MetaLearner | LinkedIn (original) (raw)
About us
MetaLearner is revolutionizing how businesses interact with ERP systems through AI-driven automation. Our AI Copilot transforms the data experience, removing the need for manual data sorting. Simply ask a question, and our AI dynamically generates instructions and pipelines to deliver hyper-personalized insights. This breakthrough saves time and empowers users with immediate, actionable answers, reshaping the way businesses leverage data for smarter decision-making.
Industry
Technology, Information and Internet
Company size
2-10 employees
Type
Privately Held
Employees at MetaLearner
Updates
- MetaLearner reposted this
Boost operational performance with MetaLearner (no relation). This AI-powered platform optimizes ERP systems, making advanced analytics accessible to all users. Learn more about how MetaLearner leverages Llama: https://bit.ly/3ZVQKTc - MetaLearner reposted this
Co-founder, Head of Technology @ MetaLearner
3mo
MetaLearner has successfully concluded our showcase in Suzhou! We were met with great enthusiasm from potential clients, investors, and students during NVIDIA‘s半程展示 showcase. The problem MetaLearner is solving is resonating strongly with Chinese businesses and data scientists, and we are thrilled to validate our solution in this market. We are grateful to Maggie Liu and the Nvidia China team for inviting us to present the opportunities MetaLearner brings to the Chinese market. We are especially grateful to Benjamin Chan, whose guidance has been valuable from the very beginning. His deep insights and hands-on support have been instrumental in helping MetaLearner refine our messaging and better communicate our strengths in Mandarin to potential clients and investors. Thanks to his efforts, we were able to present a compelling pitch and build meaningful connections at the event. Through this event, we also had the opportunity to meet other startups interested in leveraging MetaLearner to accelerate their growth. This way, they can focus on addressing their core challenges without worrying about the complexities of navigating their data.Susan Marshall Dr. Deb Goswami Jose Manuel Lama Morel Rafael Nicolas Fermin Cota Kevin Good Melvyn Sim - While Text2SQL, Retrieval-Augmented Generation (RAG), and Table-Augmented Generation (TAG) have made strides in automating data tasks, they still fall short when it comes to handling real-world challenges like ambiguous data and messy tables. At MetaLearner, we’ve taken a different approach. By building a proprietary data platform and fine-tuning our AI models to integrate business logic, we achieve greater accuracy and reliability—outperforming existing methods with a 93.3% accuracy on key benchmarks. Check out how MetaLearner is changing the game for enterprises dealing with complex data environments.#AI #GenerativeAI #DataAutomation #TechInnovation #MetaLearner #LLMs
MetaLearner: A New Standard for AI Data Queries MetaLearner on LinkedIn - We’re thrilled to share that Meta has spotlighted MetaLearner in their latest blog! In the post, Meta dives into how we’re using Llama's powerful AI models to make data science tools more accessible and intuitive for businesses. With this collaboration, we’re enabling non-technical users to harness AI-driven insights with ease. A big step forward in transforming ERP systems and decision-making processes! Check out Meta's full blog post to learn more about how we’re making data science more accessible: https://lnkd.in/grt9z8_J.
Making data science more accessible to enterprises with Llama ai.meta.com - At MetaLearner, we're constantly pushing the boundaries of AI to deliver cutting-edge solutions for our users. In this blog, we’ll answer key questions about our experience using Llama 3, including which versions we’ve integrated, how our use of Llama has evolved, and how it powers the AI-driven insights that transform the way businesses interact with data. Learn how MetaLearner harnesses the capabilities of Llama to meet our clients' needs and drive innovation in the ERP space.
Building Smarter Solutions with Llama: How MetaLearner Leverages Llama for AI-Driven Insights MetaLearner on LinkedIn - Exciting News from Hangzhou, China! MetaLearner was thrilled to participate in Alibaba’s Apsara Conference in Hangzhou, China, where we had the incredible opportunity to share our vision of the future of AI-driven ERP systems. During our live session, which was broadcast nationwide, we showcased how MetaLearner is revolutionizing business data interactions using NVIDIA’s powerful technologies, including NIM, NeMo, and RAPIDS. These cutting-edge tools allow us to deliver scalable AI models and real-time, actionable insights, empowering companies to make smarter, data-driven decisions with greater efficiency and speed. The response we received was extraordinary! Many organizations in China shared the same challenges MetaLearner is solving, and we’ve connected with several key partners eager to adopt our solution. We were also honored to present our product to Maggie Liu and the NVIDIA team, where we received positive feedback and an invitation to return to Suzhou next month to continue sharing our story and product. A special thank you to Alibaba Cloud and NVIDIA for inviting us to this incredible event. We’re excited about the opportunities ahead and look forward to further collaboration with industry leaders in China and beyond. The future of data-driven decision-making is here, and we’re just getting started
#Alibaba #ApsaraConference #NVIDIA #NIM #NeMo #RAPIDS #AI #MachineLearning #ERPSystems #AIpowered #Innovation #DataAutomation #GenerativeAI #BusinessTransformation #ChinaTech
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- Great job Carl Voller on this impressive achievement! We are proud to release the fastest Excel converter, fully integrated into our solution to handle various report customization tasks. This exemplifies the engineering excellence at MetaLearner, enabling us to deliver an unparalleled experience to our users. This solution accelerates how our AI defines and uploads customized reports securely to the users, delivering hyper-personalized reports in a matter of minutes.
Full Stack Engineer at MetaLearner | Silver TechBlazer 2019 | WWDC'22 Scholar
4mo Edited
I might have just developed the fastest CSV to XLSX converter on the market by a significant margin! A week ago, Ting Hui Lim challenged me to improve a MetaLearner feature's performance by optimising the export of a large Pandas DataFrame to an XLSX file. At first, the task seemed straightforward, so we tested the built-in Pandas to_excel method. However, for a dataset containing 580,000 rows and 72 columns, the conversion took 180 seconds—far from ideal in terms of user experience. While 180 seconds might sound reasonable given the dataset size, having users wait three minutes for a conversion is definitely not optimal. As a learning opportunity, I decided to explore a more efficient approach using Rust, along with helper libraries like PyO3 and rust-numpy. The result? A significantly faster solution that delivers a much better experience. I call this solution "excel-rs". Given a dataset container 1 million rows and 9 columns, excel-rs significantly out performed any other solution in the Python world. excel-rs was up to 45 times faster than pandas, and up to 12.5 times faster than the fastest available tool on pip (pyexcelerate). In fact, I decided to turn the underlying Rust program into a CLI tool, and the tool also out performed every CSV to XLSX converter I found on GitHub. When compared against csv2xlsx, a somewhat popular CSV to XLSX conversion tool on GitHub, excel-rs was over 12 times faster! To achieve these optimisations, I employed the following techniques: 1. Wrote my own XLSX writer, as the available open source packages were either too slow, or had too many features I did not need. 2. Dealt with data as Vec instead of Vec to remove performance hits from type conversions. 3. Used stack-allocated u8 arrays for processing information such as cell IDs to reduce the need for Rust to repeatedly allocate and deallocate under the hood + improve access speed. 4. Limit the use of Clone or Copy to reduce repetition of data in memory and reduce the number of drop_in_place called by Rust. 5. Use vectors with_capacity to prevent repeated memory allocations, especially when eventual size is already known. 6. Implemented FromSql trait that overrides defaults in rust-postgres to efficiently deal with bytes returned from Postgres Server, eliminating need for bytes to ever be converted into another type before a final Vec. I've since released excel-rs as a Python pip package "py-excel-rs" and as a CLI tool downloaded from GitHub Releases "cli-excel-rs". The Python package even includes utility functions to efficiently export numpy ndarrays and to mass export a Postgres Database to XLSX. In the future, I do hope to implement even more optimisations (such as moving cell references to a ShortVec) and also support for Golang through an FFI. If this sounds like something you'd use, a star over on GitHub at https://lnkd.in/g3QT7p6j would be really appreciated#excel #python #pip
GitHub - carlvoller/excel-rs: Rust and Python utilities to efficiently convert CSVs to XLSX github.com - We’re excited to share MetaLearner’s research on optimizing text-based data using Nvidia NIMs and the newly introduced Llama 3.1. In this blog, we demonstrate our innovative approach to optimizing traditional web search retrieval-augmented generation pipelines. This new methodology addresses common challenges such as speed, accuracy, and the risk of hallucination, ensuring a streamlined and reliable process.
MetaLearner's Research on Web Search Optimization MetaLearner on LinkedIn