AI-900 intro and ML foundations

0:00 hey this is Andrew Brown and I'm bringing you another certification course and this time it's the Azure AI fundamentals also known as the AI 900 and if you're looking to pass a certification we have everything that you need here such as Labs lectures and a free practice exam so you can go ASAT exam get that uh certification put on your resume and Linkedin to go get that job you've been looking for um if you want to support more free courses like this one the best way is to purchase the additional uh paid materials where you can get access uh to more practice exams and other resources if you don't know me um I've taught a bit of everything uh here on the cloud that's been with adabs Azure gcp devops terraform kubernetes you name it I've taught it but uh you know you know the drill here let's get into it and learn more about the Azure AI fundamentals hey this is Andrew Brown from exam Pro and we are at the start of our journey here learning about the AI 900 asking the most important question which is what is the AI 900 so the Azure AI fundamental certification is for those seeking an ml role such as AI engineer or data scientist the

1:10 certification will demonstrate if a person can Define and understand Azure AI services such as competive services and Azure applied AI Services AI Concepts knowledge mining responsible AI basics of NL pipelines classical ml models autom ml generative AI workloads which is newly added content and Azure AI studio so you don't need to know super complicated ml knowledge here but it definitely helps to get you through there so this certification is generally referred to by its course code the AI 900 and it's the natural path for the Azure AI engineer or Azure data scientist certification this generally is an easy course to pass and it's great for those new to cloud or ml related technology looking at our road map you might be asking okay well what are the paths and what should I learn first so here are a few suggested routes if you already have your a900 that's that's a great starting point before you take your AI 900 if you don't have your a z900 you can jump right into the AI 900 but I strongly recommend you go get that a z900 because it gives you General foundational knowledge it's just another thing that you should not have to worry about which is just how to use Azure at a fundamental level do you need the dp900 to take the AI 900 no but a lot of

2:21 people seem to like to go this route where they want to have that data Foundation before they move on to the AI 900 because they know that the broad knowledge is going to be useful they so it's app pairing that you see a lot of people getting the AI 900 and the dp900 together for the AI 900 the path is a little bit more clear it's either going to be data scientists or AI engineer so for the AI engineer you have to know how to use the AI services in and out for data scientists it's more focused on setting up actual pipelines and things like that within Azure machine learning so you just have to decide which path is for you the data scientist is definitely harder than the AI engineer so if you aren't ready for the data science some people like taking the AI engineer first and then doing the data scientists so this is kind of like a warmup again it's not 100% necessary but it's just based

3:07 on your personal learning style and a lot of times people like to take the data engineer after the data scientists just to round out their complete knowledge now if you already have the a900 and the administrator associate you can safely go to the data scientist if you want to risk it because this one is really hard so if you've passed the a1004 you know you're going to probably have a lot more confidence learning up about all the concepts at this level here but of course it's always recommended to go grab these foundational CTS because sometimes course materials just do not cover the information and so the obvious stuff is going to get left out okay so moving forward here how long should you study to pass for the AI 900 if you're entirely new to ml Ai and Cloud providers such as Azure you should anticipate dedicating around 15 hours to grasp the basics this estimate can vary

3:53 base on your familiarity with these concepts for complete beginners the time commitment might extend to 20 to 30 hours for the intermediate level so people that have passed the a900 or dp900 you're looking at around 8 to 10 hours if you have one or more years of experience with Azure or another cloud service provider like a WS or gcp you're looking at about 5 hours or less the average stunny time is about 8 hours this is where you should be committing 50% of the time to the lecture in labs and 50% for the practice exams the recommended study time is 30 minutes to an hour a day for 14 days this should get you through it but just don't overstudy and just don't spend too little time what does it take to pass the exam well you got to watch the lectures and memorize key information do Hands-On labs and follow along with your own Azure account I'd say that you could probably get away with just watching all the videos in this one without having to do the labs but again it really does reinforce that information if you do take the time there is some stuff that is in Azure AI Studio or machine learning you might be wary of launching instances because we do have to run instances and they will cost money unless you delete the instances after use resulting in very small costs so if

5:03 you feel that you're not comfortable with that by just watching the videos you should be okay but when you get into the associate tier you absolutely have to expect to pay something to learn and take that risk you want to do paid online practice exams that simulate the real exam as I've mentioned before I do provide a free practice exam and have paid practice exams that accompany this course that are on my platform exam Pro and that's how you can help support more of these free courses so can you pass this certification without taking a practice exam well azzure is a little bit harder if this isn't a WS exam I would say yes but for Azure exams like AI 900 dp900 and sc900 probably not it's kind of risky I think you should do at least one practice exam or go through the sample one there's a sample one probably laying around on the Azure

5:48 website let's take a look at the exam guide breakdown here and then in the following video we'll look at in more detail so it's broken down into the following domain so the exam has five domains of questions and each domain has its own waiting which determines how many questions in a domain that will show up so 15 to 20% will be described Ai workloads and considerations 20 to 25% will consist of describe fundamental principles of machine learning on Azure 15 to 20% will consist of described features of computer vision workloads on Azure 15 to 20% will be described features of natural language processing workloads on Azure and 15 to 20% will be described features of generative AI workloads on Azure I want you to notice it's says describe these domains this is good because that tells you it's not going to be super hard if you start seeing things that say Beyond describe and identify then you know it's going to be a bit harder so where do you take this exam well you can take it in person at a test center or online from the convenience of your own home so there's two popular test centers there's CER aort and there's Pearson view you can also take it at a local test center if there are nearby locations the term Proctor means a supervisor or person

6:56 that is monitoring you while you're taking the exam if I had the the option between in person or online I would always choose the in person because it's a controlled environment and it's way less stressful online there are many things that can go wrong but it's up to your personal preference and your situation the passing grade here is 700 out of a th000 so that's around 70% I would say around because you could possibly fail with 70% because these things work on scaled scoring for response types there's about 37 to 47 questions and you can afford to get about 10 to 13 questions wrong so some questions are worth more than one point some questions cannot be skipped and the format of questions can be multiple choice multiple answer drag and drop and hot area there shouldn't be any case studies for foundational level exams and there's no penalty for wrong questions so for the duration you get 1 hour that means about 1 minute per question the time for this exam is 60 Minutes your C time is 90 minutes C time refers to the amount of time that you should take to allocate for that exam so this includes time to review the instructions read and accept the NDA complete the exam and provide feedback at the end this certification is going to be valid forever and it does not expire Microsoft

8:07 fundamental certifications such as the a900 or ms9900 do not expire as long as the technology is still available or relevant so we'll proceed to the full exam guide [Music] now hey this is Andrew Brown from exam Pro and what we've pulled up here is the official exam outline on the Microsoft website if you want to find this year s you just have to type in AI 900 Azure or Microsoft you should be able to easily find it the page looks like this what I want you to do is scroll on down because we're looking for the AI 900 set of guide and from there we're going to scroll on down to the skills measured section and you might want to bump up the text Azure loves updating their courses with minor updates that don't generally affect the outcome of the study here but it does get a lot of people worried because they always say well is your course out of date so no they're just making minor changes because they'll do this like five times a year and so if there was a major revision what would happen is they would change it so instead of being the AI 900 it would be like the AI 9001 or 9002 similar to how the AI 102 was previously AI 100 but now it's the AI 102 so just watch out for those and if it's a major

9:16 revision then yes it would probably need a completely new course so there aren't any major changes with the new update other than the update for the generative AI workloads on Azure section A couple of name changes and a few things being removed everything else remains is relatively the same with very minor changes so the concepts and such are still up to date overall I think the exam is easier than the four so let's go through some of the topics and work our way through here so describe Ai workloads and considerations so here we're just kind of describing the generalities of AI so content moderation workloads involve filtering out inappropriate or harmful content from user generated inputs ensuring a safe and positive user experience personalization workloads analyze user behavior and preferences to tailor content recommendations or experiences to individual users computer vision workloads involve the analysis of images and videos to recognize patterns objects faces and actions identify natural language processing knowledge mining document intelligence and features of generative AI workloads note that these are all just Concepts you don't need to know how to use the services at a high level then you have the responsible AI section so Microsoft has these six

10:25 principles that they really want you to know and they push it throughout all their AI services so those are the six you'll need to know and they're not that hard to learn moving on we have described fundamental principles of machine learning on Azure so here it's just describing regression classification clustering and features of deep learning we have a lot of practical experience with these in the course so you will understand at the end what these are used for next we have core machine learning Concepts we can identify features and labels in a data set so that's the data labeling service describe how training validation data sets are used in machine learning so we'll touch on that describe capabilities of Automated machine learning automl simplifies building and picking the best models while data and compute Services provide the power you need for training with Azure machine learning it helps with managing and deploying your models letting you put your machine learning projects into action smoothly under computer vision workloads we have image classification object detection optical character recognition facial detection and facial analysis Solutions next we have Azure AI Vision Azure AI face detection and Azure AI video indexer the Azure AI Services

11:34 Encompass a wide range of tools designed to facilitate the development of intelligent applications these Services used to be called computer vision custom vision face service and form recognizer but have Evol or been grouped under broader service categories to streamline their application and integration into projects for NLP we have key phrase extraction entity recognition sentiment analysis language modeling speech recognition synthesis this one doesn't really appear much it's kind of a concept not so much something we have to do and then there's translation so now we have Azure tools and services for NLP workloads these include the Azure AI language service Azure AI speech service and Azure AI translator service these used to be separate Services I believe like the text analytics service Lewis speech service and translator text service but they have been added to the Azure AI umbrella of AI services and now we'll be moving on to the generative AI workloads on Azure we'll be covering features of generative AI models common scenarios for generative Ai and responsible AI considerations for generative Ai and also some of the cool features that Azure open ey service has to offer such as natural language generation code

12:44 generation and image generation so that's about a general breakdown of the AI 900 exam [Music] guide hey this is angrew Brown from exam Pro and we are looking at the layers of machine learning so here I have this thing that looks like kind of an onion and what it is it's just describing the relationship between these uh ml terms uh uh related to Ai and we'll just work our way through here starting at the top so artificial intelligence also known as AI is when machines that perform jobs that mimic human behavior so it doesn't describe uh how it does that but it's just the fact that that's what AI is uh one layer underneath we have machine learning so machines that get better at a task without explicit programming uh then we have deep learning so these are machines that have an artificial neural network inspired by the human brain to solve complex problems and if you're talking about someone that actually assembles either ml or or deep learning uh models or algorithms that's a data scientist so a person with multi-disciplinary skills and math statistics predictive modeling machine learning to make future predictions so what you need to understand is that AI is just the outcome right and so AI

13:52 could be using ml underneath or deep learning or a combination of both or just IFL statements okay [Music] all right so let's take a look here at the key elements of AI so AI is the software that imitates human behaviors and capabilities and there are key elements according to Azure or Microsoft as to what makes up AI so let's go through this list quickly here so we have machine learning which is the foundation of an AI system that can learn and predict like a human you have anomaly detection so detect outliers or things out of place like a human computer vision be able to see like a human natural language processing also known as NLP be able to process human languages in referr contexts you know like a human at conversational AI be able to hold a conversation with a human so you know I wrote here according to Microsoft and Azure because you know the the global definition is a bit different but I just wanted to put this here because I've definitely seen this as an exam question and so we're going to have to go with azure's definition here okay let's define what is a data set so a

15:04 data set is a logical grouping of units of data that are closely related to or share the same data structure and there are publicly available data sets that are used in uh learning of Statistics data analytics and machine learning I just want to cover a couple here so the first is the mnist database so images of handwritten digits used to test classify cluster image processing algorithms commonly used when learning uh how to build computer vision ml models to translate handwritten into or handwriting into digital text so it's just a bunch of handwritten uh numbers and letters and then another very popular data set is the common objects in context Coco data set so this is a data set which contains many common images using a Json file Coco format that identify objects or segments within an image uh and so this data set has a lot of stuff in it so object segmentations recognition and it contexts super pixel stuff segmentation they have a lot of images and a lot of objects uh so there's a lot of stuff in there so why am I talking about this and in particular Coco data sets well when you use um Azure machine Learning Studio it has a DAT data labeling service and um the thing is is that uh it can

16:15 actually export out into Coco formats that's why I wanted you to get exposure to what Coco was and the other thing is is that when you're building out Azure machine learning uh pipelines you uh they actually have open data sets which we'll see later in the course um that shows you that you can just use very common ones and so uh you might see mest and uh the other one there uh so I just wanted to get you some exposure