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Free Download Building Data Pipelines with Luigi 3 and Python Duration: 1h 34m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 32kHz, 2ch | Size: 208 MB Genre: eLearning | Language: English Other developers implement data pipelines by putting together a bunch of hacky scripts, that over time turn into liabilities and maintenance nightmares. Take this course to implement sane and smart data pipelines with Luigi in Python. Data arrives from various sources and needs further processing. It's very tempting to re-invent the wheel and write your own library to build data pipelines for batch processing. This results in data pipelines that are difficult to maintain. In this course, Building Data Pipelines with Luigi and Python, you'll learn how to build data pipelines with Luigi and Python. First, you'll explore how to build your first data pipelines with Luigi. Next, you'll discover how to configure Luigi pipelines. Finally, you'll learn how to run Luigi pipelines. When you're finished with this course, you'll have the Luigi skills and knowledge for building data pipelines that are easy to maintain. Homepage https://www.pluralsight.com/courses/building-data-pipelines-luigi-python Rapidgator https://rg.to/file/d47379d50828a9680196cd60ccf6cec3/dzomp.Building.Data.Pipelines.with.Luigi.3.and.Python.rar.html Fikper Free Download https://fikper.com/piRqBdl2ua/dzomp.Building.Data.Pipelines.with.Luigi.3.and.Python.rar.html No Password - Links are Interchangeable

Free Download Automating Ml Pipelines For Song Recommendation System Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.74 GB | Duration: 4h 46m Automate Song Recommendations with Docker, MLFlow, and CI/CD Practices for Machine Learning Algorithms. What you'll learn Understand the Math Behind ML Algorithms: You will learn the mathematical concepts that underlie popular machine learning algorithms. Implement Machine Learning Algorithms: You will gain hands-on experience in coding and applying various machine learning algorithms. Design and Build MLFlow Tracking: You will learn how to use MLFlow for tracking and managing machine learning experiments effectively. Implement Microservices with Docker: You will learn how to create and manage microservices for automating machine learning pipelines using Docker. Automate Model Training and Evaluation: You will learn to use Airflow triggers to automate the process of training and evaluating machine learning models. Set Up Git CI/CD for a Song Recommender App: You will learn how to implement CI/CD for a song recommendation web app. Requirements Basic Knowledge of Python programming, as it will be used for implementing machine learning algorithms and building ML pipeline microservices. A desire to learn and experiment with machine learning and microservices is encouraged. Description Math Behind Machine Learning Algorithms:K-Nearest Neighbors (KNN): A method for finding similar songs based on user preferences.Random Forest (RF): An algorithm that combines many decision trees for better predictions.Prin[beeep]l Component Analysis (PCA): A technique for reducing the number of features while retaining important information.K-Means Clustering: A way to group similar songs together based on features.Collaborative Filtering: Making recommendations based on user interactions and preferences.Data Processing Techniques:Feature Engineering (Feature Importance using Random Forest): Feature importance analysis and creating new features from existing data to improve model accuracy.Data Pre-processing (Missing Data Imputation): Cleaning and preparing data for analysis.Evaluation and Tuning:Hyperparameter Tuning (Collaborative Filtering, KNN, Naive Bayes Classifier): Adjusting the settings of algorithms to improve performance.Evaluation Metrics (Precision, Recall, ROC, Accuracy, MSE): Methods to measure how well the model performs.Data Science Fundamentals:TF-IDF (Term Frequency and Inverse Document Frequency): A technique for analyzing the importance of words in song lyrics.Correlation Analysis: Understanding how different features relate to each other.T-Test: A statistical method for comparing groups of data.Automation Tools:Building Microservices using Docker: Use containers to run applications consistently across different environments.Airflow: Automate workflows and schedule tasks for running ML models.MLFlow: Manage and track machine learning experiments and models effectively.By the end of the course, you will know how to build and automate the training, evaluation, and deployment of an ML model for a song recommendation system using these tools, libraries and techniques. Overview Section 1: Introduction Lecture 1 Course Introduction Section 2: Machine Learning - Math Intuition Lecture 2 Math Behind Collaborative Filtering Lecture 3 Math Behind KNN (Euclidean Distance) Lecture 4 Math Behind Naive Bayes (Bayes Theorem) Lecture 5 Math Behind TF and IDF Lecture 6 Math Behind Cosine Similarity Lecture 7 Evaluation Metric - MSE Lecture 8 Math Behind - K-Means Clustering (Unsupervised Learning) Lecture 9 Math Behind Prin[beeep]l Component Analysis Lecture 10 Math Behind Pearson Correlation Lecture 11 Math Behind - T-Statistic Test Lecture 12 Evaluation Metrics - Classification Models Section 3: ML Experimentation - Supervised & Unsupervised Learning Lecture 13 Module Artifacts Lecture 14 Project Env Setup (Conda) Lecture 15 Import required libraries Lecture 16 Understanding the features in data Lecture 17 Data Preprocessing Lecture 18 Feature Engineering Lecture 19 Pearson Correlation Analysis Lecture 20 T-Test Statistics Lecture 21 Collaborative Filtering - User Genre Matrix Lecture 22 Creation of user similarity network visualization (Cosine Similarity) Lecture 23 Songs Recommender Engine Model - Collaborative Filtering Lecture 24 Fetch Songs Recommendation - Collaborative Filtering Model Lecture 25 KNN and Naive Bayes Model Pipeline Lecture 26 Model Hyperparameter Tuning Lecture 27 Best Estimator Recommendation Lecture 28 K-Means Clustering and PCA Section 4: Airflow - Automate Collaborative Filtering model training and deployment Lecture 29 Module Artifacts Lecture 30 Code Environment Setup Lecture 31 MLFlow Lifecycle and Commands Lecture 32 Airflow Lifecycle and Commands Lecture 33 DAG Setup - Data Splitting, User Genre Matrix Generation, Training & Evaluation Lecture 34 train_and_deploy.py W/O Airflow Lecture 35 Optional - DAG Assets Validation Section 5: Building Microservices for MLFlow and Airflow using Docker Lecture 36 docker-compose.yml Lifecycle (Theory) Lecture 37 Dockerfile (Python and Airflow) Lecture 38 Microservices - docker-compose.yml Lecture 39 Building Docker Image for Python Lecture 40 Building Docker Image for Airflow Section 6: ML Pipeline Orchestration - Airflow Triggers and MLFlow Experiments Lecture 41 Build and Compose up the Microservices Lecture 42 Orchestrating the ML Job Triggers and Logs Section 7: Song Recommender System Web App Lecture 43 Import required modules Lecture 44 Load Pkl Model Lecture 45 Fallback condition for recommender system Lecture 46 Load and Fetch cache Data Lecture 47 Building UI for song recommender system Lecture 48 Filter and Join recommendations Lecture 49 Testing the recommender app in localhost environment Lecture 50 Push the codebase to Github repository Lecture 51 Deploy recommender app to Streamlit cloud with Github CI/CD Section 8: Challenges / Takeaways / Homework Lecture 52 Automating ML Pipeline Song Recommendation: Challenges / Takeaways / Homework Lecture 53 Thank you! Lecture 54 Codebase Artifacts Students pursuing studies in data science, computer science, or related disciplines who want to enhance their practical skills in machine learning and automation.,Individuals looking to deepen their understanding of machine learning and its applications in real-world scenarios, particularly in recommendation systems.,Programmers interested in expanding their skill set to include machine learning concepts and automation practices using tools like Docker, MLFlow, and Airflow.,Professionals wanting to learn how to build and automate machine learning pipelines and improve their workflow efficiency.,Anyone with a foundational knowledge of machine learning who wants to gain practical experience in implementing algorithms and automating processes.,Individuals looking to enhance their qualifications and job prospects by adding machine learning and automation expertise to their portfolio. Screenshot Homepage https://www.udemy.com/course/automating-ml-pipelines-for-song-recommendation-system/ Rapidgator https://rg.to/file/38d2c8769d9397e540df34784a1f7fce/ldngk.Automating.Ml.Pipelines.For.Song.Recommendation.System.part2.rar.html https://rg.to/file/9ccb4c26165e85f3d12b36acfe69ec65/ldngk.Automating.Ml.Pipelines.For.Song.Recommendation.System.part1.rar.html Fikper Free Download https://fikper.com/5GleFBaWuK/ldngk.Automating.Ml.Pipelines.For.Song.Recommendation.System.part1.rar.html https://fikper.com/9Zz1rdgVY1/ldngk.Automating.Ml.Pipelines.For.Song.Recommendation.System.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Getting Started With Jenkins Pipelines Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.01 GB | Duration: 3h 12m Learning on how to use jenkins pipelines and create shared libraries What you'll learn Understand pipeline syntax Have a grasp on basic elements Create new pipelines from scratch Avoid common pitfalls Requirements Docker compose installed Description JenkinsJenkins is a commonly used building server used to automate different types of pipelines. A pipeline is this context is a step by step process which runs different types of automations from start to finish. Within the IT space, this also referred too as a build or deploy pipeline. Within the course we will focus on these automations and how they can help you.CourseTo get the most out of Jenkins, you will need to know how to use it correctly. We will go over every stage step by step and start with the basic all the way up to re-using components that we made into the same pipelines we started with. Over the span of this course you will learn the Jenkins syntax but also know how to use and maintain a Jenkins server within your organisation. As you will grow your skill not just in Jenkins itself, but also in how to make pipelines more effective using the power of groovy coding.The combination of these techniques will help you grow your Jenkins skill, not just in making pipelines but also in start or continue your programming knowledge. After you completed this course, you will be ready to start your own projects and have a good base from where to start. Overview Section 1: Introduction Lecture 1 Introduction Section 2: Type of pipeline Lecture 2 Declarative vs Scripted Section 3: Setting up an environment Lecture 3 Overview of the setup Lecture 4 Install Gitea Lecture 5 Install Jenkins Lecture 6 Store Gitea token into jenkins Lecture 7 Add Gitea server to Jenkins Lecture 8 Set Jenkins webhook for Gitea Section 4: Setting up the pipeline Lecture 9 Create repo for pipeline Lecture 10 Create folder for pipeline Section 5: Basic concepts Lecture 11 Blue Ocean Lecture 12 Replay Section 6: Options en Environment Lecture 13 Options Lecture 14 Environment variables and secrets Lecture 15 Parameters Lecture 16 Triggers Lecture 17 Tools Section 7: Agents Lecture 18 Agent labels Lecture 19 Running dockers as agents Section 8: Basic steps Lecture 20 echo Lecture 21 sh Lecture 22 withEnv Lecture 23 sleep Lecture 24 timeout with input Lecture 25 parallel stages Section 9: Conditions and Post steps Lecture 26 Basic conditions Lecture 27 Complex conditions Lecture 28 Setting up different post actions Section 10: Stashes and code blocks Lecture 29 Using script blocks Lecture 30 Stashes Section 11: Workspace Lecture 31 Workspace in the UI Lecture 32 fileExists Lecture 33 dir Lecture 34 readFile Lecture 35 writeFile Section 12: Pipeline Utility steps Lecture 36 Plugins Lecture 37 readJson Lecture 38 readYaml Lecture 39 writeJson Lecture 40 writeYaml Section 13: Shared Libraries Lecture 41 Setting up a coding environment Lecture 42 Echo hello world Lecture 43 Load the shared library Lecture 44 Working in source Section 14: Improve Shared Library Lecture 45 Parent class Lecture 46 Create stage from code Lecture 47 Working with maps Lecture 48 Working with docker Lecture 49 Load file from library into the workspace Lecture 50 Adding gradle to our shared library Lecture 51 Unit test Lecture 52 Making a pipeline for the shared library Section 15: Conclusion Lecture 53 Thank you Devops beginner with Jenkins,Software engineer looking into building pipelines Homepage https://www.udemy.com/course/getting-started-with-jenkins-pipelines/ Rapidgator https://rg.to/file/10110a23c7fd37ef13eb8f996f359d32/qokkg.Getting.Started.With.Jenkins.Pipelines.part1.rar.html https://rg.to/file/0697b840b7a6b3ad8b153dc74b677464/qokkg.Getting.Started.With.Jenkins.Pipelines.part2.rar.html Fikper Free Download https://fikper.com/DhRcQgFN5O/qokkg.Getting.Started.With.Jenkins.Pipelines.part1.rar https://fikper.com/850R4sLSGi/qokkg.Getting.Started.With.Jenkins.Pipelines.part2.rar No Password - Links are Interchangeable

Free Download Data Pipelines with Snowflake and Streamlit Published 9/2024 Created by Marcos Vinicius Oliveira MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 40 Lectures ( 5h 17m ) | Size: 2.1 GB Using Snowflake to data engineer Kaggle and Google Trends data with Python procedures and tasks What you'll learn: Setup Snowflake and AWS Accounts Work with Kaggle and SerpAPI Download and manipulate data with Jupyter Notebooks on VS Code Work with External Access Integration and Storage Integration on Snowflake Create Snowflake Python based procedures Create Snowflake tasks Create Streamlit apps inside of Snowflake Requirements: Proficient knowledge on SQL and basic knowledge on Snowflake database Basic knowledge on data modeling and engineering Proficient Python knowledge Description: This course focuses on building a data engineering pipeline that integrates multiple data sources, including Kaggle datasets and Google Trends data (fetched via SerpAPI), to analyze the relationship between Netflix show releases and the popularity of actors. You'll learn to gather and combine data on Netflix actors and their trends on Google, particularly in the weeks following a show's release.You will use Kaggle as a source for the Netflix shows and actors dataset and Google Trends (accessed via SerpAPI) to fetch real-time search data for the actors. This data will be stored and processed within the Snowflake database, leveraging its cloud-native architecture for optimal scalability and performance.Technical Stack Overview:Snowflake Database: The central repository for storing and querying data.Streamlit in Snowflake: A web app framework to visualize the data directly inside Snowflake.AWS S3: For data storage and retrieval, particularly for intermediate datasets.Snowflake Python Procedures: Automating data manipulation and pipeline processes.Snowflake External Access & Storage Integrations: Managing secure access to external services and storage.By the end of the course, you'll have a fully functional data pipeline that processes and combines streaming data, cloud storage, and APIs for trend analysis, visualized through an interactive Streamlit app within Snowflake. Who this course is for: Data Engineers looking to get proficient on Snowflake and Streamlit for building data pipelines Homepage https://www.udemy.com/course/data-pipelines-with-snowflake-and-streamlit/ Rapidgator https://rg.to/file/f0629de1f792eeeebd379ae716fd2bad/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part1.rar.html https://rg.to/file/7f9c73ecc73cc0f8d2c385710cbea0fa/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part2.rar.html https://rg.to/file/6731a5ce1a015365253b5a46c6bf42a0/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part3.rar.html Fikper Free Download https://fikper.com/hKSvOh0u9B/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part1.rar.html https://fikper.com/WRYC5cNE7A/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part2.rar.html https://fikper.com/GRagTmZo87/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part3.rar.html No Password - Links are Interchangeable

Free Download Mastering GitLab Pipelines: The Ultimate CI/CD Guide Published 9/2024 Created by Sascha Delp,Alexander Panov MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 28 Lectures ( 5h 5m ) | Size: 3 GB Learn GitLab CI/CD pipelines, secure secrets, manage environments, and deploy apps with real-world examples. What you'll learn: You will learn how to set up your very first GitLab pipeline from scratch. Running tests and attaching test results to the pipeline Creating dynamic environments Using templates to create a cicd pipeline for a whitelabeled application Deploying your application via ssh to a compute engine You will discover how to protect sensitive secrets and configure protected variables. The difference between cache and artifacts. And when to use which Use yaml anchors Registering a shell runner Deploying the app when a commit message matches a string Become proficient in managing caching and publishing build results as artifacts. You will understand how to create and run pipeline jobs based on specific conditions. Learn to securely pass secrets into your CI/CD pipelines using GitLab variables. Requirements: Its good to have a basic understanding of how apps work. (Compiling eg) Description: Welcome to the Ultimate GitLab Pipeline Course!Whether you're new to CI/CD or have some experience, this course is designed to guide you step by step through mastering GitLab pipelines. Alexander and I will lead you through the intricacies of creating, managing, and optimizing pipelines tailored to your development needs.Are you ready to dive deep into GitLab pipelines and learn how to automate and optimize your CI/CD workflows? Do you want to uncover the secrets behind effective pipeline management and build processes within GitLab? Are you interested in learning how to securely manage secrets, deploy applications, and streamline your CI/CD pipeline using templates? Or perhaps you're aiming to become proficient in setting up modular and dynamic pipelines that support scalable deployments.If any of these resonate with you, this course is perfect for you. ____________________________________________________________________________________In this comprehensive GitLab pipeline course, you'll gain all the skills necessary to elevate your pipeline expertise. From creating basic pipelines to mastering complex scenarios involving dynamic rules and environments, we will cover everything in detail.1. IntroductionWe will begin by welcoming you to the course and providing a brief overview of what to expect. You'll also receive valuable tips on how to approach the lessons to make the most of your learning experience.2. Pipeline Skills (Language Agnostic) In this section, we will introduce the core pipeline concepts that apply to any programming language. You will learn how to set up your first pipeline job and make it report test results. We will also cover managing caching and publishing build artifacts. Additionally, you will discover how to define rules to ensure jobs only run under specific conditions. The section also includes handling CI/CD variables to securely pass secrets through the pipeline, protecting sensitive information, and registering a GitLab runner on your local machine. You will further explore the creation of modular pipelines, using templates to promote deployment steps, and implementing inversion of control to standardize templates across applications. We will guide you through setting up and managing environments via pipelines, demonstrate the use of GitLab pipelines to publish a book, and explain how to match commit messages with specific patterns. Finally, you will learn how to group environments to enhance deployment management.3. Hands-on: Building a White-labeled App CI/CD PipelineThis section takes a practical approach, walking you through the creation of a CI/CD pipeline for a white-labeled application. We will begin by presenting the project and introducing the initial steps of the pipeline. You will then deploy the application using SFTP and SSH, explained over three detailed steps. Next, we will simplify the white-labeling process by leveraging templates, which will be demonstrated in two parts. You will also learn how to dynamically register and tear down environments as needed. Furthermore, we will explore the use of dynamic pattern matching rules and demonstrate their application in two stages. Lastly, we will show you how to present the test results in GitLab's web interface using artifacts.4. End of the CourseAs the course concludes, we will provide a final message to acknowledge your completion of the course and leave you with one last piece of valuable information. By this point, you will be fully equipped to apply everything you have learned about GitLab pipelines and confidently implement these skills in your own projects. ____________________________________________________________________________________By the end of this course, you will have mastered GitLab pipelines, from basic setup to advanced configurations, and will be able to manage complex deployment workflows with confidence.We are excited to have you on this journey with us and look forward to seeing what you will achieve with your newfound skills.Thank you for your trust & best regards, Alexander & Sascha Who this course is for: Software Developers that want to increase a projects efficiency DevOps engineers that want to get advanced knowledge about pipelines Homepage https://anonymz.com/https://www.udemy.com/course/mastering-gitlab-pipelines-the-ultimate-cicd-guide/ Rapidgator https://rg.to/file/05acf56d38f286e0baeaf0e3d3aa257f/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part1.rar.html https://rg.to/file/4d48f753fa8906641b795f01f1b00a4c/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part2.rar.html https://rg.to/file/f114fd762c7a23c674c797aa502b09d5/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part3.rar.html https://rg.to/file/f8b8a742f12a70d9cca842f70e66aeff/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part4.rar.html Fikper Free Download https://fikper.com/OCJsi4mfcS/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part1.rar.html https://fikper.com/U3VK8iSlxv/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part2.rar.html https://fikper.com/d0V6tApQdW/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part3.rar.html https://fikper.com/4RY6a4JbF1/vdkwm.Mastering.GitLab.Pipelines.The.Ultimate.CICD.Guide.part4.rar.html No Password - Links are Interchangeable

Free Download Integrating Azure Functions with CI/CD Pipelines Published 8/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz Language: English | Size: 65.01 MB | Duration: 27m 36s Like all software, Azure Function Apps need to be built, tested, and deployed into an environment. This course will teach you how to build, test, and deploy Azure Functions using a CI/CD pipeline, like Azure DevOps. Azure Functions are great for writing quick pieces of code to accomplish specific tasks. But just as any application, you should be able to build, test, and deploy them using pipelines. In this course, Integrating Azure Functions with CI/CD Pipelines, you'll learn to deploy Azure Function Apps using Azure DevOps. First, you'll explore how to build, test, and package a Function App. Next, you'll discover how to deploy the app to Azure. Finally, you'll learn how to create the required infrastructure as part of your deployment pipeline. When you're finished with this course, you'll have the skills and knowledge needed to deploy an Azure Function App using Azure DevOps.. Homepage https://www.pluralsight.com/courses/azure-functions-integrating-cicd-pipelines TakeFile https://takefile.link/jjby02vpk58b/dfdod.Integrating.Azure.Functions.with.CICD.Pipelines.rar.html Rapidgator https://rg.to/file/890d72b4bf5e0a652f0b0c37e164d792/dfdod.Integrating.Azure.Functions.with.CICD.Pipelines.rar.html Fikper Free Download https://fikper.com/8sbAdovrJs/dfdod.Integrating.Azure.Functions.with.CICD.Pipelines.rar.html No Password - Links are Interchangeable