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.
About this Course
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.
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TOP REVIEWS FROM SMART ANALYTICS, MACHINE LEARNING, AND AI ON GCP
I couldn't complete the Kubeflow lab due to issues that I encountered setting it up. Overall, the course has given me a good understanding of Machine Learning model creation options available on GCP
It was a good decision to do this course as i learn and practiced lot in GCP. Thank you the team for amazing support guidance and instructions. Course content and material was appreciated. Thanks.
Very good ML course to introduce students with Google Cloud machine learning capabilities. Maybe there should be a lab for AutoML (after video lessons), as it exists on Qwiklab platform.
This course helped me to do smart analytics, and in my current job I was able to apply Machine Learning easily on GCP, and I helped my team to the AI platform like experts.
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