ML Pipelines on Google Cloud By google-cloud

382.96 MB | 00:09:01 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Course Introduction (5.25 MB)
1 TensorFlow Extended (TFX) (15.58 MB)
2 TFX concepts (14.85 MB)
3 TFX standard data components (18.84 MB)
4 TFX standard model components (18.74 MB)
5 TFX pipeline nodes (3.74 MB)
6 TFX libraries (11.18 MB)
1 TFX Orchestrators (12.78 MB)
2 Apache Beam (14.21 MB)
3 TFX on Cloud AI Platform (9.65 MB)
1 TFX custom components - Python functions (7.45 MB)
2 TFX custom components - containers + subclassed (12.47 MB)
3 CICD for TFX pipeline workflows (20.93 MB)
1 TFX Pipeline Metadata (10.19 MB)
2 TFX ML Metadata data model (7.7 MB)
1 Containerized Training Applications (7.64 MB)
2 Containerizing PyTorch, Scikit, and XGBoost Applicatio (4.63 MB)
3 KubeFlow & AI Platform Pipelines (9.88 MB)
4 Continuous Training (14 MB)
1 What is Cloud Composer (11.71 MB)
2 Core Concepts of Apache Airflow (25.93 MB)
3 Continuous Training Pipelines using Cloud Composer (data) (10.14 MB)
4 Continuous Training Pipelines using Cloud Composer (model) (7.41 MB)
5 Apache Airflow, Containers, and TFX (4.13 MB)
1 Introduction (3.76 MB)
2 Overview of ML development challenges (13.73 MB)
3 How MLflow tackles these challenges (9.54 MB)
4 MLflow tracking (16.1 MB)
5 MLflow projects (11.15 MB)
6 MLflow models (25.06 MB)
7 MLflow model registry (7.75 MB)
8 Demo - Introduction (1.6 MB)
9 Deploying MLflow Locally Tracking Keras, TensorFlow, and Sckit-learn experiments (11.85 MB)
1 Course Summary (3.18 MB)]
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