ML Pipelines on Google Cloud By google - cloud

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

Files Included :

Spoiler

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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ML Pipelines on Google Cloud
Duration: 2h 52m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 512 MB
Genre: eLearning | Language: English​

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX).
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google's production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.

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