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.
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Acerca de este Curso
¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negociosQué aprenderás
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.
¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negociosOfrecido por
Programa - Qué aprenderás en este curso
Introduction
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
Summary
Reseñas
- 5 stars69,07 %
- 4 stars23,88 %
- 3 stars4,46 %
- 2 stars1,46 %
- 1 star1,11 %
Principales reseñas sobre SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD
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.
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
Really recommended to get introduce to GCP resources and capabilities.
Good structure and overview of things which can be accomplished in GCP Analytics
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