Fuel AI的动态

You can now extract structured data from from various unstructured text sources and access web APIs! ?? DeepLearning.AI released "Function Calling and Data Extraction with LLMs," developed in collaboration with Nexusflow, taught by Jiantao Jiao and Venkat Srinivasan prompting LLMs and AI agents, enabling them to use external tools. You will: ?? Work with NexusRavenV2-13B, a 13 billion parameter open-source model that excels in function calling tasks while being compact enough to host locally. ?? Learn to use function calling to extract structured data from unstructured text. ?? Access web APIs & build end-to-end applications. ?? Develop an application that processes customer service transcripts. ?? Build LLM-powered applications to analyze feedback. ?? Automate data entry processes. ?? Enhance search functionalities. Check it out: https://lnkd.in/e7m2AKtX #AI #LLMs #NexusRaven #automation

#AI and #LLM based applications and frameworks are creating complex business problem solving opportunities like never before. #business #tech #Data #datascience #modelling #generativeAI #ML #promptengineering #bots

??The decision intelligence is not about #rules #data #machinelearning #process #businessintelligence The basic principle in #DecisionIntelligence is to ensure you start with "Decision," as the name suggests. The "Decision" is the very core concept and a separate entity from #businessrules #process #machinelearning #algorithms. ?Decision modeling is a technique that allows you to explicitly define business decisions independently. You can depict a complex business decision in a hierarchical and multistep decision graph. Once the decision is modeled, you can ?? * Manage them across the enterprise * Automate them with Composite AI techniques * Reuse them throughout applications, processes, and systems ??The benefit is that you will get to use multiple techniques, such as in #businessrules #machinelearning #optimization, for a single decision as they are appropriate, and the decision model coordinates between these pieces. ??Making and executing decisions becomes Quick, and the outcome of the automated decisions will be Accurate, Consistent, and Transparent, which we call - Quick ACT? decisions.

AI: Revolutionizing Database Management Tired of manual data cleansing and query optimization? Let AI handle it! ?? By automating repetitive tasks, AI empowers database administrators to focus on strategic initiatives. Key benefits: * Efficiency: Streamlined processes and reduced human error. * Advanced Analytics: Uncover hidden insights through pattern recognition. * Proactive Decision-Making: Leverage data trends to stay ahead. #talks_about_AI #DatabaseManagement #Automation #DataScience #MachineLearning

???? ???????????? ???????? ?????? ???????????????? ?????????? ???????? ???????????????? ???????????????? It’s clear by now that LLMs ??????????????? won’t solve complex, high-value business tasks. Pure LLM-based agents struggle with reasoning in unique contexts, and their performance depends on the quality and freshness of the data they’re fed. They’re also very sensitive to small changes in prompts, making them difficult to adapt and maintain in dynamic enterprise environments. And many other issues. The net is that building reliable, enterprise-grade AI agents is ?????????????????? than traditional software—and will be for years to come, despite what other companies are pitching. ?? At #TektonicAI, we’ve taken a different approach. Our architecture integrates neural with symbolic methods to create reliable semantic domain representations that are the foundation of our AI Agents??. We’ve applied this approach to solve fragmented, incomplete data challenges that slow business planning and decision-making. By semantically combining data from internal and external sources, we ensure that sales and revenue teams have reliable, cross-silo insights, speeding up decisions, improving forecasting, and boosting conversions. ?? Interested? Let us show you how. #AIForBusiness #DataArchitecture #TektonicAI #AIAgents #LLMs

AI can seriously simplify data quality operations when used correctly. Boring and repeatable activities are stopping data teams from validating data quality. It was fun to design a data platform, learn new technologies, and solve challenges, but nobody wanted to test it. That is when AI can solve these challenges. GenAI, in particular, could be useful for documenting data sources and answering questions about the meaning of data. Here are a few use cases when AI makes data quality easier to implement: ?? Detect anomalies in data without configuring all the rules. ?? Automatically configure data quality rules to detect the most likely errors. ?? Use vector embedding to understand the type of data stored in columns. ?? Reduce effort in managing data quality incidents. ?? Analyze sample values and the data schema to write documentation of datasets. ?? Let users ask a chatbot to find a healthy dataset to use. Right now, not all of these options are supported. Some might require significant computing power if we ask a GenAI to verify every value in a 10B record dataset. Anyway, check out DQOps, an open-source data quality and observability tool that already supports many of these capabilities. #dataquality #dataengineering #dataqatesting #DAMAGeorgia #dataqualityassociation

?? ?????? ?????????????? ?????? ???? ???????????? ???????? ????????????????????: ???????????????? ???? When building AI applications, managing data validation and serialization can become overwhelming. That’s where ???????????????? ???? steps in—bringing simplicity, speed, and structure to your workflows! The challenge is that AI models depend heavily on accurate, well-structured data. Poorly validated or incorrect data can cause errors, inconsistencies, and degrade model performance. ???????? ???? ???????????????? ????? Pydantic AI is an extension of the popular Pydantic library. It helps with: ? Data Validation: Ensures incoming data meets predefined rules. ? Type Enforcement: Automatically converts data types (e.g., strings to integers). ? Serialization: Transforms outputs into formats ready for APIs or databases. ? ?????? ???????????? ???????????????? ????? ???Ensures clean and validated data for ML models. ?? Saves time by reducing manual data processing. ?? Avoids costly debugging from type mismatches or invalid inputs. ??? Have you tried Pydantic AI yet? If not, now’s the perfect time to give it a shot and streamline your AI workflows! ?#DataScience #MachineLearning #DataValidation #PydanticAI #CleanData #AIModels #DataQuality #Automation #MLWorkflows

Excited to share that I've just finished the Jasper AI course and received a certificate of achievement from Coursiv. The program focused on enhancing skills in data analysis, spreadsheet automation, and optimizing data management efficiency. #SkillDevelopment #DataAnalysis #Automation #chatgpt #AI #digitalmarketing

?????? ???? ???? ???????????????????? ????????????????????????: ?????????????????????? ?????? ???????? ?????????? While building Gen AI-based applications, the pertinent challenge raised is current state of existing data layer that is highlighted as a bottleneck. Maybe one needs to rethink assumptions around the notion that "???? ?????????????? ?????? ???????? ???? ???????? ???? ?????? ???????????????????? ????????" in the Gen AI implementation. ? ????????’?? ??????: 1?? ???????? ??????’?? ?????????? ???????????????? ???? ????????????????????????'?? ????????:?unlike traditional AI systems, LLMs don’t require training on your data to deliver value. Instead, they interact with it in real-time, learning from the systems they pull data from over time. This capability allows LLMs to work with your existing data structures and improve their responses with operational context. This means many traditional data challenges such as inconsistencies across sources, silos, or redundancies are no longer barriers and there are work arounds for building a functional AI applications. 2?? ?????????? ???? ?????? ??????????, ?????? ????????:?real question isn’t whether data is perfect. It’s about problems that needs to be solved and whether AI can solve specific use case. Gen AI models can derive value from less-than-perfect data. The key is defining actionable use cases that align with business goals. 3?? ???????? ???? ??????’?? ?????????? ??????:? * Scalability issues: systems can handle AI-powered workloads. * Data integrity problems: require robust governance. * Latency challenges: latency can impact user experience. ? Gen AI can enable organizations to move faster and innovate. Time to expand the narrative from "data isn’t good enough" to "how to leverage what exists?"

??? "Unlock Your Data's Power with Altair" ??? Tired of struggling with data analysis? Altair makes it easy to find valuable insights that help your business grow. Here's what Altair offers: ?? Simple to use: No data expert needed! Analyze data with ease. ?? Hidden gems revealed: Uncover patterns and trends to make confident decisions. ?? Faster results: Boost your productivity with Altair's powerful tools. Plus, Altair RapidMiner for AI and machine learning! ?? RapidMiner makes it easy, even for beginners: ??? No coding required: Build AI models without writing a single line! ??Get started quickly: User-friendly interface for everyone. ?? Faster insights: Get results fast to stay ahead of the competition. Ready to transform your data analysis? Visit [https://lnkd.in/gHDcWvAn] and take control! #Altair #DataAnalytics #AI #RapidMiner #onlyforward