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Free Download Platform Engineering - Build Scalable & Resilient Systems Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 21.50 GB | Duration: 40h 5m Learn DevOps, Infrastructure as Code, CI/CD, Kubernetes, more. Prepare for a successful career in platform engineering! What you'll learn Understand and Define Platform Engineering: Grasp the fundamental concepts, importance, benefits, and challenges of Platform Engineering. Master Infrastructure as Code (IaC): Implement IaC effectively using tools like Terraform and Ansible, enabling consistent & automated infrastructure. Design and Implement CI/CD Pipelines: Understand the principles of Continuous Integration & Continuous Delivery, build efficient pipelines, apply best practices Embrace Cloud-Native Technologies: Learn benefits & challenges of cloud-native architectures, hands-on with Kubernetes for container orchestration & deployment. Leverage Observability Tools: Implement monitoring, logging, and metrics collection with tools like Prometheus and Grafana for platform performance and health. Understand Platform Design Principles: Apply key design principles to build self-service platforms that empower development teams and improve overall efficiency Prepare for Platform Engineering Interviews: Develop a strong resume, practice answering common interview questions, and gain confidence through mock interviews Apply Platform Engineering Principles in Real-World Scenarios: Understand practical examples & implementation strategies to bridge gap between theory & practice Enhance Collaboration and Communication Skills: Foster DevOps culture necessary for effective collaboration and communication within cross-functional teams. Requirements Enthusiasm and determination to make your mark on the world! Description A warm welcome to the Platform Engineering: Build Scalable & Resilient Systems course by Uplatz.Platform Engineering is a discipline in software development focused on building and maintaining an internal platform that enhances the productivity and efficiency of developers and operations teams. The goal is to provide reusable, scalable, and automated infrastructure solutions that streamline the software development lifecycle, enabling faster delivery of applications.In essence, platform engineering allows for the creation of a standardized, highly automated environment where developers can focus on writing code, while platform engineers manage the complexity of the underlying infrastructure. This leads to improved developer experience, faster deployments, and reduced operational overhead.Key Aspects of Platform Engineering:Internal Developer Platform (IDP): Platform engineers design and build IDPs, which are custom platforms tailored to meet the specific needs of the development teams within an organization. These platforms typically abstract away complex infrastructure concerns, providing a unified and self-service environment where developers can easily deploy, monitor, and manage their applications.Automation: A core principle of platform engineering is automation. This includes automating infrastructure provisioning (using Infrastructure as Code), CI/CD pipelines, monitoring, and logging. The aim is to reduce manual intervention and repetitive tasks.Self-Service Capabilities: The platform provides self-service tools, allowing developers to independently create environments, deploy applications, and troubleshoot issues without relying on other teams (such as DevOps). This boosts developer autonomy and shortens development cycles.Scalability and Reliability: Platforms built by platform engineers are designed to scale with the needs of the business. They integrate fault-tolerance, monitoring, and observability to ensure high availability and reliability, crucial for large-scale applications.Collaboration between DevOps, Security, and Developers: Platform engineering bridges the gap between DevOps teams, developers, and security teams. It aligns infrastructure, tooling, and workflows with the requirements of each group, ensuring compliance, security, and faster software delivery.How Platform Engineering Works:Infrastructure as Code (IaC): Platform engineers use IaC tools (like Terraform, Ansible, or Pulumi) to automate and manage the underlying infrastructure, ensuring it can be provisioned and maintained consistently across different environments (development, staging, production).CI/CD Pipeline Integration: They integrate Continuous Integration/Continuous Deployment (CI/CD) pipelines into the platform. This involves tools like Jenkins, GitLab CI, or GitHub Actions to automate testing, building, and deployment processes.Containerization and Orchestration: Platform engineers leverage containers (e.g., Docker) and orchestration platforms (like Kubernetes) to abstract applications from the underlying infrastructure, making deployments consistent and scalable across environments.Observability and Monitoring: Platform engineers implement monitoring, logging, and alerting tools (e.g., Prometheus, Grafana, ELK Stack) to ensure the health, performance, and security of the applications running on the platform. These insights help in proactive troubleshooting and maintenance.Security and Governance: Security best practices are embedded into the platform, such as managing secrets (with HashiCorp Vault), ensuring compliance with organizational standards, and enforcing access control and security policies.Collaboration and Tooling: Tools are designed with usability in mind, allowing developers to access necessary resources through a unified interface (like a dashboard or CLI). This allows for consistent communication between platform engineers and development teams to ensure the platform evolves with their needs.Platform Engineering - Course CurriculumModule 1: Introduction to Platform EngineeringLecture 1: Define Platform Engineering, its importance, benefits, and challenges. Discuss the future of Platform Engineering.Module 2: Core ConceptsLecture 2: Explore the benefits of Infrastructure as Code (IaC) and learn about popular tools like Terraform and Ansible.Lecture 3: Understand CI/CD pipelines, their components, and best practices.Lecture 4: Learn about self-service platforms, their purpose, and design principles. Explore Platform APIs, their role, and development considerations.Lecture 5: Master platform observability, including metrics, logging, and monitoring.Lecture 6: Deep dive into platform observability, monitoring, and the future of observability.Module 3: Cloud-NativeLecture 7: Introduction to cloud-native concepts, benefits, and challenges.Lecture 8: Real-world examples of cloud-native and its future.Lecture 9: Introduction to Kubernetes architecture, components, and cluster management.Lecture 10: Container orchestration, deployment strategies, and best practices.Lecture 11: Serverless computing, use cases, benefits, challenges, and future.Lecture 12: Cloud security, best practices, and common threats.Module 4: Design Principles and Shell ScriptingLecture 13: Understand the design principles behind platform engineering.Lecture 14: Recap and connect the terminologies learned so far.Lecture 15 & 16: Introduction to Shell Scripting.Lecture 17: Implementation of Shell Commands and Cocalc.Lecture 18: CHMO - Understanding Programming BasicsModule 5: Kubernetes and Platform LifecycleLecture 19 & 20: Implementation of Kubernetes.Lecture 21: Platform lifecycle management, including planning, development, deployment, and operations.Module 6: Observability with GrafanaLecture 22: Understanding Grafana for observability.Lecture 23: Connecting Grafana and other automation tools.Lecture 24: Grafana Labs connections.Module 7: DevOps and ToolingLecture 25: Understanding Jenkins.Lecture 26: Connection between Kubernetes and JenkinsLecture 27: DevOps implementationLecture 28: Service connectionLecture 29: Platform Engineering documentationLecture 30: Understanding Redhat OpenShiftLecture 31: Understanding GitLabModule 8: Practical Examples and Interview PreparationLecture 32: Practical examples of Platform Engineering and implementationLecture 33 & 34: How to make a resume for Platform Engineering rolesLecture 35 & 36: Common Platform Engineering interview questions and answersLecture 37: Tips and rules for interviewsLecture 38, 39 & 40: Company-oriented interview questionsLecture 41: Top questions and framing answersLecture 42: LinkedIn sessionCourse RecapFinal Lecture: Review key concepts, achievements, and next steps.Benefits of Learning Platform Engineering1. Enhanced Career OpportunitiesPlatform engineering is a rapidly growing field with high demand for skilled professionals. By mastering platform engineering, you'll unlock a wealth of career opportunities, including roles such as:Platform EngineerCloud ArchitectDevOps EngineerSite Reliability Engineer (SRE)Software Engineer specializing in InfrastructureTechnical Lead or Manager2. Increased Earning PotentialDue to the high demand and specialized skillset required, platform engineers command competitive salaries and benefits.3. Improved Technical SkillsYou'll gain proficiency in a wide array of cutting-edge technologies and tools, including:Infrastructure as Code (IaC) (Terraform, Ansible)CI/CD Pipelines (Jenkins, GitLab CI/CD)Container Orchestration (Kubernetes)Cloud Platforms (AWS, Azure, GCP)Monitoring and Observability (Prometheus, Grafana)4. Greater Impact and InfluencePlatform engineers play a crucial role in enabling and empowering development teams. You'll have a direct impact on the efficiency, productivity, and success of the entire organization.5. Problem-Solving and InnovationYou'll be challenged to think critically, solve complex problems, and find creative solutions to streamline software delivery and optimize the development experience.6. Professional Growth and DevelopmentPlatform engineering is a constantly evolving field. By staying current with emerging trends and technologies, you'll ensure continuous professional growth and development.Career Path in Platform EngineeringThe typical career path in platform engineering often starts with roles like:Software Engineer/Developer: Gaining experience in building and deploying applications lays a strong foundation for understanding developer needs and pain points.DevOps Engineer: Building on development experience, DevOps engineers gain experience in automating and streamlining the software delivery process.System Administrator: Experience in managing and maintaining infrastructure can be leveraged to transition into automating infrastructure provisioning and management.From there, individuals can progress into specialized platform engineering roles, such as:Platform Engineer: Focus on designing, building, and maintaining internal developer platforms.Cloud Architect: Design and implement cloud solutions incorporating platform engineering principles.Site Reliability Engineer (SRE): Focus on ensuring the reliability and performance of the platform and its applications.With experience and expertise, platform engineers can move into leadership positions like:Technical Lead or Manager: Lead and mentor teams of platform engineers.Director of Platform Engineering: Oversee the strategy and execution of platform initiatives across the organization. Overview Section 1: Introduction to Platform Engineering - Benefits, Challenges, Future Lecture 1 Introduction to Platform Engineering - Benefits, Challenges, Future Section 2: Infrastructure-as-Code (IaC) with Terraform and Ansible Lecture 2 Infrastructure-as-Code (IaC) with Terraform and Ansible Section 3: Continuous Integration (CI) and Continuous Delivery (CD) Lecture 3 Continuous Integration (CI) and Continuous Delivery (CD) Section 4: API and Platform API Lecture 4 API and Platform API Section 5: Platform Observability - Metrics and Logging Lecture 5 Platform Observability - Metrics and Logging Section 6: Platform Observability - Monitoring and Future of Observability Lecture 6 Part 1 - Platform Observability - Monitoring and Future of Observability Lecture 7 Part 2 - Platform Observability - Monitoring and Future of Observability Section 7: Introduction to Cloud Native - Benefits and Challenges Lecture 8 Introduction to Cloud Native - Benefits and Challenges Section 8: Real-world Example of Cloud Native and Future Aspects Lecture 9 Real-world Example of Cloud Native and Future Aspects Section 9: Introduction to Kubernetes Lecture 10 Introduction to Kubernetes Section 10: Container Orchestration Lecture 11 Container Orchestration Section 11: Serverless Computing - Benefits, Challenges, Future Lecture 12 Serverless Computing - Benefits, Challenges, Future Section 12: Cloud Security - Best Practices and Future Lecture 13 Cloud Security - Best Practices and Future Section 13: Understanding the Design Principles Lecture 14 Understanding the Design Principles Section 14: Recap and Connecting the Terminologies Lecture 15 Recap and Connecting the Terminologies Section 15: Understand Shell Scripting Lecture 16 Part 1 - Understand Shell Scripting Lecture 17 Part 2 - Understand Shell Scripting Section 16: Shell Commands and CoCalc (Collaborative Calculation and Data Science) Lecture 18 Shell Commands and CoCalc (Collaborative Calculation and Data Science) Section 17: Understand Programming Basics Lecture 19 Understand Programming Basics Section 18: Implementation of Kubernetes Lecture 20 Part 1 - Implementation of Kubernetes Lecture 21 Part 2 - Implementation of Kubernetes Section 19: Platform Lifecycle Lecture 22 Platform Lifecycle Section 20: Grafana Lecture 23 Grafana for Observability Lecture 24 Connecting Grafana and other Automation Tools Lecture 25 Grafana Labs Connections Section 21: Understanding Jenkins Lecture 26 Understanding Jenkins Section 22: Connection between Kubernetes and Jenkins Lecture 27 Connection between Kubernetes and Jenkins Section 23: DevOps Implementation Lecture 28 DevOps Implementation Section 24: Service Connection Lecture 29 Service Connection Section 25: Platform Engineering Documentation Lecture 30 Platform Engineering Documentation Section 26: Red Hat OpenShift Lecture 31 Red Hat OpenShift Section 27: Understanding GitLab Lecture 32 Understanding GitLab Section 28: Practical Example of Platform Engineering and its Implementation Lecture 33 Practical Example of Platform Engineering and its Implementation Section 29: How to make Resume for Platform Engineering and DevOps Lecture 34 Part 1 - How to make Resume for Platform Engineering and DevOps Lecture 35 Part 2 - How to make Resume for Platform Engineering and DevOps Section 30: Interview Questions on Platform Engineering Lecture 36 Part 1 - Interview Questions on Platform Engineering Lecture 37 Part 2 - Interview Questions on Platform Engineering Section 31: Tips and Key Points for Platform Engineer Interview Lecture 38 Tips and Key Points for Platform Engineer Interview Section 32: Company-oriented PE Interview Questions Lecture 39 Part 1 - Company-oriented PE Interview Questions Lecture 40 Part 2 - Company-oriented PE Interview Questions Lecture 41 Part 3 - Company-oriented PE Interview Questions Section 33: Top Questions and Framing Answers for Platform Engineer Interviews Lecture 42 Top Questions and Framing Answers for Platform Engineer Interviews Section 34: LinkedIn Profile for a Platform Engineer Lecture 43 LinkedIn Profile for a Platform Engineer Software Engineers and Developers: Those who want to expand their skill set beyond application development and gain expertise in building and managing the underlying platforms that support software delivery.,DevOps Engineers: Those looking to deepen their understanding of platform engineering principles and tools to streamline development, deployment, and operations processes.,Platform Engineers: Those already working in platform engineering roles seeking to expand their knowledge, learn new tools and techniques, and stay current with industry best practices.,Beginners and newbies aspiring for a career in cloud and platform engineering.,Anyone interested in learning DevOps, platform engineering, and site reliability engineering both from architecture and implementation point of view.,Cloud Architects: Those responsible for designing and implementing cloud solutions at scale, and looking to integrate platform engineering principles into their overall architecture.,Cloud Engineers: Those seeking to enhance their knowledge of cloud-native technologies and their application in building scalable and resilient platforms.,System Administrators: Those transitioning to a more development-centric role and wanting to learn how to automate and manage infrastructure using code.,IT Professionals: Those aspiring to move into the growing field of platform engineering and gain a competitive edge in the job market.,Technical Leaders and Managers: Those responsible for overseeing software development and infrastructure teams and seeking to optimize their processes and improve delivery efficiency. 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Free Download GenAI Application Architecture - Scalable & Secure AI Design Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 2h 40m | Size: 2.51 GB Build scalable, secure, and efficient GenAI applications with AWS, MLOps, monitoring, and cloud-native architecture What you'll learn Design Scalable GenAI Applications: Learn to architect and build scalable GenAI applications using the LGPL architecture, focusing on Layer, Gate, Pipes Implement Resiliency and Error Handling: Understand how to incorporate error handling, monitoring, logging, and disaster recovery to create resilient GenAI Apps Ensure Security and Cost Efficiency: Develop secure and cost-effective GenAI solutions by leveraging AWS security services, containerization Automate and Optimize with MLOps & CI/CD: Learn to implement MLOps, CI/CD, and Explainable AI (XAI) for streamlined deployment and future-proofing GenAI apps Requirements Basic Knowledge of AI and Machine Learning: Understanding of fundamental AI and machine learning concepts. Familiarity with AWS: Experience with AWS services such as Lambda, S3, and DynamoDB is recommended. Programming Skills: Intermediate-level knowledge of Python is essential. Basic Understanding of Software Architecture: Familiarity with software architecture principles such as scalability, load balancing, and error handling. Description Master the essential techniques and best practices for designing and architecting scalable, secure, and cost-effective Generative AI (GenAI) applications. In this course, you'll explore the principles of the LGPL architecture (Layers, Gates, Pipes, and Loops) and how they apply to building GenAI systems using modern cloud services like AWS. We'll cover critical topics such as load balancing, containerization, error handling, monitoring, logging, and disaster recovery. This course is ideal for those looking to understand GenAI architecture, ensuring applications are resilient, secure, and efficient.What You'll Learn:Architect scalable and secure GenAI applications using the LGPL model.Understand core concepts such as containerization, load balancing, and disaster recovery.Learn best practices for monitoring, logging, and error handling in GenAI systems.Explore MLOps, CI/CD, and security strategies for future-proofing AI applications.This course focuses on the architecture and principles behind building robust GenAI systems, providing the knowledge needed to design effective AI solutions.Enroll now to transform your GenAI Application Architecture skills to the next level. Master GenAI Application Architecture - the core best practices and techniques for building secure, efficient, scalable GenAI Applications.Ready to take your skills to the next level? Join me, and let's get started. See you inside the course! Who this course is for AI Developers and Engineers: Those looking to build scalable, secure, and cost-effective GenAI applications. Cloud Architects: Professionals working with AWS who want to implement GenAI architectures using best practices. Machine Learning Enthusiasts: Individuals with a foundational understanding of machine learning and programming who want to expand into GenAI development. Software Engineers: Engineers seeking to integrate AI into cloud-native applications and implement MLOps pipelines Homepage https://www.udemy.com/course/genai-application-architecture/ Rapidgator https://rg.to/file/eb0dab6db8b173a5e31e2c1124e05cb1/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part1.rar.html https://rg.to/file/a15457d993ba63048e481b780ea9f7a6/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part2.rar.html https://rg.to/file/ce1437543d5df735599d92cd55596194/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part3.rar.html Fikper Free Download https://fikper.com/kOled1XDOx/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part1.rar.html https://fikper.com/5kl1QK2FDE/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part2.rar.html https://fikper.com/sW8thybbOP/idmrc.GenAI.Application.Architecture.Scalable..Secure.AI.Design.part3.rar.html No Password - Links are Interchangeable

Building A Scalable Serverless Application on AWS MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours | 410 MB Genre: eLearning | Language: English Serverless computing allows you to build and run applications and services without thinking about servers. With serverless computing, your application still runs on servers, but all the server management is done by AWS. Using AWS and its Serverless Platform, you can build and deploy applications on cost-effective services that provide built-in application availability and flexible scaling capabilities. This lets you focus on your application code instead of worrying about provisioning, configuring, and managing servers. The course will show viewers how to build a fully serverless, fully scalable application from the ground up, leveraging some of Amazon's suite of application services, including API Gateway, Lambda, Cloudfront, DynamoDB, S3, and SQS. We will start by gaining an understanding of what it means to be "serverless", and how some of this cutting-edge technology can help us to build serverless applications before we move on to designing and implementing our very own serverless API. By the end of the course, you will understand the clear benefits of next-generation serverless architecture, such as never again needing to worry about how to scale an application or manage virtual servers. Download From NitroFlare http://nitroflare.com/view/5732A67EB95B0BD/xidau123_BuildingAScalableServerlessApplicationonAWS.rar Download From Rapidgator https://rapidgator.net/file/c6e6f24f8dcf81962f7db0d50ac0b7f8/xidau123_BuildingAScalableServerlessApplicationonAWS.rar.html

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Udemy - Build scalable apps on Google App Engine With Michael Gradek 130 lectures | 12 hours | MP4, AVC, 1152x720, 30.00 fps | AAC, 44100 Hz, 64 kbps, 2 Ch | 2.44GB Genre: eLearning Learn what Google's platform offers to easily build scalable apps in no time. No previous coding experience required Web app development isn't hard, but there sure is a lot of material to go over. I wanted to create a comprehensive course because I feel if someone is serious on building something great, you need to have knowledge on many of the different aspects of creating web apps. In addition, I didn't want to create a boring course which goes over the basics of programming and then, separately, shows how to develop feature after feature without a cohesive vision. Instead, this course is project based, meaning that we will develop an entire web application throughout the course, and along the way, learn about programming basics and all of the different aspects on web app development as we encounter the need to implement certain features or learn certain concepts. In this course our main project will be building a re[beeep]e search engine using Python and Google App Engine! Yeah, a full-blown re[beeep]e search engine. It's kind of like a smaller version of Google, where the only thing you can search for are re[beeep]es instead of the entire web. Check the "What am I going to get from this course?" to see all of the different concepts we are going to learn :) Python is in my eyes, the best programming language to learn when you're starting. It reads like english so it's easy to understand the code and what it's doing, and Python tends to be terse, meaning that it's neatly concise and free of syntactical and verbal redundancy. Or in other words, it's clean and productive, where one line of Python code can do what maybe 5-6 lines of code do in other languages. Python is also a lot of fun to code! Google App Engine is an astonishing technology we can use to deploy our apps. It takes away all of the headaches of managing servers and infrastructure so you can focus 100% on your application code. Whenever your app gets popular and receive a peak of traffic you don't have to worry about your servers crashing over the increased load. Google App Engine automagically spins up new servers to serve your users. In addition, App Engine provides many extremely useful services we will use, but that we don't have to install and configure. For example, we don't need to install nor configure a database, a email server, full text search indices, queues, etc. All of that is already build by Google on App Engine and we simply need to call those services to use them. Do you know how much time that saves us in the development process? A ton! All of that allows us to rapidly develop apps, test them out and figure if we are getting traction or if we need to improve our product. It's this ability of fast development cycles that leads us to find our ideas that work, and hopefully, generate revenues and lead us to live the lifestyle we dream of. What am I going to get from this course? Over 130 lectures and 12 hours of content! Learn how to program in Python and create web applications on Google App Engine Learn about development patterns following Google's best practices Build a Re[beeep]e Search Engine which allows users to register, log in, post re[beeep]es with images, search for re[beeep]es and even build a parser or crawler that will go our and fetch re[beeep]es from other sites HTML development with Twitter's Bootstrap Framework javascript and jQuery development AJAX and asynchronous requests Backend development Using the NoSQL Google DataStore database Creating users and logging them in using cookie sessions Encrypting passwords and cookies Posting content, including images, to our app Indexing re[beeep]es into our Full text Search Index Building a parser (crawler, spider) and fetching content from other sites Debugging Deploying the application Pointing a custom domain to our app Monitoring and performance testing and enhancing using logs and traces Download From Uploaded http://uploaded.net/file/k47vwjnr/xidau123_buildScalappGooglAppEgnin.part1.rar http://uploaded.net/file/7whkkz4x/xidau123_buildScalappGooglAppEgnin.part2.rar http://uploaded.net/file/4spxue38/xidau123_buildScalappGooglAppEgnin.part3.rar Download From NitroFlare http://www.nitroflare.com/view/D38A752D94263F6/xidau123_buildScalappGooglAppEgnin.part1.rar http://www.nitroflare.com/view/B8828485D427429/xidau123_buildScalappGooglAppEgnin.part2.rar http://www.nitroflare.com/view/0B0E5925CF1AC2A/xidau123_buildScalappGooglAppEgnin.part3.rar Download From Rapidgator http://rapidgator.net/file/774955b67a5fa41d58580ab892374ec6/xidau123_buildScalappGooglAppEgnin.part1.rar.html http://rapidgator.net/file/32edd6aeca9c09eb2525da7024f3be2b/xidau123_buildScalappGooglAppEgnin.part2.rar.html http://rapidgator.net/file/a875059d22f8dbd105f0fb48a863e5f7/xidau123_buildScalappGooglAppEgnin.part3.rar.html