Learn MLOps for Machine Learning
Language: English | Size:606.3 MB
Genre:eLearning
Files Included :
001 Learn MLOps for Machine Learning Introduction.mp4 (13.67 MB)
MP4
001 Learning objectives.mp4 (4.9 MB)
MP4
002 1 1 Gather the data.mp4 (15.73 MB)
MP4
003 1 2 Analyze the data.mp4 (15.12 MB)
MP4
004 1 3 Prepare the data.mp4 (13.81 MB)
MP4
005 1 4 Train a model.mp4 (14.83 MB)
MP4
006 1 5 Evaluate the model.mp4 (15.58 MB)
MP4
007 1 6 Validate the model.mp4 (12.77 MB)
MP4
008 1 7 Deploy the model.mp4 (15.02 MB)
MP4
009 1 8 Monitor the model.mp4 (14.46 MB)
MP4
001 Learning objectives.mp4 (4.72 MB)
MP4
002 2 1 Determine what the data sources are.mp4 (10.52 MB)
MP4
003 2 2 Create ETL pipelines to compile the data.mp4 (27.87 MB)
MP4
004 2 3 Understand the data schema with respect to the model.mp4 (12.09 MB)
MP4
005 2 4 Identify data that can be used for the model.mp4 (9.78 MB)
MP4
006 2 5 Perform feature engineering.mp4 (24.15 MB)
MP4
007 2 6 Version the data with DVC.mp4 (32.48 MB)
MP4
008 2 7 Make multiple data sets.mp4 (8.56 MB)
MP4
009 2 8 MLOps best practices for data.mp4 (11.52 MB)
MP4
001 Learning objectives.mp4 (3.5 MB)
MP4
002 3 1 Use common Python libraries.mp4 (25.76 MB)
MP4
003 3 2 Code versioning with Git.mp4 (23.88 MB)
MP4
004 3 3 Perform hyperparameter tuning.mp4 (29.86 MB)
MP4
005 3 4 Track experiments with MLFlow.mp4 (29.26 MB)
MP4
006 3 5 Track experiments with DVC.mp4 (35.63 MB)
MP4
007 3 6 Evaluate the models.mp4 (22.92 MB)
MP4
001 Learning objectives.mp4 (3.75 MB)
MP4
002 4 1 Decide the best deployment method.mp4 (23.77 MB)
MP4
003 4 2 Test on pre-production environments.mp4 (26.48 MB)
MP4
004 4 3 Deploy to production.mp4 (13.04 MB)
MP4
005 4 4 Monitor the model for drift.mp4 (28.97 MB)
MP4
006 4 5 Validate the pipeline flow.mp4 (24.71 MB)
MP4
007 4 6 Automation points in MLOps.mp4 (18.86 MB)
MP4
008 4 7 Set up redeploy pipeline.mp4 (7.46 MB)
MP4
001 Learn MLOps for Machine Learning Summary.mp4 (10.87 MB)
MP4
MP4
001 Learning objectives.mp4 (4.9 MB)
MP4
002 1 1 Gather the data.mp4 (15.73 MB)
MP4
003 1 2 Analyze the data.mp4 (15.12 MB)
MP4
004 1 3 Prepare the data.mp4 (13.81 MB)
MP4
005 1 4 Train a model.mp4 (14.83 MB)
MP4
006 1 5 Evaluate the model.mp4 (15.58 MB)
MP4
007 1 6 Validate the model.mp4 (12.77 MB)
MP4
008 1 7 Deploy the model.mp4 (15.02 MB)
MP4
009 1 8 Monitor the model.mp4 (14.46 MB)
MP4
001 Learning objectives.mp4 (4.72 MB)
MP4
002 2 1 Determine what the data sources are.mp4 (10.52 MB)
MP4
003 2 2 Create ETL pipelines to compile the data.mp4 (27.87 MB)
MP4
004 2 3 Understand the data schema with respect to the model.mp4 (12.09 MB)
MP4
005 2 4 Identify data that can be used for the model.mp4 (9.78 MB)
MP4
006 2 5 Perform feature engineering.mp4 (24.15 MB)
MP4
007 2 6 Version the data with DVC.mp4 (32.48 MB)
MP4
008 2 7 Make multiple data sets.mp4 (8.56 MB)
MP4
009 2 8 MLOps best practices for data.mp4 (11.52 MB)
MP4
001 Learning objectives.mp4 (3.5 MB)
MP4
002 3 1 Use common Python libraries.mp4 (25.76 MB)
MP4
003 3 2 Code versioning with Git.mp4 (23.88 MB)
MP4
004 3 3 Perform hyperparameter tuning.mp4 (29.86 MB)
MP4
005 3 4 Track experiments with MLFlow.mp4 (29.26 MB)
MP4
006 3 5 Track experiments with DVC.mp4 (35.63 MB)
MP4
007 3 6 Evaluate the models.mp4 (22.92 MB)
MP4
001 Learning objectives.mp4 (3.75 MB)
MP4
002 4 1 Decide the best deployment method.mp4 (23.77 MB)
MP4
003 4 2 Test on pre-production environments.mp4 (26.48 MB)
MP4
004 4 3 Deploy to production.mp4 (13.04 MB)
MP4
005 4 4 Monitor the model for drift.mp4 (28.97 MB)
MP4
006 4 5 Validate the pipeline flow.mp4 (24.71 MB)
MP4
007 4 6 Automation points in MLOps.mp4 (18.86 MB)
MP4
008 4 7 Set up redeploy pipeline.mp4 (7.46 MB)
MP4
001 Learn MLOps for Machine Learning Summary.mp4 (10.87 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!
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!