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Accelerate innovation with Power Virtual Agents + AI Builder! Discover how these tools empower developers of all levels to create lightning-fast, AI-powered solutions. Explore the synergy of AI and low-code tools in our blog!

Companies that want to keep pace with AI and its business applications are still facing barriers to entry due to the cost and complexities of integrating accurate and trustworthy AI and machine learning projects. seekr Technologies Inc. is changing that. With the launch of its enterprise-ready platform, SeekrFlow, businesses can now train, validate, deploy, and scale AI applications in under 30 minutes—all through an intuitive, no-code interface. Congratulations to our Broadsheet Communications client for advancing the frontier of AI technology. Read more about it in SiliconANGLE & theCUBE below. https://lnkd.in/etvXCUR3

Breaking News 📰✨: GenAI Transforming Business Processes and Enterprise Value ㅤㅤㅤ 🤖 GenAI is revolutionizing business processes, productivity gains, and enterprise value. Companies are excited about leveraging Large Language Models (LLMs) like never before. ㅤㅤㅤ 🚀 Despite the huge potential, challenges exist with only 10-15% of companies having GenAI applications in production. Domino introduces new features for responsible and cost-effective GenAI project promotion. ㅤㅤㅤ 🛡️ AI Gateway ensures secure LLM access, controls costs, and enables easy model switching. Pinecone Vector Database connection accelerates RAG application development for enterprise Q&A and chatbots. ㅤㅤㅤ 💼 Domino provides a complete package for Enterprise Generative AI with AI Hub templates, coding assistants, fine-tuning wizard, data access, responsible AI practices, scalable deployment, and cost controls. ㅤㅤㅤ This post was generated and summarized by the HAL149 AI Assistant – your go-to for creating engaging content and growing your business with custom-trained AI Assistants. ㅤㅤㅤ #HAL149 #AIAssistant #ContentGeneration #GenAI #Dominopowered ㅤㅤㅤ

💡 Recommended read: Developing GitLab Duo: AI Impact analytics dashboard measures the ROI of AI https://lnkd.in/dghgyMR8 #devsecops #efficiency #ai

Companies that want to keep pace with AI and its business applications are still facing barriers to entry due to the cost and complexities of integrating accurate and trustworthy AI and machine learning projects. Seekr Technologies Inc. is changing that. With the launch of its enterprise-ready platform, SeekrFlow, businesses can now train, validate, deploy, and scale AI applications in under 30 minutes—all through an intuitive, no-code interface. Congratulations to our Broadsheet client for advancing the frontier of AI technology. Read more about it in SiliconANGLE & theCUBE below. https://lnkd.in/etvXCUR3

Let's talk about deploying #LLMs in production. Here are 4 approaches to deploy LLMs in production...Read👇 These four approaches range from easy and cheap to difficult and expensive to deploy, and enterprises should assess their AI maturity, model selection (open vs. closed), data available, use cases, and investment resources when choosing the approach that works for their company’s AI strategy. 1. Prompt Engineering with Context: Many enterprises will begin their LLM journey with this approach since it’s the most cost effective and time efficient. This involves directly calling third party AI providers like OpenAI, Cohere or Anthropic with a prompt. However, given that these are generalized LLMs, they might not respond to a question unless it’s framed in a specific way or elicit the right response unless it’s guided with some more direction. Building these prompts, also called “Prompt Engineering”, involves creative writing skills and multiple iterations to get the best response. 2. Retrieval Augmented Generation (RAG): Foundation models are trained with general domain corpora, making them less effective in generating domain-specific responses. As a result, enterprises will want to deploy LLMs on their own data to unlock use cases in their domain (e.g. customer chatbots on documentation and support, internal chatbots on IT instructions, etc), or generate responses that are up-to-date or using non-public information. However, many times there might be insufficient instructions to justify fine tuning a model, let alone training a new one. In this case, enterprises can use RAG to augment prompts by using external data in the form of one or more documents or chunks of them, and is then passed as context in the prompt so the LLM can correctly respond with that information. 3. Fine Tuned Model: While prompt engineering and RAG can be a good option for some enterprise use cases, we also reviewed their shortcomings. As the amount of enterprise data and the criticality of the use case increases, fine tuning an LLM offers a better ROI. When you fine tune, the LLM absorbs your fine tuning dataset knowledge into the model itself, updating its weights. So, once the LLM is finetuned, you no longer have to send examples or other information in the context of a prompt. 4. Trained Model: If you have a domain specific use case and a large amount of domain centric data, then training an LLM from scratch can provide the highest quality LLM. This approach is by far the most difficult and expensive to adopt. Enterprises need to be aware of costs related to training LLMs from scratch since they require large amounts of compute that can add up costs very quickly. This is an amazing article if you like to know more: https://lnkd.in/gD6gwxCu No matter what approach you choose, you need a vector database. Try SingleStore database for free: https://lnkd.in/gCAbwtTC

Looking for a new way to measure the ROI of #AI? Enter: the AI Impact analytics dashboard for GitLab Duo, available in GitLab 17.0. Swipe ➡️ to see what’s in store. More details here:

AI News: Anthropic Launches Claude Enterprise! I've just published an in-depth article on Anthropic's game-changing move in the AI space. Here are the key takeaways (featuring thoughts from Claude product lead Scott White): - Massive 500,000 token context window - equivalent to 200,000 lines of code! - 'Projects' allow for accessible integration of company data - Upcoming native integrations, starting with Github - Enterprise-level security with granular access control Read the full article to learn if Claude Enterprise is right for you: https://lnkd.in/dXSbEBzi What are your thoughts on this development? How do you see AI transforming business operations? #AI #AnthropicAI #ClaudeEnterprise #FutureOfWork

Hot off the press: Your handy guide to understanding the effectiveness of AI investments 💡 Learn how GitLab Duo’s AI Impact analytics dashboard was built to measure the ROI of AI. https://bit.ly/4cxcXdu

Enough about building #LLM apps, let's talk about deploying them in production. Here are 4 approaches to deploy LLMs in production. These four approaches range from easy and cheap to difficult and expensive to deploy, and enterprises should assess their AI maturity, model selection (open vs. closed), data available, use cases, and investment resources when choosing the approach that works for their company’s AI strategy. 1. Prompt Engineering with Context: Many enterprises will begin their LLM journey with this approach since it’s the most cost effective and time efficient. This involves directly calling third party AI providers like OpenAI, Cohere or Anthropic with a prompt. However, given that these are generalized LLMs, they might not respond to a question unless it’s framed in a specific way or elicit the right response unless it’s guided with some more direction. Building these prompts, also called “Prompt Engineering”, involves creative writing skills and multiple iterations to get the best response. 2. Retrieval Augmented Generation (RAG): Foundation models are trained with general domain corpora, making them less effective in generating domain-specific responses. As a result, enterprises will want to deploy LLMs on their own data to unlock use cases in their domain (e.g. customer chatbots on documentation and support, internal chatbots on IT instructions, etc), or generate responses that are up-to-date or using non-public information. However, many times there might be insufficient instructions to justify fine tuning a model, let alone training a new one.  In this case, enterprises can use RAG to augment prompts by using external data in the form of one or more documents or chunks of them, and is then passed as context in the prompt so the LLM can correctly respond with that information. 3. Fine Tuned Model: While prompt engineering and RAG can be a good option for some enterprise use cases, we also reviewed their shortcomings. As the amount of enterprise data and the criticality of the use case increases, fine tuning an LLM offers a better ROI. When you fine tune, the LLM absorbs your fine tuning dataset knowledge into the model itself, updating its weights. So, once the LLM is finetuned, you no longer have to send examples or other information in the context of a prompt. 4. Trained Model: If you have a domain specific use case and a large amount of domain centric data, then training an LLM from scratch can provide the highest quality LLM. This approach is by far the most difficult and expensive to adopt. Enterprises need to be aware of costs related to training LLMs from scratch since they require large amounts of compute that can add up costs very quickly. This is an amazing article if you like to know more: https://lnkd.in/gD6gwxCu No matter what approach you choose, you need a vector database. Try SingleStore database for free: https://lnkd.in/gCAbwtTC