Smart Analytics, ML, and AI on GCP ?
Streamline data analysis and deployment by mastering the integration of machine learning into data pipelines using Google Cloud products such as AutoML, BigQuery ML, and Vertex AI.
Description for Smart Analytics, ML, and AI on GCP ?
Understanding the Concepts of AI, ML, and Deep Learning: In order to establish a solid foundation, it is essential to understand the differences between artificial intelligence, machine learning, and deep learning.
Utilizing Machine Learning APIs for Unstructured Data: Discover the utilization of machine learning APIs for the analysis and processing of unstructured datasets.
Developing Machine Learning Models with BigQuery ML: Directly generate machine learning models in BigQuery by employing SQL syntax and execute commands from Notebooks to facilitate analysis.
Implementing Machine Learning Solutions with Vertex AI: Learn how to deploy production-ready machine learning solutions using the Vertex AI platform from Google Cloud.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 3 weeks at 2 hours a week
Schedule: Flexible
Pricing for Smart Analytics, ML, and AI on GCP ?
Use Cases for Smart Analytics, ML, and AI on GCP ?
FAQs for Smart Analytics, ML, and AI on GCP ?
Reviews for Smart Analytics, ML, and AI on GCP ?
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Smart Analytics, ML, and AI on GCP ?
Featured Tools
Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.
This curriculum clarifies the strategic, ethical, and compliance consequences of AI implementation in organizational contexts.
Learn to build machine learning solutions using Generative AI on AWS, including an understanding of AWS cloud computing and utilizing services like Amazon Bedrock.
Explore the use of generative AI tools to enhance data preparation, querying, and machine learning model development in data science workflows through hands-on projects.
The course teaches advanced AI development for real-world applications by integrating intuitive learning and hands-on projects.