Learn how to use Google Cloud Platform to train and deploy machine learning models for predictive analytics.
- [Kumaran] Businesses love predictions. Why? Because they want to predict what their customers want, and when they will need it. But, there's a lot of data here. Lots of businesses making lots of decisions. For scalability and reliability purposes, many of these prediction algorithms are built on cloud platforms, like Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Expertise in these platforms is an essential skill for an IT professional.
In this course, I will show you the technologies available on Google Cloud Platform for predictive analytics that create and deploy models in the cloud to enable data signs. You'll need prior familiarity with the basics of GCP platform, as well as Python programming. So join me, Kumaram Ponnambalam, in my course. Let's explore and experience the options for predictive analytics.
Author
Released
11/7/2018- Evaluating the machine learning tools in GCP
- Understanding the predictive analytics process
- Building models
- Training models with jobs
- Building and running predictions
- Best practices for cost control, testing, and performance monitoring
Skill Level Intermediate
Duration
Views
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Introduction
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1. ML Options in GCP
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Cloud Dataproc56s
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Cloud ML Engine1m 37s
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Cloud Natural Language1m 20s
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Cloud Translation1m 17s
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Cloud Vision1m 18s
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Cloud Video Intelligence1m 2s
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Cloud Dialogflow1m 14s
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2. Cloud ML Basics
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Models56s
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Model versions41s
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Jobs56s
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Predictive analytics process1m 55s
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3. Model Building with Cloud ML
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Understanding input data1m 30s
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Build and test model locally1m 53s
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Modify code to work with GCP1m 42s
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Creating a training package1m 21s
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Training using jobs3m 49s
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4. Predictions in Cloud ML
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Creating a model version2m 10s
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Running a prediction1m 37s
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5. Cloud ML Best Practices
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Cost control1m 17s
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Local testing1m 12s
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Performance monitoring1m 35s
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Conclusion
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Next steps41s
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Video: Why use predictive analytics on GCP?