Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
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Acerca de este Curso
Qué aprenderás
Understand use-cases for real-time streaming analytics.
Use Pub/Sub asynchronous messaging service to manage data events. Write streaming pipelines and run transformations where necessary.
Understand both sides of a streaming pipeline: production and consumption.
Interoperate Dataflow, BigQuery and Pub/Sub for real-time streaming and analysis.
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Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Programa - Qué aprenderás en este curso
Introduction
In this module, we introduce the course and agenda
Introduction to Analytics and AI
This modules talks about ML options on Google Cloud
Prebuilt ML model APIs for Unstructured Data
This module focuses on using pre-built ML APIs on your unstructured data
Big Data Analytics with Notebooks
This module covers how to use Notebooks
Production ML Pipelines with Kubeflow
This module covers building custom ML models and introduces Kubeflow and AI Hub
Custom Model building with SQL in BigQuery ML
This module covers BigQuery ML
Custom Model Building with AutoML
Custom model building with AutoML
Summary
This module recaps the topics covered in the course
Reseñas
- 5 stars68,75 %
- 4 stars24,08 %
- 3 stars4,47 %
- 2 stars1,52 %
- 1 star1,16 %
Principales reseñas sobre SMART ANALYTICS, MACHINE LEARNING, AND AI ON GCP
In general - very hot industry knowledge, however it feels that this content needs some time to get flawless and robust as the other ones from this specialization.
Excellent course. Gets pretty advanced with developing ML pipelines with Kubernetes Engine, but otherwise very accessible.
Content was fun and exciting but some exercises/graded labs inside this course are very unclear with the instructions and also took a long time to finish (model training).
Great course to have a complete overview of the GCP platform and components.
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