Vertex AI Studio - Introduction
Utilizing Vertex AI Studio for model management, integrating with Gemini multimodal capabilities, employing effective prompts, and optimizing models through tuning methods are all topics addressed on the course page.
Description for Vertex AI Studio - Introduction
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Google Cloud
Duration: 1 hour to complete
Schedule: Flexible
Pricing for Vertex AI Studio - Introduction
Use Cases for Vertex AI Studio - Introduction
FAQs for Vertex AI Studio - Introduction
Reviews for Vertex AI Studio - Introduction
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Vertex AI Studio - Introduction
This learning path provides a thorough overview of generative AI. This specialization delves into the ethical considerations that are essential for the responsible development and deployment of AI, as well as the foundations of large language models (LLMs) and their diverse applications.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Learn how to use Gemini for Google Workspace to boost productivity and efficiency in Gmail through its generative AI features.
Understand Generative AI, its potential and challenges, and the responsible use of the Gemini Enterprise add-on.
Learn to leverage Google Cloud's data-to-AI tools, generative AI capabilities, and Vertex AI for comprehensive ML model development.
Learn to use Vertex AI on Google Cloud for no-code AutoML model development, training, and deployment, while integrating ML with cloud tools and adhering to Responsible AI principles.
Outlines methods to determine main products, develop streaming pipelines, explore alternatives, and define essential steps for machine learning workflows on Google Cloud.
Data Engineering on Google Cloud. Embark on a vocation in data engineering. Provide business value through the application of machine learning and big data.
Explore the functionality, practical applications, limitations, and advancements of diffusion models, including their text-to-image applications.
This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.
Featured Tools
Begin your journey to becoming an AWS Solutions Architect by beginning here. Acquire the necessary skills and knowledge to develop architectural solutions on AWS and prepare for the AWS Certified Solutions Architect - Associate exam.
Acquire the ability to utilize AI tools and concepts to implement innovative, ethical, and efficient project management practices.
Learners will gain the fundamentals necessary to implement AI solutions on Microsoft Azure with this course specialization, which will set them up for success with the AI-900 competency.
Learn to integrate sophisticated tools such as ChatGPT into cybersecurity practices while managing AI and machine learning risks.
In a nutshell, this concentration helps business professionals get ready for the CDSP exam by teaching them how to put data science knowledge to use in real-world scenarios.