IBM Data, AI & Automation

📢 IBM announces the general availability of IBM Concert. Powered by IBM #watsonx, IBM Concert provides a new approach to application management, one that uses generative #AI to help app owners, SREs and developers proactively manage application risk and compliance. Discover gaps, prioritize insights and instrument changes to make your business more resilient, cost-effective and better performing. Start using IBM Concert today: https://ibm.co/4cmkWK6

We're excited to announce the general availability of the API Assistant feature in IBM API Connect, powered by IBM watsonx. 🙌 See how this new virtual assistant can help developers create better APIs faster: https://ibm.co/3AyQ6AO

We're excited to announce the general availability of the API Assistant feature in IBM API Connect, powered by IBM watsonx. 🙌 See how this new virtual assistant can help developers create better APIs faster: https://ibm.co/3AyQ6AO

What if I told you that finding the right data product could be as easy as subscribing to your favorite movie? With IBM Data Product Hub, you can subscribe to data and analytics products in minutes to accelerate data-driven innovation. Users across the organization can find high-quality data products that they need, subscribe to them, and have them delivered in various ways, all in a compliant, secure, and seamless manner. If all this sounds interesting, join our live webinars on November 5, 2024, at 9 AM ET and December 5, at 11 AM ET to learn more about Data Product Hub and how it addresses data-sharing challenges in an organization: 1. November 5, 9 AM: Reimagine data sharing and discovery to accelerate data-driven innovation: https://shorturl.at/yTGw7 2. December 5, 11 AM: Transform Business Intelligence by managing your data as a product: https://shorturl.at/z23ki Sajan Kuttappa #DataSharing #DataAsAProduct #DataMarketplace #DataExchange #DataDemocratization #DataProductHub

The world is all-in on AI and organizations are showing no signs of slowing down, but one question remains: How can you afford it? 💰 AI affordability is a complex matter that’s influenced by several factors, including initial costs, ongoing costs, skills, compute requirements, data availability and more—the answer may not be simple. The key is balance. Balance between ambitions and costs, between new investments and the current ones, and between going at it on your own and relying on trusted experts. Discover how to create that balance to start your AI journey in our latest IT Optimization guidebook: https://ibm.biz/Bdapaf

We are excited to announce that IBM has been ranked #1 in 4 out of 6 Use Cases evaluated in the 2024 Gartner Critical Capabilities for API Management report! Get the details here: https://ibm.biz/BdagZX

We are excited to announce that IBM has been ranked #1 in 4 out of 6 Use Cases evaluated in the 2024 Gartner Critical Capabilities for API Management report! Get the details here: https://ibm.biz/BdagZX

This is not a cooked demo. This is a real AI Agent that solves Github issues autonomously with remarkable efficiency. You should block a few minutes to watch this today. The agent localizes and fixes issues within 5 minutes, with an impressive success rate on the comprehensive SWE-bench task. Imagine the productivity that this gives to all software developers!!!

How to choose the best LLM for your use case 𝟭. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 - Start with task-based benchmarking: Choose a shortlist of LLMs and run tests specific to your use case (e.g., generate product descriptions, summarize long documents, or extract key insights). - Use open benchmark platforms like Hugging Face’s Evaluation or proprietary in-house benchmarks tailored to your data. 𝟮. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝘃𝘀. 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 - If your use case requires specialized knowledge, consider models already fine-tuned for your industry (like healthcare or finance). - For more general tasks, evaluate popular pre-trained models (e.g., GPT-4, LLaMA, Mistral) to see if they perform well out-of-the-box. 𝟯. 𝗣𝗶𝗹𝗼𝘁 𝗦𝗲𝘃𝗲𝗿𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗮 𝗦𝗮𝗻𝗱𝗯𝗼𝘅 - Set up a controlled environment and test models under real-world conditions. Look for how they handle edge cases and whether they require significant prompt engineering. - Pay attention to the ease of fine-tuning if customization is needed. 𝟰. 𝗔𝘀𝘀𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗮𝗻𝗱 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 - Check the support and community around each model. Open-source models like LLaMA have vibrant communities that offer quick help and resources. - Evaluate the ecosystem of tools (e.g., prompt optimization libraries, monitoring solutions, or integration plugins) that come with each model. 𝟱. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝘀𝘁𝘀 - For enterprise use, factor in not just model performance but also long-term sustainability. This includes how often the model is updated, security patches, and total costs. - Consider if the LLM vendor provides good SLAs for managed services or if it’s better to host open-source models on your infrastructure to manage costs effectively. What tips do you have to share with all of us that worked well?

We're excited to announce the preview of IBM Data Integration for Unstructured Data. 🙌 This capability will help unlock the true potential of generative AI by empowering data teams to ingest, transform, and pre-process their unstructured data, enabling RAG use cases: https://ibm.co/4e9VEiI