Udemy - Complete MLOps Bootcamp From Zero to Hero in Python 2022

Files Included :

1 - How to get the most out of the course.mp4 (77.17 MB)
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37 - Introduction to DagsHub for the code repository.mp4 (13.13 MB)
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38 - EDA and data preprocessing.mp4 (86.47 MB)
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39 - Training and evaluation of the prototype of the ML model.mp4 (117.89 MB)
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40 - DagsHub account creation.mp4 (26.17 MB)
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41 - Creating the Python environment and dataset.mp4 (39.02 MB)
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42 - Deployment of the model in DagsHub.mp4 (27.69 MB)
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43 - Training and versioning the ML model.mp4 (40.55 MB)
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44 - Improving the model for a production environment.mp4 (34.1 MB)
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45 - Using DVC to version data and models.mp4 (22.91 MB)
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46 - Sending code data and models to DagsHub.mp4 (22.97 MB)
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47 - Experimentation and registration of experiments in DagsHub.mp4 (78.22 MB)
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48 - Using DagsHub to analyze and compare experiments and models.mp4 (53.83 MB)
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49 - Pycaret and Dagshub integration.mp4 (4.47 MB)
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50 - Hands on laboratory of registering a model and dataset with Pycaret and DagsHub.mp4 (44.34 MB)
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51 - Handson ExerciseDevelopment of a model with Pycaret and registration in MLFlow.mp4 (2.33 MB)
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52 - Solution Development of a model with Pycaret and registration in MLFlow.mp4 (70.55 MB)
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53 - Handson exercise Generating a repository with DagsHub.mp4 (1.74 MB)
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54 - Solution Generating a repository with DagsHub.mp4 (18.27 MB)
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55 - Handson exercise Data versioning with DVC.mp4 (2.04 MB)
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56 - Solution Data versioning with DVC.mp4 (44.29 MB)
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57 - Handson exercise Registering the model on a shared MLFlow server.mp4 (1.8 MB)
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58 - Solution Registering the model on a shared MLFlow server.mp4 (48.48 MB)
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59 - Basics of interpretability with SHAP.mp4 (11.63 MB)
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60 - Interpreting Scikit Learn models with SHAP.mp4 (18.56 MB)
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61 - Interpreting models with SHAP in Pycaret.mp4 (22.22 MB)
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62 - Deploying Models in Production.mp4 (10.62 MB)
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63 - Fundamentals of APIs and FastAPI.mp4 (10.33 MB)
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64 - Functions methods and parameters in FastAPI.mp4 (14.02 MB)
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65 - POST Method Swagger and Pydantic in FastAPI.mp4 (13.49 MB)
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66 - API development for Scikitlearn model with FastAPI.mp4 (16.17 MB)
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67 - Automated API development with Pycaret.mp4 (17.46 MB)
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68 - Serve the model through a Web Application.mp4 (2.54 MB)
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69 - Basic Gradio commands.mp4 (11.56 MB)
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70 - Development of a Gradio web application for Machine Learning.mp4 (29.66 MB)
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71 - Automated web application development with Pycaret.mp4 (5.09 MB)
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72 - Flask Fundamentals.mp4 (9.38 MB)
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73 - Building a project from start to finish with Flask.mp4 (12.9 MB)
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74 - Backend development with Flask and frontend development with HTML and CSS.mp4 (15.43 MB)
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75 - Containers to isolate our applications.mp4 (10.7 MB)
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76 - Docker and Kubernetes Basics.mp4 (13.16 MB)
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77 - Generating a container for an ML API with Docker.mp4 (20.27 MB)
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78 - Docker to generate a container of a web application from Flask HTML.mp4 (16.47 MB)
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79 - Introduction to BentoML for generating ML services.mp4 (20.91 MB)
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80 - Generating an ML service with BentoML.mp4 (62.79 MB)
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81 - Putting the service into production with BentoML and Docker.mp4 (25.34 MB)
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82 - BentoML and MLflow integration and custom models.mp4 (14.47 MB)
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83 - GPU preprocessing data validation and multiple models in BentoML.mp4 (62.09 MB)
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84 - Different tools for developing ML services.mp4 (36.04 MB)
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85 - Exercise Using BentoML to develop a ML service.mp4 (1.77 MB)
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86 - Exercise Solution Using BentoML to develop a ML service.mp4 (24 MB)
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87 - Introduction to Machine Learning in Cloud.mp4 (9.89 MB)
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88 - Putting the ML application into production in Azure Container with Docker.mp4 (23.68 MB)
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89 - SDKs and Azure Blob Storage for model deployment to Azure.mp4 (56.2 MB)
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90 - Model training and production deployment in Azure Blob Storage.mp4 (46.37 MB)
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91 - Download the Azure Blob Storage model and get predictions.mp4 (38.16 MB)
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3 - Introduction to Machine Learning.mp4 (4.67 MB)
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4 - Benefits of Machine Learning.mp4 (1.41 MB)
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5 - MLOps Fundamentals.mp4 (4.31 MB)
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6 - DevOps and DataOps Fundamentals.mp4 (5.52 MB)
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92 - Introduction to GitHub Actions.mp4 (12.89 MB)
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93 - GitHub Actions basic workflow.mp4 (9.43 MB)
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94 - GitHub Actions handson lab.mp4 (41.75 MB)
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95 - CI with Continuous Machine Learning CML.mp4 (14.84 MB)
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96 - CML Use Cases.mp4 (46.03 MB)
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97 - HandsOn Lab Applying GitHub Actions and CML to MLOps.mp4 (26.82 MB)
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98 - HandsOn Lab Tracking Performance with GitHub Actions and CML.mp4 (24.32 MB)
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100 - Data Drift Concept Drift and Model Performance.mp4 (21.26 MB)
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101 - ML model and service monitoring tools.mp4 (11.13 MB)
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102 - Evidently AI Fundamentals.mp4 (26.88 MB)
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103 - Drift and data quality target drift and model quality.mp4 (113.54 MB)
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99 - Introduction to monitoring ML models and services.mp4 (5.63 MB)
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104 - MLOps endtoend projectMLOps endtoend project.mp4 (3.15 MB)
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105 - Development of the ML model.mp4 (80.2 MB)
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106 - Validation of the quality of the code model and preprocessing.mp4 (43.81 MB)
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107 - Project versioning with MLFlow and DVC.mp4 (65.95 MB)
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108 - Shared repository with DagsHub and MLFlow.mp4 (51.52 MB)
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109 - API development with BentoML.mp4 (46.67 MB)
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110 - App development with Streamlit.mp4 (31.79 MB)
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111 - CICD Data validation workflow with GitHub Actions.mp4 (30.02 MB)
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112 - CICD Validating app functionality with GitHub Actions.mp4 (15.22 MB)
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113 - CICD Automated app deployment with GitHub Actions and Heroku.mp4 (12.36 MB)
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10 - MLOps stages.mp4 (14.47 MB)
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7 - Problems that MLOps solves.mp4 (2.35 MB)
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8 - MLOps Components.mp4 (13.11 MB)
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9 - MLOps Toolbox.mp4 (24.54 MB)
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11 - How to install libraries and prepare the environment.mp4 (23.85 MB)
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12 - Jupyter Notebook Basics.mp4 (16.72 MB)
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13 - Installing Docker and Ubuntu.mp4 (52.48 MB)
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14 - Cookiecutter for managing the structure of the Machine Learning model.mp4 (17.77 MB)
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15 - Libraries and tools for project management from start to finish.mp4 (2.21 MB)
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16 - Poetry for dependency management.mp4 (13.58 MB)
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17 - Makefile for automated task execution.mp4 (2.54 MB)
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18 - Hydra to manage YAML configuration files.mp4 (16.52 MB)
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19 - Hydra applied to a Machine Learning project.mp4 (16.4 MB)
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20 - Automatically check and fix code before commit in Git.mp4 (4.58 MB)
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21 - Code review with Black and Flake8 in the precommit.mp4 (14.14 MB)
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22 - Code review with Isort and Iterrogate in the Precommit and Git integration.mp4 (26.28 MB)
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23 - Automatically generate documentation for ML project.mp4 (11.53 MB)
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24 - Volere design and implementation.mp4 (16.3 MB)
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25 - AutoML Basics.mp4 (3.75 MB)
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26 - Building a model from start to finish with Pycaret.mp4 (28.74 MB)
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27 - EDA and Advanced Preprocessing with Pycaret.mp4 (27.95 MB)
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28 - Development of advanced models XGBoost CatBoost LightGBM with Pycaret.mp4 (21.13 MB)
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29 - Production deployment with Pycaret.mp4 (28.18 MB)
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30 - Model registry and versioning with MLFlow.mp4 (14.54 MB)
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31 - Registering a ScikitLearn model with MLFlow.mp4 (24.4 MB)
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32 - Registering a Pycaret model with MLFlow.mp4 (24.23 MB)
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33 - Introduction to DVC.mp4 (12.27 MB)
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34 - DVC commands and process.mp4 (9.7 MB)
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35 - Handson lab with DVC.mp4 (38.94 MB)
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36 - DVC Pipelines.mp4 (11.99 MB)
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