In the ML Pipelines on Google Cloud course, you will delve into the world of machine learning pipelines with a focus on Google Cloud technologies. This comprehensive course equips you with the knowledge and skills to build and manage ML pipelines effectively. From understanding the components of TensorFlow Extended (TFX) to orchestrating pipelines and automating processes with continuous integration and deployment, you will gain a deep understanding of the entire ML pipeline lifecycle.
The course begins with an overview of TFX and progresses to cover topics such as orchestrating pipelines with TFX, custom components and CI/CD for TFX pipelines, managing metadata with TFX, continuous training with various SDKs, Kubeflow, and AI Platform Pipelines, continuous training with Cloud Composer, and ML pipelines with MLflow. Each module is designed to provide practical insights and hands-on experience, ensuring that you develop the essential skills to work with ML pipelines in real-world scenarios.
Upon completion of this course, you will be proficient in leveraging Google Cloud tools and platforms to build, automate, and manage ML pipelines across different machine learning frameworks, enhancing your capabilities as a machine learning professional.
Certificate Available ✔
Get Started / More Info
This course covers TFX, orchestration with TFX, custom components and CI/CD, metadata management, continuous training with various SDKs, Cloud Composer, and MLflow.
Gain a comprehensive understanding of the course and its objectives, setting the stage for your ML pipeline journey.
Delve into TensorFlow Extended (TFX), exploring its concepts, standard data and model components, pipeline nodes, libraries, and engage in an interactive TFX tutorial.
Learn to orchestrate pipelines with TFX, using tools such as Apache Beam and TFX on Cloud AI Platform, and participate in a hands-on workshop for practical application.
Explore the creation of custom components and continuous integration/continuous deployment (CI/CD) for TFX pipelines, followed by an immersive tutorial on the CI/CD process.
Discover metadata management with TFX, including pipelines metadata and ML metadata, and engage in an insightful workshop focused on managing pipelines metadata.
Understand continuous training with various SDKs, Kubeflow, and AI Platform Pipelines, and participate in a comprehensive workshop on continuous training with Kubeflow and AI Platform Pipelines.
Explore continuous training with Cloud Composer, covering Apache Airflow fundamentals, continuous training pipelines with Cloud Composer, and the integration of Apache Airflow, containers, and TFX.
Dive into ML pipelines with MLflow, including its features, solving development challenges, tracking, projects, models, and a demonstration of deploying and using MLflow.
Summarize and consolidate your learning from the course, reinforcing key concepts and insights gained throughout the modules.
AI Product Management equips professionals with foundational understanding of machine learning, human-centered design, and AI project management. No coding required,...
Supervised Machine Learning: Classification equips aspiring data scientists with hands-on experience in classifying categorical outcomes. Gain expertise in logistic...
Aprende a diseñar y entrenar redes neuronales convolucionales básicas utilizando Pytorch en este proyecto de 1 hora.
Supervised Machine Learning: Regression equips aspiring data scientists with hands-on experience in training regression models to predict continuous outcomes and...