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 Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
About this Course
- 5 stars69.03%
- 4 stars23.92%
- 3 stars4.45%
- 2 stars1.45%
- 1 star1.11%
TOP REVIEWS FROM SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD
Great Big Picture about ML options on GCP, with good highlighting to main advantages and differences for each option.
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
Good structure and overview of things which can be accomplished in GCP Analytics
Excellent course. Gets pretty advanced with developing ML pipelines with Kubernetes Engine, but otherwise very accessible.
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