AI for DevOps

Generative artificial intelligence (AI) focuses on creating models that can generate content such as text, images, or even multimedia. Unlike discriminative models that classify or label existing data, generative models operate by learning patterns from the provided data and producing novel outputs. You'll begin this course with an overview of generative. You will explore some notable examples of generative models, including OpenAI's ChatGPT and Google Bard. Next, you will look at the use of prompt engineering when interacting with AI chatbots. Then, you will then delve into the history and evolution of generative AI models including important milestones that culminated in the conversational agents that we work with today. Finally, you will explore the risks and ethical considerations associated with generative AI, such as unintentional use of copyrighted data, the use of personal data for training, and the creation of malicious deepfakes using AI. You will also learn how you can mitigate some of these risks while working with generative technologies.

Generative Pre-trained Transformer (GPT) models are advanced artificial intelligence (AI) systems designed to understand and generate human-like text based on the information they've been trained on. These models can perform a wide range of language tasks, from writing stories to answering questions, by learning patterns in vast amounts of text data. In this course, you will dive into the world of GPT models and the foundational models that are pivotal to the development of the GPT-n series. You will gain an understanding of the terminology and concepts that make GPT models outstanding in performing natural language processing tasks. Next, you will explore the concept of attention in language models and explore the mechanics of the Transformer architecture, the cornerstone of GPT models. Finally, you will explore the details of the GPT model. You will discover methods used to adapt these models for particular tasks through supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and techniques such as prompt engineering and prompt tuning.

This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.

Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.

Generative artificial intelligence (GenAI) can create new content, such as text, images, and music. It is powered by machine learning (ML) models that have been trained on massive datasets of existing content. Prompt engineering is the process of designing and crafting prompts that guide generative AI models to produce the desired output. You will start this course by learning how you can leverage prompt engineering to improve your day-to-day and work-related tasks. Next, you will see examples of prompting in action with external generative AI chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat. As several of these tools may not be supported on many corporate devices, you will not be expected to create accounts on those platforms, but you will be able to apply the learnings and principles to any corporate conversational AI chatbot in similar ways.

Different types of prompts serve distinct purposes when interacting with language models. Each type enables tailored interactions, from seeking answers and generating code to engaging in creative storytelling or eliciting opinions. In this course, you will learn the four elements of a prompt: context, instruction, input data, and output format. Next, will also explore prompt categories, such as open-ended, close-ended, multi-part, scenario-based, and opinion-based. Finally, you will look at different types of prompts based on the output that they provide. You will use prompts that generate objective facts, abstractive and extractive summaries, classification and sentiment analysis, and answers to questions. You'll tailor prompts to perform grammar and tone checks, ideation and roleplay, and mathematical and logical reasoning.

Data generation prompts instruct language models to generate synthetic data, useful for creating datasets. Code generation prompts are used to produce code snippets or entire programs, aiding developers in coding tasks. Zero-shot prompts challenge models to respond to unfamiliar tasks, relying on their general knowledge. Few-shot prompts provide limited context to guide models in addressing specific tasks, enhancing their adaptability. You will start this course by working with data generation and code generation prompts. You will explore how to use starter code prompts, convert code from one language to another, and prompt models to explain a piece of code. Next, you will see how to leverage generative AI to debug your code and generate complex bits of code with step-by-step instructions. Finally, you will explore techniques to improve prompt performance.

In the rapidly evolving landscape of information technology (IT), the integration of generative artificial intelligence (AI) is becoming increasingly essential. Generative AI, a branch of artificial intelligence, is responsible for generating content, such as text, images, and even code that is often indistinguishable from that created by humans. This course is designed to provide IT professionals with the knowledge and practical skills to leverage generative AI in the field of IT administration. Begin this course by exploring generative AI concepts and tools. You will learn how to deploy generative AI models and apply generative AI techniques on Azure. Then you will investigate how to create effective prompts and how to integrate your organization's data with Azure OpenAI models. Next, you will focus on analyzing generative AI models, automating tasks, and integrating generative AI tools. Finally, you will discover security concerns with using generative AI, find out how to apply security protocols, and troubleshoot generative AI. At the end of this course, you will be able to apply Generative AI models to address real-world IT administration challenges.

Unlock the transformative potential of advanced generative AI (GenAI) models in site reliability engineering (SRE). This course caters to SRE professionals, IT architects, and those eager to harness the full scope of generative AI in the context of SRE and covers the concepts of advanced generative AI techniques to bolster system reliability, scalability, and efficiency in SRE. In this course, we will begin with an overview of advanced GenAI and SRE. You'll explore how to deploy advanced models from the Azure AI model catalog and transition into testing approaches for applications integrating GenAI. Next, you'll deploy a GenAI-based application to Azure Kubernetes Service (AKS), explore the suitability of GenAI models for SRE, and see which SRE tools incorporate GenAI. With that foundation, you'll experiment with various methods for supporting SRE operations with GenAI including chatbots, implementing a backoff mechanism, evaluating model performance, and configuring log analytics for GenAI models on Azure. Lastly, you'll explore GenAI and SRE advancements, and fine-tune a GenAI model in Azure OpenAI Studio.

The rapid integration of artificial intelligence (AI) in information technology (IT) brings forth ethical responsibilities that demand critical attention. This course delves into the ethical and responsible use of AI in IT, providing IT professionals, developers, and decision-makers with the knowledge and tools needed to ensure AI models are designed and deployed ethically. Begin by exploring ethical considerations and biases and the implications of biased AI models. Then you will learn how to design an AI model while incorporating legal and compliance considerations and use anonymization to ensure privacy. You will configure content safety filters in Azure OpenAI Studio to prevent the generation of harmful content. You will discover strategies, guidelines, and legal and compliance considerations for ethical AI model development, as well as the ethical impact of AI models. Next, you will examine common AI principles and goals and dive into approaches and algorithms to help identify and reduce biases. Finally, you will investigate how ignoring sensitive features when training a predictor does not necessarily address AI model disparities.

In a business environment where optimizing operational efficiency is pivotal, the integration of artificial intelligence (AI) automation stands out as a transformative approach to advancing IT operational frameworks. In this course, you will see how AI automation can be integrated with IT operations to design AI automation workflows that will enhance IT operations efficiency and automate IT tasks and processes effectively. Next, you will explore how IT automation can be evaluated and optimized using AI Tools and continuous improvement strategies. Finally, we will demonstrate the use of AI tools to integrate IT Operations.

Practical AI automation integration is not just a technological upgrade. It is a strategic enabler for enhanced system reliability, performance, and business continuity, allowing organizations to be more responsive and adaptive in a dynamic market landscape. In this course, you will see how AI-powered scripts can be used for enhanced IT support and site reliability engineering (SRE) operations, as well as tasks such as server monitoring and log analysis. Next, you will explore strategies for implementing secure and effective AI-powered IT solutions and evaluating their impact on IT support and SRE operations. You will also consider best practices for implementing secure and effective AI-powered IT solutions. Finally, you will discover how to optimize AI automation workflows for better performance in IT operations.

Successful DevOps has to monitor the entire life cycle and use tools to facilitate collaboration to maximize the speed, quality, and rapid deployments necessary for today's business environment. Artificial intelligence (AI) can be a fundamental tool in achieving these goals, allowing teams to focus on core work while leaving monitoring and other tasks to AIs. This course will introduce how AI can play a role in DevOps, including the benefits and limitations of using DevOps in this way. You will explore the ethics of using AI and how AI automation can impact DevOps efficiency and accuracy. Next, you will discover how to use generative AI in DevOps automation and how it can generate, complete, document, and explain relevant code. You will then explore how generative AI can impact continuous deployment, including integrating with tools like Bard, Jenkins, and ChatGPT. Finally, you will see how you can use generative AI to improve the handling and triaging of customer feedback.

Using artificial intelligence (AI)-powered cloud platforms to build, train, and deploy AI models can help combat the increasing complexity and resultant pain points of DevOps implementation. In this course, you will explore AI-powered cloud platforms like Amazon SageMaker, Google AI Cloud Platform, Microsoft Azure AI, and IBM Watson. Then you will assess the scalability, performance, and monitoring benefits of AI platforms in DevOps. Next, you will explore integration, delivery, and deployment strategies for AI-powered cloud platforms, learn how to integrate IBM's Watson for handling customer support, and work with Amazon CodeGuru to analyze code for security issues. You will also investigate Microsoft Azure AI solutions to create a pipeline using Azure AI DevOps. Finally, you will examine Google Cloud AI tools, including Duet AI, to modernize apps, create application programming interfaces (APIs), and use natural language prompts to analyze data.

As software demands for businesses increase, the need to release quicker and with fewer issues has become paramount. Continuous integration and continuous deployment (CI/CD) have become necessary steps in DevOps to improve software delivery and maintain automation. Now, with artificial intelligence (AI), it is possible to augment these steps to develop even better and faster processes. In this course, you will learn how AI can help in the CI/CD pipeline and some of the AI-based tools available to take advantage of the AI offerings and to make the transition smoother. You will begin the course with an overview of the CI/CD pipeline and look at advantages, disadvantages, and strategies for using AI-powered CI/CD pipelines. You will then explore AI tools that can support CI/CD pipelines and ways to optimize them. Next, you will discover how analytics and automation work with CI/CD pipelines. Finally, you will delve into some security options to fortify your CI/CD pipelines.

To build robust, secure, and performant systems it is necessary to be able to monitor and observe the entire DevOps lifecycle. Such monitoring can generate significant amounts of data and AI tools can be game changers for identifying issues, tracking down the root cause of problems, and some tools can even provide real-time metrics or predict future failures based on collected data. In this course, you will learn about monitoring and observability tools that incorporate elements of AI and the benefits that such tools can provide to the DevOps process.

Release management is responsible for managing the software process from initial development to deployment. When releases are smaller, shorter, and can be done quicker, it requires enormous effort to manage. In this course, you will discover how AI and AI-powered release management tools can enhance and optimize software release management. You will examine the impact that AI can have on release management, including how AI can help reduce errors and optimize workflows. Then you will investigate various AI release management tools. Next, you will explore release management with JFrog Artifactory, Bitbucket, and Jira. Additionally, you will learn how AI-powered release management tools can improve change management and enhance deployment reliability. Finally, you will use the GitLab tool to create, manage, and edit a release.

AI has impacted almost every phase of the DevOps life cycle, however, new technology like generative AI is still relatively new and continually improving. AI will likely continue to have an impact on DevOps well into the future. In this course, you will explore the potential future changes AI will bring to DevOps. You will identify future AI trends that will impact software development and influence test automation. Then you will discover how future AI will transform security, protecting networks and businesses from threats. Next, you will investigate how AI is changing business infrastructure, impacting observability, and influencing release and deployment. You will examine how AI can enhance collaboration, change how businesses make decisions, and transform continuous improvement. Finally, you will take a look at the impact of AI on business and occupations and prepare to integrate these advancements.