431.11 MB | 00:06:19 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
01 - Full-stack deep learning, MLOps, and MLflow.mp4 (9.8 MB)
02 - Prerequisites.mp4 (898.79 KB)
01 - Introducing full-stack deep learning.mp4 (7.78 MB)
02 - Introducing MLOps.mp4 (6.62 MB)
03 - Introducing MLflow.mp4 (6.32 MB)
04 - Setting up the environment on Google Colab.mp4 (13.02 MB)
05 - Running MLflow and using ngrok to access the MLflow UI.mp4 (10.26 MB)
01 - Loading and exploring the EMNIST dataset.mp4 (9.94 MB)
02 - Logging metrics, parameters, and artifacts in MLflow.mp4 (11.05 MB)
03 - Set up the dataset and data loader.mp4 (6.9 MB)
04 - Configuring the image classification DNN model.mp4 (10.48 MB)
05 - Training a model within an MLflow run.mp4 (11.14 MB)
06 - Exploring parameters and metrics in MLflow.mp4 (9.04 MB)
07 - Making predictions using MLflow artifacts.mp4 (11.35 MB)
01 - Preparing data for image classification using CNN.mp4 (9.74 MB)
02 - Configuring and training the model using MLflow runs.mp4 (15.5 MB)
03 - Visualizing charts, metrics, and parameters on MLflow.mp4 (15.23 MB)
04 - Setting up the objective function for hyperparameter tuning.mp4 (12.36 MB)
05 - Hyperparameter optimization with Hyperopt and MLflow.mp4 (13.88 MB)
06 - Identifying the best model.mp4 (7.79 MB)
07 - Registering a model with the MLflow registry.mp4 (5.68 MB)
01 - Setting up MLflow on the local machine.mp4 (8.18 MB)
02 - Workaround to get model artifacts on the local machine.mp4 (4.27 MB)
03 - Deploying and serving the model locally.mp4 (13.76 MB)
01 - Summary and next steps.mp4 (2.47 MB)]
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