Lee iles

IBM's Senior Director of Product Marketing, Ben Ball, discussed the company's advances in AI and the new product IBM Concert during an interview preceding the Intelligent Automation not Conference. IBM Concert is designed to manage the complex data involved in app development and maintenance, utilizing AI to improve application performance. Ball highlighted the importance of making AI's recommendations understandable and questioned to ensure transparency. IBM aims to offer a use-case-neutral suite of AI technologies, and IBM Concert has already demonstrated potential in simplifying data management for organizations. As part SEC of IBM's AI solutions, the product will be showcased at the Intelligent Automation Conference, highlighting its role in transforming tech operations. Ball advises organizations new to AI to approach it with a clear use case in mind for the best outcomes, as IBM Concert continues to progress and offer deeper insights.

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Great presentation by Tony Du of #dataminr https://lnkd.in/eJM4_aVE as every day they process billions of input of disparate data types worldwide to transform data to alerts....

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https://lnkd.in/ey_uX3vG interesting evolution of RAG from unstructured data such as PDF documents to structured data such as database tables. Interesting walk through here…

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Maximizing Data Extraction Precision with Dual LLMs Integration and Human-in-the-Loop #Azure #Microsoft #OpenAI #LLM #GenAI #AI

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🚀 Unleash the true power of PostgreSQL with AlloyDB! Join our upcoming webinar on September 4th to see how Google's fully-managed PostgreSQL database service can revolutionise your database experience. Save your spot today → https://lnkd.in/e3GHchjP

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One of the most common deployments of the #DenodoPlatform in organizations across the globe is serving as a semantic layer that simplifies data consumption to increase self-service for business users. The use of a #semanticlayer is a well established practice. However, in this era of generative AI (#GenAI), we were also wondering if it can enhance the text-to-SQL capabilities commonly provided by large language models (#LLMs). Can the features that facilitate #data usage for business users have an impact on the success rate of #generativeAI models? Read this new #DataManagement blog by Pablo Alvarez-Yañez, Gloval VP of Product Management at Denodo, and discover all the key factors! https://buff.ly/44XE3HX

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Meet Llama 3.2 on Databricks: faster and now multi-modal. The Llama 3.2 release pushes the frontier of enterprise #GenAI with new smaller models for latency + cost-sensitive use cases and larger multimodal models for visual understanding. Customers can easily get started tuning the Llama 3.2 models securely and efficiently on their data. Available today in Databricks Mosaic AI. 

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Reflecting on this article, written by Adrian Bridgwater and contributed to by Richard Timperlake, the value of data products is clear, particularly to those organisations harnessing exabytes of data that are stuck in silos without any lineage or schema taxonomy. With Snowflake introducing Polaris Catalog and Confluent introducing Tableflow, the ecosystem needed in order to adopt a data products mindset is clearly becoming more accessible, and the potential is unprecedented. Couldn’t be more excited to see how things evolve both from the product and the market side when it comes to this space.

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AI for AI: makes it easy to maintain complex provisioning & workflows. As from the comments section..."This is exactly the right sort of usage of GenAI for today. Targeted and useful and nuanced."

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12 Reasons Why #AI Readiness Is the New Imperative: https://lnkd.in/eeVZTE8f (article by me, with Pure Storage) ———— #CDO #GenAI #GenerativeAI #DataScience #MachineLearning

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Great advice as ever from Nicola Reed. Learn tips and tricks for securing your Snowflake instance using the likes of Duo Security and Okta

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Really powerful advancement of HPC + AI along with even logical qubits now!!!

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Great insight from the panel at the #datajam event on developing an effective London market data strategy with Chris Payne ‘Key is clarifying and quantifying the value of any data used’ ‘For technology solutions, do proof of value (POV) exercises, not proof of concept (POC) with customers’

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We've got some exciting announcements today with Anthropic 🎉 Take a look: 📣 Claude 3 Opus, Anthropic’s most intelligent model, is now generally available on Vertex AI. ⚙️ Tool use is available for the Claude 3 model family on Vertex AI, enabling Claude to act as an autonomous agent to handle tasks requiring real-time data or complex computations. 📈 Provisioned throughput is available for the Claude 3 model family on Vertex AI to get assured performance and predictable costs for your production workloads. Learn how to get started → https://goo.gle/4aHWL7U #google #vertex #AImodels

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Here is a condensed version of my Braindate by e180 sessions at Databricks Data + AI Summit NVIDIA Spark RAPIDS in Amazon Web Services (AWS) EMR provides 2x-5x acceleration and 30% cost savings for the 25% most GPU friendly queries NVIDIA Spark RAPIDS in Microsoft Azure Databricks provides 34% cost savings Use NVIDIA Spark RAPIDS qualification tool to predict the speedup up your workload Use Speedata.io Spark workload analyzer to predict the speedup of your workload Speedata.io delivers 20x-200x acceleration and 50x better performance/$ than CPUs and GPUs

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IBM's Granite 3.1 8B LLM: A quick test of its 'swift'iness in coding. As an ex-IBMer its always great to see Big Blue releasing open-source software. I tested it with Agent Orange using a few Swift coding challenges, with one particular question (shown) being a favourite of mine - a complex swift 6 concurrency question I use on every new LLM I come across. The result was pretty disappointing, though even the frontier models (including Gemini 2.0) get this question wrong. No model has yet to work out the 'sendability hell' challenges with Swift 6. That said, Qwen 2.5 Coder 7B gives a better answer, with Qwen 2.5 Coder 32B still being my go-to open model.

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My latest on how infostealers play an outsized role in breaches and what organizations can do. Snowflake is just the latest example of how this complex ecosystem is impacting the landscape.

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