Google Cloud Big Data and ML Fundamentals en Espanol
Using Google Cloud's advanced tools learners will acquire the knowledge necessary to develop and execute machine learning models and big data pipelines.
Description for Google Cloud Big Data and ML Fundamentals en Espanol
Data-to-AI Lifecycle on Google Cloud: Understand the complete lifecycle of data collection to AI model deployment using Google Cloud's services.
Designing Streaming Pipelines with Dataflow and Pub/Sub: Master the art of designing and managing real-time streaming data pipelines using Google Cloud's Dataflow and Pub/Sub technology.
Analyze Big Data with BigQuery: Become proficient in the analysis of large-scale data using BigQuery to obtain quicker insights using Big Data.
Building Machine Learning Solutions with Vertex AI: Explore a variety of techniques for the development and deployment of machine learning solutions on Google Cloud, utilizing Vertex AI for model development.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 8 hours at your own pace
Schedule: Flexible
Pricing for Google Cloud Big Data and ML Fundamentals en Espanol
Use Cases for Google Cloud Big Data and ML Fundamentals en Espanol
FAQs for Google Cloud Big Data and ML Fundamentals en Espanol
Reviews for Google Cloud Big Data and ML Fundamentals en Espanol
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 en Espanol
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
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.
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.
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.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
Featured Tools
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
Acquire an extensive understanding of reinforcement learning, deep neural networks, clustering, and dimensionality reduction to effectively address real-world machine learning challenges.
This course covers Python programming, TensorFlow for linear regression, and app development for stock market prediction.
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
Compare & Review AI Tool Directory AI Course Marketplace
