Download Free Download : Udemy - Complete MLOps Bootcamp From Zero to Hero in Python 2022
mp4 | Video: h264,1920X1080 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.8 GB
Files Included :
1 - How to get the most out of the course.mp4 (77.17 MB)
MP4
37 - Introduction to DagsHub for the code repository.mp4 (13.13 MB)
MP4
38 - EDA and data preprocessing.mp4 (86.47 MB)
MP4
39 - Training and evaluation of the prototype of the ML model.mp4 (117.89 MB)
MP4
40 - DagsHub account creation.mp4 (26.17 MB)
MP4
41 - Creating the Python environment and dataset.mp4 (39.02 MB)
MP4
42 - Deployment of the model in DagsHub.mp4 (27.69 MB)
MP4
43 - Training and versioning the ML model.mp4 (40.55 MB)
MP4
44 - Improving the model for a production environment.mp4 (34.1 MB)
MP4
45 - Using DVC to version data and models.mp4 (22.91 MB)
MP4
46 - Sending code data and models to DagsHub.mp4 (22.97 MB)
MP4
47 - Experimentation and registration of experiments in DagsHub.mp4 (78.22 MB)
MP4
48 - Using DagsHub to analyze and compare experiments and models.mp4 (53.83 MB)
MP4
49 - Pycaret and Dagshub integration.mp4 (4.47 MB)
MP4
50 - Hands on laboratory of registering a model and dataset with Pycaret and DagsHub.mp4 (44.34 MB)
MP4
51 - Handson ExerciseDevelopment of a model with Pycaret and registration in MLFlow.mp4 (2.33 MB)
MP4
52 - Solution Development of a model with Pycaret and registration in MLFlow.mp4 (70.55 MB)
MP4
53 - Handson exercise Generating a repository with DagsHub.mp4 (1.74 MB)
MP4
54 - Solution Generating a repository with DagsHub.mp4 (18.27 MB)
MP4
55 - Handson exercise Data versioning with DVC.mp4 (2.04 MB)
MP4
56 - Solution Data versioning with DVC.mp4 (44.29 MB)
MP4
57 - Handson exercise Registering the model on a shared MLFlow server.mp4 (1.8 MB)
MP4
58 - Solution Registering the model on a shared MLFlow server.mp4 (48.48 MB)
MP4
59 - Basics of interpretability with SHAP.mp4 (11.63 MB)
MP4
60 - Interpreting Scikit Learn models with SHAP.mp4 (18.56 MB)
MP4
61 - Interpreting models with SHAP in Pycaret.mp4 (22.22 MB)
MP4
62 - Deploying Models in Production.mp4 (10.62 MB)
MP4
63 - Fundamentals of APIs and FastAPI.mp4 (10.33 MB)
MP4
64 - Functions methods and parameters in FastAPI.mp4 (14.02 MB)
MP4
65 - POST Method Swagger and Pydantic in FastAPI.mp4 (13.49 MB)
MP4
66 - API development for Scikitlearn model with FastAPI.mp4 (16.17 MB)
MP4
67 - Automated API development with Pycaret.mp4 (17.46 MB)
MP4
68 - Serve the model through a Web Application.mp4 (2.54 MB)
MP4
69 - Basic Gradio commands.mp4 (11.56 MB)
MP4
70 - Development of a Gradio web application for Machine Learning.mp4 (29.66 MB)
MP4
71 - Automated web application development with Pycaret.mp4 (5.09 MB)
MP4
72 - Flask Fundamentals.mp4 (9.38 MB)
MP4
73 - Building a project from start to finish with Flask.mp4 (12.9 MB)
MP4
74 - Backend development with Flask and frontend development with HTML and CSS.mp4 (15.43 MB)
MP4
75 - Containers to isolate our applications.mp4 (10.7 MB)
MP4
76 - Docker and Kubernetes Basics.mp4 (13.16 MB)
MP4
77 - Generating a container for an ML API with Docker.mp4 (20.27 MB)
MP4
78 - Docker to generate a container of a web application from Flask HTML.mp4 (16.47 MB)
MP4
79 - Introduction to BentoML for generating ML services.mp4 (20.91 MB)
MP4
80 - Generating an ML service with BentoML.mp4 (62.79 MB)
MP4
81 - Putting the service into production with BentoML and Docker.mp4 (25.34 MB)
MP4
82 - BentoML and MLflow integration and custom models.mp4 (14.47 MB)
MP4
83 - GPU preprocessing data validation and multiple models in BentoML.mp4 (62.09 MB)
MP4
84 - Different tools for developing ML services.mp4 (36.04 MB)
MP4
85 - Exercise Using BentoML to develop a ML service.mp4 (1.77 MB)
MP4
86 - Exercise Solution Using BentoML to develop a ML service.mp4 (24 MB)
MP4
87 - Introduction to Machine Learning in Cloud.mp4 (9.89 MB)
MP4
88 - Putting the ML application into production in Azure Container with Docker.mp4 (23.68 MB)
MP4
89 - SDKs and Azure Blob Storage for model deployment to Azure.mp4 (56.2 MB)
MP4
90 - Model training and production deployment in Azure Blob Storage.mp4 (46.37 MB)
MP4
91 - Download the Azure Blob Storage model and get predictions.mp4 (38.16 MB)
MP4
3 - Introduction to Machine Learning.mp4 (4.67 MB)
MP4
4 - Benefits of Machine Learning.mp4 (1.41 MB)
MP4
5 - MLOps Fundamentals.mp4 (4.31 MB)
MP4
6 - DevOps and DataOps Fundamentals.mp4 (5.52 MB)
MP4
92 - Introduction to GitHub Actions.mp4 (12.89 MB)
MP4
93 - GitHub Actions basic workflow.mp4 (9.43 MB)
MP4
94 - GitHub Actions handson lab.mp4 (41.75 MB)
MP4
95 - CI with Continuous Machine Learning CML.mp4 (14.84 MB)
MP4
96 - CML Use Cases.mp4 (46.03 MB)
MP4
97 - HandsOn Lab Applying GitHub Actions and CML to MLOps.mp4 (26.82 MB)
MP4
98 - HandsOn Lab Tracking Performance with GitHub Actions and CML.mp4 (24.32 MB)
MP4
100 - Data Drift Concept Drift and Model Performance.mp4 (21.26 MB)
MP4
101 - ML model and service monitoring tools.mp4 (11.13 MB)
MP4
102 - Evidently AI Fundamentals.mp4 (26.88 MB)
MP4
103 - Drift and data quality target drift and model quality.mp4 (113.54 MB)
MP4
99 - Introduction to monitoring ML models and services.mp4 (5.63 MB)
MP4
104 - MLOps endtoend projectMLOps endtoend project.mp4 (3.15 MB)
MP4
105 - Development of the ML model.mp4 (80.2 MB)
MP4
106 - Validation of the quality of the code model and preprocessing.mp4 (43.81 MB)
MP4
107 - Project versioning with MLFlow and DVC.mp4 (65.95 MB)
MP4
108 - Shared repository with DagsHub and MLFlow.mp4 (51.52 MB)
MP4
109 - API development with BentoML.mp4 (46.67 MB)
MP4
110 - App development with Streamlit.mp4 (31.79 MB)
MP4
111 - CICD Data validation workflow with GitHub Actions.mp4 (30.02 MB)
MP4
112 - CICD Validating app functionality with GitHub Actions.mp4 (15.22 MB)
MP4
113 - CICD Automated app deployment with GitHub Actions and Heroku.mp4 (12.36 MB)
MP4
10 - MLOps stages.mp4 (14.47 MB)
MP4
7 - Problems that MLOps solves.mp4 (2.35 MB)
MP4
8 - MLOps Components.mp4 (13.11 MB)
MP4
9 - MLOps Toolbox.mp4 (24.54 MB)
MP4
11 - How to install libraries and prepare the environment.mp4 (23.85 MB)
MP4
12 - Jupyter Notebook Basics.mp4 (16.72 MB)
MP4
13 - Installing Docker and Ubuntu.mp4 (52.48 MB)
MP4
14 - Cookiecutter for managing the structure of the Machine Learning model.mp4 (17.77 MB)
MP4
15 - Libraries and tools for project management from start to finish.mp4 (2.21 MB)
MP4
16 - Poetry for dependency management.mp4 (13.58 MB)
MP4
17 - Makefile for automated task execution.mp4 (2.54 MB)
MP4
18 - Hydra to manage YAML configuration files.mp4 (16.52 MB)
MP4
19 - Hydra applied to a Machine Learning project.mp4 (16.4 MB)
MP4
20 - Automatically check and fix code before commit in Git.mp4 (4.58 MB)
MP4
21 - Code review with Black and Flake8 in the precommit.mp4 (14.14 MB)
MP4
22 - Code review with Isort and Iterrogate in the Precommit and Git integration.mp4 (26.28 MB)
MP4
23 - Automatically generate documentation for ML project.mp4 (11.53 MB)
MP4
24 - Volere design and implementation.mp4 (16.3 MB)
MP4
25 - AutoML Basics.mp4 (3.75 MB)
MP4
26 - Building a model from start to finish with Pycaret.mp4 (28.74 MB)
MP4
27 - EDA and Advanced Preprocessing with Pycaret.mp4 (27.95 MB)
MP4
28 - Development of advanced models XGBoost CatBoost LightGBM with Pycaret.mp4 (21.13 MB)
MP4
29 - Production deployment with Pycaret.mp4 (28.18 MB)
MP4
30 - Model registry and versioning with MLFlow.mp4 (14.54 MB)
MP4
31 - Registering a ScikitLearn model with MLFlow.mp4 (24.4 MB)
MP4
32 - Registering a Pycaret model with MLFlow.mp4 (24.23 MB)
MP4
33 - Introduction to DVC.mp4 (12.27 MB)
MP4
34 - DVC commands and process.mp4 (9.7 MB)
MP4
35 - Handson lab with DVC.mp4 (38.94 MB)
MP4
36 - DVC Pipelines.mp4 (11.99 MB)
MP4
MP4
37 - Introduction to DagsHub for the code repository.mp4 (13.13 MB)
MP4
38 - EDA and data preprocessing.mp4 (86.47 MB)
MP4
39 - Training and evaluation of the prototype of the ML model.mp4 (117.89 MB)
MP4
40 - DagsHub account creation.mp4 (26.17 MB)
MP4
41 - Creating the Python environment and dataset.mp4 (39.02 MB)
MP4
42 - Deployment of the model in DagsHub.mp4 (27.69 MB)
MP4
43 - Training and versioning the ML model.mp4 (40.55 MB)
MP4
44 - Improving the model for a production environment.mp4 (34.1 MB)
MP4
45 - Using DVC to version data and models.mp4 (22.91 MB)
MP4
46 - Sending code data and models to DagsHub.mp4 (22.97 MB)
MP4
47 - Experimentation and registration of experiments in DagsHub.mp4 (78.22 MB)
MP4
48 - Using DagsHub to analyze and compare experiments and models.mp4 (53.83 MB)
MP4
49 - Pycaret and Dagshub integration.mp4 (4.47 MB)
MP4
50 - Hands on laboratory of registering a model and dataset with Pycaret and DagsHub.mp4 (44.34 MB)
MP4
51 - Handson ExerciseDevelopment of a model with Pycaret and registration in MLFlow.mp4 (2.33 MB)
MP4
52 - Solution Development of a model with Pycaret and registration in MLFlow.mp4 (70.55 MB)
MP4
53 - Handson exercise Generating a repository with DagsHub.mp4 (1.74 MB)
MP4
54 - Solution Generating a repository with DagsHub.mp4 (18.27 MB)
MP4
55 - Handson exercise Data versioning with DVC.mp4 (2.04 MB)
MP4
56 - Solution Data versioning with DVC.mp4 (44.29 MB)
MP4
57 - Handson exercise Registering the model on a shared MLFlow server.mp4 (1.8 MB)
MP4
58 - Solution Registering the model on a shared MLFlow server.mp4 (48.48 MB)
MP4
59 - Basics of interpretability with SHAP.mp4 (11.63 MB)
MP4
60 - Interpreting Scikit Learn models with SHAP.mp4 (18.56 MB)
MP4
61 - Interpreting models with SHAP in Pycaret.mp4 (22.22 MB)
MP4
62 - Deploying Models in Production.mp4 (10.62 MB)
MP4
63 - Fundamentals of APIs and FastAPI.mp4 (10.33 MB)
MP4
64 - Functions methods and parameters in FastAPI.mp4 (14.02 MB)
MP4
65 - POST Method Swagger and Pydantic in FastAPI.mp4 (13.49 MB)
MP4
66 - API development for Scikitlearn model with FastAPI.mp4 (16.17 MB)
MP4
67 - Automated API development with Pycaret.mp4 (17.46 MB)
MP4
68 - Serve the model through a Web Application.mp4 (2.54 MB)
MP4
69 - Basic Gradio commands.mp4 (11.56 MB)
MP4
70 - Development of a Gradio web application for Machine Learning.mp4 (29.66 MB)
MP4
71 - Automated web application development with Pycaret.mp4 (5.09 MB)
MP4
72 - Flask Fundamentals.mp4 (9.38 MB)
MP4
73 - Building a project from start to finish with Flask.mp4 (12.9 MB)
MP4
74 - Backend development with Flask and frontend development with HTML and CSS.mp4 (15.43 MB)
MP4
75 - Containers to isolate our applications.mp4 (10.7 MB)
MP4
76 - Docker and Kubernetes Basics.mp4 (13.16 MB)
MP4
77 - Generating a container for an ML API with Docker.mp4 (20.27 MB)
MP4
78 - Docker to generate a container of a web application from Flask HTML.mp4 (16.47 MB)
MP4
79 - Introduction to BentoML for generating ML services.mp4 (20.91 MB)
MP4
80 - Generating an ML service with BentoML.mp4 (62.79 MB)
MP4
81 - Putting the service into production with BentoML and Docker.mp4 (25.34 MB)
MP4
82 - BentoML and MLflow integration and custom models.mp4 (14.47 MB)
MP4
83 - GPU preprocessing data validation and multiple models in BentoML.mp4 (62.09 MB)
MP4
84 - Different tools for developing ML services.mp4 (36.04 MB)
MP4
85 - Exercise Using BentoML to develop a ML service.mp4 (1.77 MB)
MP4
86 - Exercise Solution Using BentoML to develop a ML service.mp4 (24 MB)
MP4
87 - Introduction to Machine Learning in Cloud.mp4 (9.89 MB)
MP4
88 - Putting the ML application into production in Azure Container with Docker.mp4 (23.68 MB)
MP4
89 - SDKs and Azure Blob Storage for model deployment to Azure.mp4 (56.2 MB)
MP4
90 - Model training and production deployment in Azure Blob Storage.mp4 (46.37 MB)
MP4
91 - Download the Azure Blob Storage model and get predictions.mp4 (38.16 MB)
MP4
3 - Introduction to Machine Learning.mp4 (4.67 MB)
MP4
4 - Benefits of Machine Learning.mp4 (1.41 MB)
MP4
5 - MLOps Fundamentals.mp4 (4.31 MB)
MP4
6 - DevOps and DataOps Fundamentals.mp4 (5.52 MB)
MP4
92 - Introduction to GitHub Actions.mp4 (12.89 MB)
MP4
93 - GitHub Actions basic workflow.mp4 (9.43 MB)
MP4
94 - GitHub Actions handson lab.mp4 (41.75 MB)
MP4
95 - CI with Continuous Machine Learning CML.mp4 (14.84 MB)
MP4
96 - CML Use Cases.mp4 (46.03 MB)
MP4
97 - HandsOn Lab Applying GitHub Actions and CML to MLOps.mp4 (26.82 MB)
MP4
98 - HandsOn Lab Tracking Performance with GitHub Actions and CML.mp4 (24.32 MB)
MP4
100 - Data Drift Concept Drift and Model Performance.mp4 (21.26 MB)
MP4
101 - ML model and service monitoring tools.mp4 (11.13 MB)
MP4
102 - Evidently AI Fundamentals.mp4 (26.88 MB)
MP4
103 - Drift and data quality target drift and model quality.mp4 (113.54 MB)
MP4
99 - Introduction to monitoring ML models and services.mp4 (5.63 MB)
MP4
104 - MLOps endtoend projectMLOps endtoend project.mp4 (3.15 MB)
MP4
105 - Development of the ML model.mp4 (80.2 MB)
MP4
106 - Validation of the quality of the code model and preprocessing.mp4 (43.81 MB)
MP4
107 - Project versioning with MLFlow and DVC.mp4 (65.95 MB)
MP4
108 - Shared repository with DagsHub and MLFlow.mp4 (51.52 MB)
MP4
109 - API development with BentoML.mp4 (46.67 MB)
MP4
110 - App development with Streamlit.mp4 (31.79 MB)
MP4
111 - CICD Data validation workflow with GitHub Actions.mp4 (30.02 MB)
MP4
112 - CICD Validating app functionality with GitHub Actions.mp4 (15.22 MB)
MP4
113 - CICD Automated app deployment with GitHub Actions and Heroku.mp4 (12.36 MB)
MP4
10 - MLOps stages.mp4 (14.47 MB)
MP4
7 - Problems that MLOps solves.mp4 (2.35 MB)
MP4
8 - MLOps Components.mp4 (13.11 MB)
MP4
9 - MLOps Toolbox.mp4 (24.54 MB)
MP4
11 - How to install libraries and prepare the environment.mp4 (23.85 MB)
MP4
12 - Jupyter Notebook Basics.mp4 (16.72 MB)
MP4
13 - Installing Docker and Ubuntu.mp4 (52.48 MB)
MP4
14 - Cookiecutter for managing the structure of the Machine Learning model.mp4 (17.77 MB)
MP4
15 - Libraries and tools for project management from start to finish.mp4 (2.21 MB)
MP4
16 - Poetry for dependency management.mp4 (13.58 MB)
MP4
17 - Makefile for automated task execution.mp4 (2.54 MB)
MP4
18 - Hydra to manage YAML configuration files.mp4 (16.52 MB)
MP4
19 - Hydra applied to a Machine Learning project.mp4 (16.4 MB)
MP4
20 - Automatically check and fix code before commit in Git.mp4 (4.58 MB)
MP4
21 - Code review with Black and Flake8 in the precommit.mp4 (14.14 MB)
MP4
22 - Code review with Isort and Iterrogate in the Precommit and Git integration.mp4 (26.28 MB)
MP4
23 - Automatically generate documentation for ML project.mp4 (11.53 MB)
MP4
24 - Volere design and implementation.mp4 (16.3 MB)
MP4
25 - AutoML Basics.mp4 (3.75 MB)
MP4
26 - Building a model from start to finish with Pycaret.mp4 (28.74 MB)
MP4
27 - EDA and Advanced Preprocessing with Pycaret.mp4 (27.95 MB)
MP4
28 - Development of advanced models XGBoost CatBoost LightGBM with Pycaret.mp4 (21.13 MB)
MP4
29 - Production deployment with Pycaret.mp4 (28.18 MB)
MP4
30 - Model registry and versioning with MLFlow.mp4 (14.54 MB)
MP4
31 - Registering a ScikitLearn model with MLFlow.mp4 (24.4 MB)
MP4
32 - Registering a Pycaret model with MLFlow.mp4 (24.23 MB)
MP4
33 - Introduction to DVC.mp4 (12.27 MB)
MP4
34 - DVC commands and process.mp4 (9.7 MB)
MP4
35 - Handson lab with DVC.mp4 (38.94 MB)
MP4
36 - DVC Pipelines.mp4 (11.99 MB)
MP4
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!