MetaLearner Suggested Posts

[1] Fast decisions, rapid adaptations—welcome to the era of MetaLearner. Our vision is to provide enterprises with the flexibility to respond quickly to the rapidly changing demands of the market, increase the customization and personalization of dashboards, shorten product life cycles, and enhance productivity. Multiple components, including modern database technologies, vertical and horizontal processes, ERP system integration, decision support systems, and cyber-physical systems, come together seamlessly, allowing an enterprise to become smart and agile without sacrificing security and privacy. https://www.dhirubhai.net/posts/rnfc_fast-decisions-rapid-adaptationswelcome-activity-7216240926925451264-kyq7

[2] The value of AI in Enterprise Resource Planning is immense and could unlock tremendous opportunities in data insights and operational efficiency if implemented correctly. It is a race where whoever can bring AI to their customers first could establish the winning strategy. At MetaLearner we recognize the impact of AI in ERP and have assisted our clients in implementing AI in their SAP data. https://www.dhirubhai.net/posts/rnfc_experience-the-power-of-seamless-integration-activity-7211166650555920386-PnTe

[3] Specialized LLMs for specific use cases are the future. For example, when LLMs are incorporated into analytical workflows, they can save time by capturing, storing, and representing information in a way that facilitates decision-making. The ultimate promise of GenAI and automation is that they could complete entire analytical workflows for us. We do not actually want to do the job ourselves; we would prefer to just click a button and have the information analyzed for decision-making. We are already seeing some enterprises looking to automate data analytics, and this trend is likely to continue. The current enterprise software stack is ineffective in serving end customers. Many clients have spent years on various ERP implementations that never realized their envisioned benefits, with knowledge lost in glue code.?https://www.dhirubhai.net/posts/rnfc_this-chart-about-the-costs-of-training-ai-activity-7212296053973286912-ygfx

[4] A crucial task in time-series forecasting is the early identification of the most suitable forecasting method. For the past decade we have been designing, coding and optimizing a general framework for forecast-model selection using metalearning.https://www.dhirubhai.net/posts/rnfc_httpsgithubcomrnfermincotaacademic-activity-7210081953063014401-Ga2K

[5] During the past 15+ years of teaching data science to students, I noticed a significant mismatch in progression between academia and enterprise. For example, in the data science flow, there have been advancements in feature engineering such as feature-based decomposition and machine learning stacks such as Time-Series Dense Encoder (TiDE) and Conformal Quantile Regression (CQR). However, we have yet to see mass adoption of such techniques in enterprise, despite a few basis points of optimization meaning millions of dollars saved or generated for large companies. The root cause I have identified is the lack of high-quality education across different institutions, where multiple higher learning institutions are not updating their curricula and are not sharing the latest state-of-the-art forecasting stack with their students. https://www.dhirubhai.net/posts/rnfc_httpsgithubcomrnfermincotaacademic-activity-7215022130759938048-eKVX

[6] Orchestrating an AI application for highly scrutinized and impactful industries such as healthcare and finance requires extreme caution and experience. In the early stages of the Generative AI boom, there are numerous opportunities for applying Generative AI. However, many developers and clients face one of the biggest drawbacks of Generative AI—hallucinations. https://www.dhirubhai.net/posts/rnfc_architecture-for-sensitive-ai-applications-activity-7210733544006926336-TnBD

[7] 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. https://www.dhirubhai.net/pulse/metalearners-research-web-search-optimization-metalearner-ai-itr9e/