliability and Safety

Now, another principle when it comes to responsible AI is reliability and safety. Let's say you are building an AI solution when it comes to the medical industry. So let's say it's a very crucial solution that goes ahead and detects if there's any sort of disease in a person, or let's say it is used to go in and detect if there's any sort of condition, a critical condition, for a particular patient. So when you're building an AI solution, it definitely needs to be reliable. It also needs to adhere to the different safety protocols when it comes to the medical industry. So depending on the industry you are building the AI solution for, you have to ensure that the solution is reliable and safe.

9. Microsoft Guiding principles for response AI - Privacy and Security

Now, another important principle when it comes to responsible AI is privacy and security. So normally, when it comes to AI and machine learning, it works with a lot of data, right? So we collect a lot of data, and the data might contain sensitive information. So as an AI-enabled solution, we have to enable the privacy and security of the underlying data. So, again, this is very important in today's world. We want to ensure that an AI-enabled solution respects the privacy and security of online data.

10. Microsoft Guiding principles for response AI – Transparency

Now, another important principle is transparency. So when you're building an AI solution, it should not be the case that nobody understands the fundamental principles of that AI solution or what happens internally in that AI solution. So transparency is important, for example, if you're looking at machine learning. So the developers, let's say, the person whois building that AI solution, they have tounderstand what the AI solution is meant for. If someone has already gone ahead and built a machine learning model, the developers should know what was used to train that model. What was the algorithm that was used? What was the type of data set that was used, respecting the privacy and security of the data? You start going ahead and also ensure that there is food transparency when it comes to the AI solution. So, again, this is another important principle when it comes to responsible AI.

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