Google Cloud Big Data and ML Fundamentals
Gain proficiency in the development of machine learning models and big data pipelines by utilizing Google Cloud's state-of-the-art tools, such as BigQuery, Dataflow, Vertex AI, and Pub/Sub.
Description for Google Cloud Big Data and ML Fundamentals
A Comprehensive Understanding of the Data-to-AI Lifecycle: Discover the fundamental process, obstacles, and advantages of employing big data and machine learning to implement AI solutions.
Big Data Pipeline Design with Google: Cloud Create and execute streaming pipelines that utilize Dataflow and Pub/Sub to facilitate seamless data processing.
Data Analytics on a Large Scale with BigQuery: Utilize Google Cloud's BigQuery to efficiently execute sophisticated analytics on large datasets.
Developing Machine Learning Models with Vertex AI: Examine the tools and methods available for the development of machine learning solutions on the Vertex AI platform of Google Cloud.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 3 weeks at 3 hours a week
Schedule: Flexible
Pricing for Google Cloud Big Data and ML Fundamentals
Use Cases for Google Cloud Big Data and ML Fundamentals
FAQs for Google Cloud Big Data and ML Fundamentals
Reviews for Google Cloud Big Data and ML Fundamentals
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Google Cloud Big Data and ML Fundamentals
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
This course is dedicated to the setting up of GPU-based environments, the deployment of local large language models (LLMs), and their integration into Python applications utilizing open-source tools.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
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.
Learn the skills necessary to operate, optimize, and implement large language models through practical experience with state-of-the-art LLM architectures and open-source resources.
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
Featured Tools
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
Explore the world of AI-powered language processing by acquiring the skills necessary to construct chatbots, analyze sentiment, and incorporate AI insights into practical applications.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.