This blog post will give a quick understanding of the topics covered in Microsoft Azure Artificial Intelligence Fundamentals [AI-900] Training and some common questions asked during Day 1 Live training on Microsoft Azure AI Fundamentals AI-900 Certification.
The Azure AI-900 Certification is for all those who are looking forward to starting working with or shifting their career in Azure AI and Machine Learning domain. It focuses on different Azure Machine Learning modules (algorithms, data preparation modules, etc.) and includes Custom Vision/computer vision, Data Science, NLP, Cognitive Services, Conversational AI, etc.
On our Day 1 Live Session, we have covered Introduction to Azure, Artificial Intelligence in Azure, Responsible AI, Azure Machine Learning and also performed hands-on, where we have created Bird species Classifier.
Here are the questions that we discussed in Day 1 Session:
Introduction to Azure
Microsoft Azure commonly referred to as Azure, is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life. solutions including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
Source: Microsoft
What are Microsoft Azure products and services?
Following are the Azure products and services and use cases =
- Compute. These services enable a user to deploy and manage VMs, containers as well as support remote application access. Compute resources created within the Azure cloud can be configured with either a public IP address or a private IP address.
- Web. These services support the development and deployment of web applications.
- Storage. This category of services provides scalable cloud storage for structured and unstructured data. It also supports big data projects.
- Networking. This group includes Virtual networks, dedicated connections, and gateways, as well as services for traffic management and diagnostics, load balancing, DNS hosting, and network protection.
- content delivery network (CDN). These CDN services include on-demand streaming, digital rights protection, encoding and media playback, and indexing.
- Integration. These are services for server backup, site recovery, and connecting private and public clouds.
- Internet of things. These services help users capture, monitor, and analyze IoT data from sensors and other devices.
- DevOps. This group provides project and collaboration tools, such as Azure DevOps.
- Security. These products provide capabilities to identify and respond to cloud security threats, as well as manage encryption keys and other sensitive assets.
- Artificial intelligence (AI) and machine learning. This is a wide range of services that a developer can use to infuse AI and ML machine learning and cognitive computing capabilities into applications and data sets.
- Containers. These services help an enterprise create, register, orchestrate and manage huge volumes of containers in the Azure cloud, using common platforms such as Docker and Kubernetes.
- Databases. This category includes Database as a Service (DBaaS) offerings for SQL and NoSQL, as well as other database instances — such as Azure Cosmos DB and Azure Database for PostgreSQL. It also includes Azure SQL Database support. It is a relational database that provides SQL functionality without the need for deploying a SQL server.
- Migration. This suite of tools helps an organization estimate workload Migration costs and perform the actual migration of workloads from local data centers to the Azure cloud.
Source: Microsoft
Introduction to AI(Artificial Intelligence)
Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines “man-made,” and intelligence defines “thinking power”, hence AI means “a man-made thinking power.” Simply put, AI is the creation of software that imitates human behaviors and capabilities.
AI focuses on three cognitive skills: learning, reasoning, and self-correction or problem-solving –
In Learning processes: AI programming focuses on data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Reasoning processes: AI programming focuses on choosing the right algorithm to reach the desired outcome.
Self-correction processes: It is designed to ensure that algorithms provide the most accurate results possible.
Here are some FAQs:
Q.1 Write application of AI and Major Goals?
Ans. Applications of AI = Gaming, Natural Language Processing, Expert Systems, Vision Systems, Speech Recognition, Handwriting Recognition, Intelligent Robots, etc.
Major Goals:
- Machine learning– This is often the foundation for an AI system, and is the way we “teach” a computer model to make predictions and draw conclusions from data.
- Anomaly detection– The capability to automatically detect errors or unusual activity in a system.
- Computer vision– The capability of software to interpret the world visually through cameras, video, and images.
- Natural language processing– The capability for a computer to interpret written or spoken language, and respond in kind.
- Conversational AI– The capability of a software “agent” to participate in a conversation.
Q.2 What are the Business Benefits of AI and ML?
Ans. The Business Benefits of AI and ML are:
- A Better Customer Experience: One of the top benefits of using AI is the enhanced customer experience it enables. Artificial intelligence enables organizations to put enhanced product offerings through the rigorous, ongoing analysis of consumer activity.
- Improve Business Efficiency: One other benefit to artificial intelligence is the ability to automate business processes.
- Increase Data Security: A huge plus of AI is the fraud and threat protection possibilities it is able to bring to organizations. Since AI monitors user activity, it can learn to detect potential security threats, both internally and externally.
- Identify New Business Opportunities: With AI, your organization will be able to uncover a multitude of new business opportunities by combing through market, customer, and company data and searching for associations. For one example, AI can help identify quality leads and discard low-quality leads by automatically assessing the validity of form-fills.
- More sources of data input: AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources.
Q.3 What is responsible AI?
Ans. Artificial Intelligence is a powerful tool that can be used to greatly benefit the world. However, like any tool, it must be used responsibly. At Microsoft, AI software development is guided by a set of six principles, designed to ensure that AI applications provide amazing solutions to difficult problems without any unintended negative consequences.
Source: Microsoft
Q.4 Explain all six guiding principles of responsible AI?
Ans. Fairness: AI systems should treat all people fairly. For example, suppose you create a machine learning model to support a loan approval application for a bank. The model should make predictions of whether or not the loan should be approved without incorporating any bias based on gender, ethnicity, or other factors that might result in an unfair advantage or disadvantage to specific groups of applicants.
Reliability and safety: AI systems should perform reliably and safely. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of systems can result in substantial risk to human life.
Privacy and security: AI systems should be secure and respect privacy. The machine learning models on which AI systems are based rely on large volumes of data, which may contain personal details that must be kept private. Even after the models are trained and the system is in production, it uses new data to make predictions or take action that may be subject to privacy or security concerns.
Inclusiveness: AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.
Transparency: AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected.
Accountability: People should be accountable for AI systems. Designers and developers of AI-based solutions should work within a framework of governance and organizational principles that ensure the solution meets ethical and legal standards that are clearly defined.
Q.5 How is Azure AI Ecosystem?
Ans. An AI Eco-system can be defined as a group of AI systems together to some extent to achieve a common goal. The most common goal of establishing an AI ecosystem is “automation via applied ML”. This is generally achieved by putting AI systems in both real-time as well as historical scenarios and generating intelligence out of it.
There are three basics AI ecosystems Pre-Built AI, Custom AI, and Conversational AI.
Q.6 What is Anomaly Detection?
Ans. The capability to automatically detect errors or unusual activity in a system. Anomaly Detector service provides an application programming interface (API) that developers can use to create anomaly detection solutions. It also assesses your time-series data set and automatically selects the best algorithm and the best anomaly detection techniques from the model gallery. Use the service to ensure high accuracy for scenarios including monitoring IoT device traffic, managing fraud, and responding to changing markets.
Source: Microsoft
Q.7 Explain Anomaly Detection Architecture?
Ans. To learn more, Anomaly Detection
Data source: Microsoft
Q.8 AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected. Which principle of responsible AI does this come under?
Ans. Transparency
Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. ML is the way we “teach” a computer model to make predictions and draw conclusions from data.
Also Check: Introduction To Data Science and Machine Learning
Source: Microsoft
Here are some FAQs:
Q.1 Explain different types of Machine Learning algorithms?
Ans. There are three types of Machine Learning algorithms –
- Supervised learning = Data sets are labeled so that patterns can be detected and used to label new data sets.
- Unsupervised learning = Data sets aren’t labeled and are sorted according to similarities or differences.
-
Semi-Supervised = In Semi-Supervised Learning Input data is a mixture of labeled and unlabeled examples.
- Reinforcement learning = Data sets aren’t labeled but, after performing an action or several actions, the AI system is given feedback.
Check more on Reinforcement Learning
Source: IBM
Q.2 Explain Azure ML Architecture?
Ans. The following diagram shows Azure Machine Learning architecture:
Source: Microsoft
Q. 3 Explain Features supported by Azure Machine Learning?
Ans. As shown in the diagram above, Azure ML supports the following business-critical features:
1. Scalable on-demand compute: Azure ML supports many compute (aka. compute targets), including its powerful compute cluster. It is scalable auto-on-demand compute usage for machine learning workloads. That means, in a business sense, you pay only for what you use.
2. Data Storage and connectivity: Provides an easy-to-use, yet powerful data storage layer. It is so flexible, yet powerful, that data storage can be easily integrated with Python Pandas data frame and Spark data frame
3. Metrics and monitoring: Feature is very helpful to data scientists and machine learning engineers. You can get various metrics for a given business case and often, it helps you to see how different metrics are behaving
Other features such as ML orchestration using Pipeline and model registration.
Q.4 Comparison to understand deep learning Vs machine learning Vs AI?
Ans. Different b/w deep learning Vs machine learning Vs AI –
- Deep learning is a subset of machine learning that’s based on artificial neural networks. The learning process is deep because the structure of artificial neural networks consists of multiple input, output, and hidden layers. Each layer contains units that transform the input data into information that the next layer can use for a certain predictive task. A machine can learn through its own data processing.
- Machine learning is a subset of artificial intelligence that uses techniques that enable machines to use the experience to improve at tasks.
- Artificial intelligence (AI) is a technique that enables computers to mimic human intelligence. It includes machine learning.
Also Check: Deep Learning Vs Machine Learning
Q.5 How do machines learn?
Ans. In today’s world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.
Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.
Also check: https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/2-understand-machine-learn
Q.6 How Machine learning Work?
Ans. Step 1: Collect and prepare the data: Once data sources are identified, available data is compiled. The type of data that you have can help inform which machine learning algorithms you can use.
Step 2: Train the workflow model: The prepared data is split into two groups: the training set and the test set. The training set is a large portion of your data that’s used to tune your machine learning models to the highest accuracy.
Step 3: Validate the workflow model: When you’re ready to select your final data model, the test set is used to evaluate performance and accuracy.
Step 4: Interpret the results: Review the outcome to find insights, and predict outcomes.
> Classification
Classification is a form of machine learning that is used to predict which category, or class, an item belongs to. For example, a health clinic might use the characteristics of a patient (such as age, weight, blood pressure, and so on) to predict whether the patient is at risk of diabetes. In this case, the characteristics of the patient are the features, and the label is a classification of either 0 or 1, representing non-diabetic or diabetic.
Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label so that the model learns to fit the feature combinations to the label. Then, after training has been completed, you can use the trained model to predict labels for new items for which the label is unknown.
Example – E-mail Filtering
Here are some FAQs:
Q.1 How Classification works?
Ans. Cognitive service: A general cognitive services resource that includes Computer Vision along with many other cognitive services; such as Text Analytics, Translator Text, and others. There are 28 types of Cognitive service.
Custom Vision: Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifiers.
> Linear Regression
Linear regression is a basic and commonly used type of predictive analysis. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
This equation defines a linear regression = y = m*x +b, where y is a dependent variable, x is an independent variable, and b is a constant.
Let’s see the below graph-
Note: Blue dots define real data, while a red line defines a linear regression equation, which shows that the amount of sales is very highly correlated with the advertising budget. However, the red line is not able to determine exact values.
Quiz Time (Sample Exam Questions)!
With our Microsoft Azure AI Fundamentals training, we cover more sample exam questions to help you prepare for the certification AI-900. Check out these questions and see if you can answer them.
Question: Is there any service for Machine learning from Microsoft Azure?
Ans. Yes, Microsoft Azure provides the Azure Machine Learning service – a cloud-based platform for creating, managing, and publishing machine learning models.
Question: What is the benefit of using the Azure Machine learning service?
Ans. Data scientists expend a lot of effort exploring and pre-processing data and trying various types of model-training algorithms to produce accurate models, which is time-consuming, and often expensive compute hardware. Azure Machine Learning is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage.
Most importantly, it helps data scientists increase their efficiency by automating many of the time-consuming tasks associated with training models.
Question: What is Machine Learning Studio?
Ans. You can manage your workspace using the Azure portal, but for data scientists and Machine Learning operations engineers, Azure Machine Learning Studio provides a more focused user interface for managing workspace resources.
Also Check: Machine Learning Studio
Question: Is python learning is necessary for Azure AI ML, I’m a dot net developer
can I use C# for AI ML?
Ans. It is not necessary, but Azure ML certification training requires Python and nowadays python is the most used language for AI, ML & Data Science.
Related/References:
- Microsoft Azure AI Fundamentals [AI-900]: Step By Step Activity Guides (Hands-On Labs)
- AI-900 Microsoft Azure AI-Fundamentals training.
- AI-900 Microsoft Certified Azure AI-Fundamentals Everything-you-need-to-know.
Next Task For You
To know more about AI, ML, Data Science for beginners, why you should learn, Job opportunities, and what to study to clear Microsoft Azure AI Fundamentals Certification [AI-900].
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