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
What you will learn
Differentiate between ML, AI and Deep Learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from Notebooks.
Create ML models by using SQL syntax in BigQuery and without coding using AutoML.
Syllabus - What you will learn from this course
Introduction to Analytics and AI
Prebuilt ML model APIs for Unstructured Data
Big Data Analytics with Notebooks
Production ML Pipelines
Custom Model building with SQL in BigQuery ML
Custom Model Building with AutoML
- 5 stars69.02%
- 4 stars23.97%
- 3 stars4.43%
- 2 stars1.45%
- 1 star1.10%
TOP REVIEWS FROM SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD
Excellent course. Gets pretty advanced with developing ML pipelines with Kubernetes Engine, but otherwise very accessible.
Great Big Picture about ML options on GCP, with good highlighting to main advantages and differences for each option.
Great insight about using machine learning on Google cloud platform. I am impressed
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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