Description for Google Cloud Big Data and ML Fundamentals en Francais
Data-to-AI Lifecycle on Google Cloud: Gain an understanding of the complete lifecycle, from data acquisition to the deployment of an AI model, through the use of Google Cloud services.
Understanding the Design and Management of Real-Time Data Streaming Pipelines with Dataflow and Pub/Sub: Develop an understanding of the development and operation of real-time data streaming pipelines using Dataflow and Pub/Sub.
Analyzing Big Data with BigQuery: Develop the ability to efficiently analyze large-scale datasets using BigQuery.
Developing Machine Learning Solutions with Vertex AI: Investigate the various methods of generating machine learning models on Google Cloud with Vertex AI.
Level: Intermediate
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 en Francais
Use Cases for Google Cloud Big Data and ML Fundamentals en Francais
FAQs for Google Cloud Big Data and ML Fundamentals en Francais
Reviews for Google Cloud Big Data and ML Fundamentals en Francais
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 Francais
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.
This training offers essential knowledge in the domains of data, computation, cryptography, and programming, with a focus on the Ruby on Rails framework.
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.
Featured Tools
Develop your skills in image processing, augmented reality, and object recognition to prepare yourself to create cutting-edge AI-powered visual apps.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
A structured guide to the study of business opportunities in the chatbot space, as well as the comprehension, design, and deployment of chatbots using Watson Assistant.
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.
Compare & Review AI Tool Directory AI Course Marketplace
