Embark on a transformative journey into the world of machine learning pipelines with ML Pipelines on Google Cloud - 日本語版. This course, offered by Google Cloud, offers a deep dive into the orchestration of cutting-edge ML pipelines using TensorFlow Extended (TFX) and other tools, empowering participants to harness the full potential of machine learning in real-world scenarios.
Throughout this comprehensive course, participants will absorb knowledge from seasoned ML engineers and trainers, delving into the intricacies of ML pipeline management and metadata using TensorFlow as the foundation. The initial modules provide a solid understanding of TFX standard pipeline components and the TFX interactive context for prototype development. Participants will then explore the automation and reuse of ML pipelines across multiple frameworks, including TensorFlow, PyTorch, Scikit Learn, and XGBoost, underlining the importance of continuous integration and continuous deployment (CI/CD) in pipeline automation. Additionally, the course sheds light on utilizing Google Cloud's Cloud Composer for orchestration in continuous training pipelines, while also exploring the management of the complete machine learning lifecycle using MLflow.
With a focus on practical application, participants will engage in hands-on labs to reinforce their learning, ensuring they can implement their newfound knowledge effectively in real-world scenarios. By the end of the course, participants will emerge with a profound understanding of ML pipeline orchestration and be equipped to tackle complex machine learning challenges with confidence and expertise.
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This course comprises an in-depth exploration of ML pipeline orchestration, covering TFX standard pipeline components, orchestration, CI/CD, metadata, continuous training, and MLflow. Participants will gain hands-on experience and practical knowledge, equipping them to excel in the realm of machine learning pipeline orchestration.
Module 1: The course overview provides a brief introduction, setting the stage for the in-depth exploration of ML pipeline orchestration in subsequent modules.
Module 2: Delve into the fundamentals of TFX, gaining a comprehensive understanding of its standard data and model components, pipeline nodes, and libraries for prototype development.
Module 3: Explore TFX orchestration, leveraging Apache Beam and Cloud AI Platform to gain practical insights into deploying TFX pipelines.
Module 4: Gain expertise in customizing TFX pipeline components and understand the workflow for continuous integration and continuous deployment (CI/CD) in pipeline automation.
Module 5: Discover the intricacies of managing ML pipeline metadata, understanding TFX pipeline metadata and its data model.
Module 6: Learn the automation of ML pipelines across multiple SDKs, KubeFlow, and AI Platform Pipelines for continuous training using various frameworks like TensorFlow, PyTorch, Scikit Learn, and XGBoost.
Module 7: Gain insights into utilizing Cloud Composer for orchestration in continuous training pipelines, leveraging Apache Airflow's fundamental concepts.
Module 8: Explore MLflow and its role in managing the complete ML lifecycle, including tracking, project management, model management, and deployment.
Module 9: Wrap up the course by summarizing the key learnings and insights gained throughout the comprehensive exploration of ML pipeline orchestration.
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