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 ?
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Learn to build machine learning solutions using Generative AI on AWS, including an understanding of AWS cloud computing and utilizing services like Amazon Bedrock.
Acquire the skills necessary to program powerful systems in Rust. Through projects in data engineering, Linux tools, DevOps, LLMs, Cloud Computing, and machine learning operations, acquire the skills necessary to develop software that is both efficient and robust, utilizing Rust's distinctive safety and speed.
Become an adept in the field of machine learning. Break into the field of artificial intelligence by mastering the fundamentals of deep learning. Recently upgraded with state-of-the-art methodologies!
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.
Gain practical experience in optimizing, deploying, and scaling machine learning models using Google Cloud Platform through a structured five-course specialization with hands-on labs and a focus on advanced topics and recommendation systems.
Become a machine learning engineer. Enhance your programming abilities with MLOps
Construct and train neural networks and tree ensemble methods using TensorFlow, while applying effective machine learning practices for real-world data generalization.
Learn to develop, train, and assess neural networks using TensorFlow to resolve classification issues by understanding the fundamental principles of neural networks.
Acquire practical full stack development skills, knowledge of Cloud Native tools, proficiency in front-end development languages, and build a GitHub portfolio through hands-on tasks and a capstone project.
Featured Tools
Brief Summary This course analyzes the deployment of machine learning models on Arm microcontrollers, with an emphasis on real-world applications in edge computing.
Develop proficiency in AI risk management by emphasizing security, impartiality, and alignment with business objectives through the use of frameworks such as the NIST AI RMF.
The course offers a practical experience with potent, free AI tools to generate media content, while also equipping students with an understanding of the evolving risks associated with AI.
Empowering learners to utilize AI tools for innovative and effective course design and delivery, this course offers an in-depth understanding of AI applications in education.
Develop expertise in the exposure and deployment of large language models via application programming interfaces (APIs), configure server environments, and incorporate natural language processing (NLP) functionalities into applications.