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Google > Case Studies > Vertex AI Adds Mistral AI Model for Powerful and Flexible AI Solutions

Vertex AI Adds Mistral AI Model for Powerful and Flexible AI Solutions

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Customer Company Size
Large Corporate
Region
  • Europe
Country
  • European Union
Product
  • Vertex AI
  • Mistral-7B
  • Google Kubernetes Engine
Tech Stack
  • vLLM
  • Google Cloud
  • BigQuery Omni
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Environmental Impact Reduction
  • Digital Expertise
Technology Category
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Data Management Platforms
  • Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
  • Software
  • Professional Service
Applicable Functions
  • Product Research & Development
  • Business Operation
Use Cases
  • Edge Computing & Edge Intelligence
  • Remote Asset Management
  • Predictive Maintenance
Services
  • Cloud Planning, Design & Implementation Services
  • Software Design & Engineering Services
  • System Integration
About The Customer
Mistral AI is a prominent player in the European AI landscape, known for its commitment to developing high-performance and efficient open-source foundation models. The company is dedicated to advancing AI technology by optimizing models for better performance and sustainability. Mistral AI's collaboration with Google Cloud aims to enhance the accessibility and adoption of AI solutions, making it easier for businesses of all sizes to implement AI-driven products and services. The company's focus on open-source solutions aligns with the broader industry trend of fostering innovation and collaboration in the AI and machine learning ecosystems.
The Challenge
Mistral AI, a leading provider of AI solutions in Europe, is focused on designing highly efficient open-source foundation models. The challenge lies in integrating these models into platforms that can accelerate AI adoption across various business sizes. The need is to make AI products and services more accessible, while also ensuring sustainability and efficiency in terms of training time, cost, and energy consumption. Additionally, there is a demand for AI ecosystems that support data sharing and open infrastructure, allowing organizations to manage their AI infrastructure effectively.
The Solution
Mistral AI has partnered with Google Cloud to integrate its Mistral-7B model into Vertex AI, a move that facilitates the deployment and management of AI models. This integration allows businesses to experiment with and fine-tune AI models using Vertex AI Notebooks, which support collaborative model development and MLOps management. The use of vLLM, an optimized LLM serving framework, enhances model inference performance, providing users with a robust platform for AI experimentation and deployment. Google Cloud's commitment to multi-cloud and hybrid cloud environments ensures that users can maintain data privacy and security while leveraging open-source technologies for AI infrastructure management.
Operational Impact
  • The integration of Mistral AI's model with Vertex AI enables businesses to easily launch AI products and services, accelerating AI adoption across various industries.
  • Google Cloud's open-source technologies provide users with the flexibility to manage their AI infrastructure in a secure and private environment, ensuring data privacy and control.
  • The use of Vertex AI Notebooks facilitates collaborative model development, allowing data scientists to experiment with different modeling techniques and manage the model lifecycle effectively.
  • Mistral AI's model optimization leads to significant improvements in sustainability and efficiency, reducing the environmental impact of AI operations.
  • The partnership between Mistral AI and Google Cloud supports the broader AI community by promoting open-source solutions and fostering innovation in AI and machine learning ecosystems.
Quantitative Benefit
  • Mistral-7B model integration reduces training time and cost.
  • Energy consumption and environmental impact are minimized.
  • High-speed and accurate model inference is achieved with GQA and SWA methods.

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