Mistral AI’s Post

This just in: We're the first to bring you Codestral, Mistral AI’s first open-weight #generativeAI model explicitly designed for code generation tasks—as a fully-managed service in Vertex AI Model Garden! But wait, there's more! We've also added Mistral AI's Large 2 (Mistral AI's flagship model, offering the best performance and versatility of any of the company’s models to date) and Nemo models (12B model delivering exceptional performance at a fraction of the cost) to Vertex AI Model Garden, now generally available via our Model-as-a-Service (MaaS) endpoints. ✨ These models excel at coding, mathematics, and multilingual tasks. Codestral, in particular, is optimized for code completion, documentation, and test generation. ✨ Here’s why you’ll love working with Mistral AI models on #VertexAI: ➕ Get started in minutes with our Model-as-a-Service. No setup or infrastructure complexities. You can further explore the model capabilities using our side by side evaluations. ➕ When moving to practical applications, you can deploy at scale using our pay-as-you-go pricing and without having to manage any infrastructure. You can also maintain consistent performance and capacity with Provisioned Throughput, available in the coming weeks. ➕ And of course, you benefit from Google Cloud's robust security, privacy, and compliance measures, building in a world-class infrastructure purpose-built for AI workloads. Ready to explore these models on Vertex AI? Get started here → https://goo.gle/3LBTM6F

Reading this post, I am sensing a trend here. Probably the next decade we would evolve around the MAGs being in competition to bring in the latest and greatest open source FMs in their respective model playground. It feels like the early days of Spark, Map Reduce all over again, on an AI context instead of compute. There might be a complete open source LLM dominance and MAGs playing the role of providing the PaaS around exploiting these foundation models. The only counter to this trend might be Open AI themselves who are exclusively coupled with Microsoft, or at least for now? #GenAI #Trends #CloudWars

This just in: We're the first to bring you Codestral, Mistral AI’s first open-weight Хэштег #generativeAI model explicitly designed for code generation tasks—as a fully-managed service in #VertexAI Model Garden! But wait, there's more! We've also added #MistralAI's Large 2 (Mistral AI's flagship model, offering the best performance and versatility of any of the company’s models to date) and Nemo models (12B model delivering exceptional performance at a fraction of the cost) to Vertex AI Model Garden, now generally available via our Model-as-a-Service (MaaS) endpoints. ✨ These models excel at coding, mathematics, and multilingual tasks. Codestral, in particular, is optimized for code completion, documentation, and test generation. ✨ Here’s why you’ll love working with Mistral AI models on Хэштег #VertexAI: ➕ Get started in minutes with our Model-as-a-Service. No setup or infrastructure complexities. You can further explore the model capabilities using our side by side evaluations. ➕ When moving to practical applications, you can deploy at scale using our pay-as-you-go pricing and without having to manage any infrastructure. You can also maintain consistent performance and capacity with Provisioned Throughput, available in the coming weeks. ➕ And of course, you benefit from Google Cloud's robust security, privacy, and compliance measures, building in a world-class infrastructure purpose-built for AI workloads. Ready to explore these models on Vertex AI? Get started here → https://goo.gle/3LBTM6F

Generative AI Is there any AI that can answer the following questions of business intelligence? "Who, in the Ontario province of Canada, have new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇" province of China, have new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" With our intellectual property (IP), a Chinese-English multilingual metadata, we can get the full list answers for the above questions. Do you or any of your contacts need our expertise/IP to do the data analysis that AI can't do? Metadata is an enabler. It is like a treasure map for treasure hunting. Without metadata, like a treasure map, NO data can be found/retrieved, even by the most advanced technologies, like AI, high-end chips, supercomputers, etc. https://lnkd.in/g-aJFnXR

👉 𝐓𝐡𝐢𝐬 𝐣𝐮𝐬𝐭 𝐢𝐧: Codestral, Mistral AI’s first open-weight #generativeAI model explicitly designed for code generation tasks—as a fully-managed service in Vertex AI Model Garden! Additionally Mistral AI's Large 2 (Mistral AI's flagship model, offering the best performance and versatility of any of the company’s models to date) and Nemo models (12B model delivering exceptional performance at a fraction of the cost) to Vertex AI Model Garden, now generally available via 𝐌𝐨𝐝𝐞𝐥-𝐚𝐬-𝐚-𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐌𝐚𝐚𝐒) endpoints. ✨ These models excel at coding, mathematics, and multilingual tasks. Codestral, in particular, is optimized for code completion, documentation, and test generation. ✨ Here’s why you’ll love working with Mistral AI models on #VertexAI: ➕ Get started in minutes with our Model-as-a-Service. No setup or infrastructure complexities. You can further explore the model capabilities using our side by side evaluations. ➕ When moving to practical applications, you can deploy at scale using our pay-as-you-go pricing and without having to manage any infrastructure. You can also maintain consistent performance and capacity with Provisioned Throughput, available in the coming weeks. ➕ And of course, you benefit from Google Cloud's robust security, privacy, and compliance measures, building in a world-class infrastructure purpose-built for AI workloads. Read more -> https://lnkd.in/dRqGnSdY

Exciting news! 🚀 Mistral AI's open-weight generative AI model, Codestral, is now available in Vertex AI Model Garden, along with Large 2 and Nemo models. These models excel in coding, mathematics, and multilingual tasks, with Codestral optimized for code completion, documentation, and test generation. I love the ease of use and scalability offered by Vertex AI's Model-as-a-Service, allowing developers to get started in minutes without infrastructure complexities. The pay-as-you-go pricing and Provisioned Throughput features make it easy to deploy at scale while maintaining consistent performance. Thanks for sharing this update! I'm looking forward to exploring the capabilities of Mistral AI models on Vertex AI and leveraging Google Cloud's robust security and compliance measures. #GenerativeAI #VertexAI #MistralAI #AIModels #CodeGeneration

💡 Introducing Pinecone Serverless: A revolutionary serverless vector database for building generative AI applications 🌟 Features: 💪 Cost-Effective (up to 50x lower cost): - Usage-based pricing - only pay for what you use. - Separated pricing for reads, writes, and storage optimizes costs. - Efficient indexing and searching further reduce expenses. 🚀 Effortless Operations: - No need to manage complex pod, shard, or storage limit configurations. - Simply create an index, load data, and start querying. ⚡️ High-Performance Results: - Supports live index updates, metadata filtering, and hybrid search. - Experience low latencies and consistent performance for warm queries (cold-start queries will have additional latency). In addition, $100 in free usage credits are available during the public preview. 🚀 The link to the original article can be found in the comments. 👇 #PineconeServerless #GenerativeAI #VectorDatabase

Fireworks AI Soars with $52M Series B Funding! 🚀 Fireworks AI Raises $52M, Valued at $522M! 🚀 Fireworks AI, an innovative player in generative AI, has just secured $52 million in a Series B round led by @Sequoia Capital, achieving a $522 million valuation. This round also saw participation from tech giants like @Nvidia, @AMD, and @MongoDB, along with renowned investors like Sheryl Sandberg and Howie Liu. Fireworks AI offers developers access to ultra-fast, cost-effective AI models that are customizable and secure. CEO Lin Qiao highlighted their unique approach, saying, "Our LoRA fine-tuning allows developers to customize models swiftly, maintaining performance and cost efficiency." Since its inception, Fireworks AI has partnered with industry leaders like @AWS, @GoogleCloud, and @Oracle to optimize model infrastructure. Their platform’s capabilities extend across text, image, audio, and more, making it a versatile tool for developers. 🔍 Key Stat: Fireworks AI’s technology has been adopted by major city transit agencies like New York's MTA and Washington D.C.'s Metro. What's your take on the future of specialized AI models? Could they outperform mega models in real-world applications? #AI #SaaS #CloudComputing #GenerativeAI #TechFunding #SequoiaCapital #Nvidia #AWS #GoogleCloud #Oracle #Saasverse

I've been away from Kubeflow Pipelines on Vertex AI for a bit, focusing on other (Gen)AI tools, but I return back to them today... and wow, I was impressed! 🤯 📚 Documentation: A+ for the improvement. The Kubeflow docs have gotten a major upgrade, making it way easier to find the info you need. Kudos to the maintainers! 👏 🐛🔍 Local Pipeline Testing: Game Changer! Now you can test your Vertex AI pipelines locally before deploying them. This is huge for catching those pesky bugs early on. Debugging just got a whole lot smoother! If you haven't checked out the latest Kubeflow and Vertex AI pipeline updates, now's the time. Check out the documentation 👇 🔗 Kubeflow documentation: https://lnkd.in/dJf-hTwN 🔗 Vertex AI Pipeline documentation: https://lnkd.in/deJ69VZc Let me know if you've tried this out or have any questions. Happy coding! 👩💻 #VertexAI #Kubeflow #MLOps #MachineLearning #AI #GoogleCloud