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Files:
  1. Get Bonus Downloads Here.url 183 bytes
  2. 01 - Full-stack deep learning, MLOps, and MLflow.mp4 9.8 MB
  3. 01 - Full-stack deep learning, MLOps, and MLflow.srt 12.5 KB
  4. 02 - Prerequisites.mp4 898.8 KB
  5. 02 - Prerequisites.srt 1.1 KB
  6. 01 - Introducing full-stack deep learning.mp4 7.8 MB
  7. 01 - Introducing full-stack deep learning.srt 11.4 KB
  8. 02 - Introducing MLOps.mp4 6.6 MB
  9. 02 - Introducing MLOps.srt 7.6 KB
  10. 03 - Introducing MLflow.mp4 6.3 MB
  11. 03 - Introducing MLflow.srt 7.9 KB
  12. 04 - Setting up the environment on Google Colab.mp4 13.0 MB
  13. 04 - Setting up the environment on Google Colab.srt 9.2 KB
  14. 05 - Running MLflow and using ngrok to access the MLflow UI.mp4 10.3 MB
  15. 05 - Running MLflow and using ngrok to access the MLflow UI.srt 9.7 KB
  16. 01 - Loading and exploring the EMNIST dataset.mp4 9.9 MB
  17. 01 - Loading and exploring the EMNIST dataset.srt 8.8 KB
  18. 02 - Logging metrics, parameters, and artifacts in MLflow.mp4 11.0 MB
  19. 02 - Logging metrics, parameters, and artifacts in MLflow.srt 11.0 KB
  20. 03 - Set up the dataset and data loader.mp4 6.9 MB
  21. 03 - Set up the dataset and data loader.srt 6.4 KB
  22. 04 - Configuring the image classification DNN model.mp4 10.5 MB
  23. 04 - Configuring the image classification DNN model.srt 8.7 KB
  24. 05 - Training a model within an MLflow run.mp4 11.1 MB
  25. 05 - Training a model within an MLflow run.srt 7.0 KB
  26. 06 - Exploring parameters and metrics in MLflow.mp4 9.0 MB
  27. 06 - Exploring parameters and metrics in MLflow.srt 7.9 KB
  28. 07 - Making predictions using MLflow artifacts.mp4 11.4 MB
  29. 07 - Making predictions using MLflow artifacts.srt 8.8 KB
  30. 01 - Preparing data for image classification using CNN.mp4 9.7 MB
  31. 01 - Preparing data for image classification using CNN.srt 6.9 KB
  32. 02 - Configuring and training the model using MLflow runs.mp4 15.5 MB
  33. 02 - Configuring and training the model using MLflow runs.srt 10.9 KB
  34. 03 - Visualizing charts, metrics, and parameters on MLflow.mp4 15.2 MB
  35. 03 - Visualizing charts, metrics, and parameters on MLflow.srt 12.0 KB
  36. 04 - Setting up the objective function for hyperparameter tuning.mp4 12.4 MB
  37. 04 - Setting up the objective function for hyperparameter tuning.srt 9.8 KB
  38. 05 - Hyperparameter optimization with Hyperopt and MLflow.mp4 13.9 MB
  39. 05 - Hyperparameter optimization with Hyperopt and MLflow.srt 11.7 KB
  40. 06 - Identifying the best model.mp4 7.8 MB
  41. 06 - Identifying the best model.srt 6.0 KB
  42. 07 - Registering a model with the MLflow registry.mp4 5.7 MB
  43. 07 - Registering a model with the MLflow registry.srt 6.0 KB
  44. 01 - Setting up MLflow on the local machine.mp4 8.2 MB
  45. 01 - Setting up MLflow on the local machine.srt 8.4 KB
  46. 02 - Workaround to get model artifacts on the local machine.mp4 4.3 MB
  47. 02 - Workaround to get model artifacts on the local machine.srt 3.9 KB
  48. 03 - Deploying and serving the model locally.mp4 13.8 MB
  49. 03 - Deploying and serving the model locally.srt 10.6 KB
  50. 01 - Summary and next steps.mp4 2.5 MB
  51. 01 - Summary and next steps.srt 3.2 KB
  52. Bonus Resources.txt 386 bytes
  53. emnist-letters-test.csv 27.3 MB
  54. emnist-letters-train.csv 163.7 MB
  55. demo_01_EMNISTClassificationUsingDNN.ipynb 1.7 MB
  56. demo_02_EMNISTClassificationUsingCNN.ipynb 3.1 MB
  57. demo_03_ModelDeployment.ipynb 37.7 KB
  58. demo_01_EMNISTClassificationUsingDNN-checkpoint.ipynb 1.7 MB
  59. demo_03_ModelDeployment-checkpoint.ipynb 46.3 KB

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