Aditi Consulting’s Post

https://lnkd.in/g-pc6WG9 Silent Data Errors in data centers are a big problem and require multi-phased approach to identify and potentially eliminate. Tartan provides a machine learning solution to identifying problem devices before field failure may occur.

🚀 Learn how to efficiently downsample 6.48 billion high-frequency records to 61 million minute-level records in just 41 seconds with DolphinDB! Watch the demo for detailed operations and witness outstanding performance in data downsampling. #DataScience #BigData #AI #DolphinDB

Normal distribution of data is preferred for ML algorithms. But how to check if data is in normal distribution.

Consider this comparison: JP Morgan's proprietary data set is 150 petabytes, whereas GPT-4 was trained on an internet dataset less than one petabyte. Big Deal right now: We are mostly done training on Public Data. But the volume and richness of YOUR enterprise can provide a substantial edge in training sophisticated AI models. Make sure to read the T&C before leaking it all TLDR summary of the talk below: Public Data and the Data Wall The AI industry faces a significant challenge: the exhaustion of easily accessible public internet data for training models. Known as the “data wall,” this phenomenon means that further improvements from public sources alone are becoming increasingly difficult. The Need for Frontier Data Experts advocate for "Frontier data" to overcome this limitation. Unlike familiar datasets, Frontier data includes complex reasoning chains, intricate discussions, and unique agentic behaviors that are rarely documented online. This uncharted territory holds the potential to significantly advance AI capabilities. Proprietary Data Large enterprises possess vast amounts of proprietary data, serving as a significant competitive advantage. However, they must balance leveraging their data while ensuring its security and avoiding sharing with competitors. On-Premises Models Recognizing the immense value of their proprietary data, many enterprises opt for customizable on-premises AI models, offering greater control over data security and usage, and allowing organizations to avoid reliance on external parties and safeguard their competitive edge. https://lnkd.in/dxcNW8FV

Concept drift and data drift are two important challenges that ML Ops addresses. Concept drift refers to changes in the underlying distribution of the target variable, which can cause the model's predictions to become less accurate. Data drift, on the other hand, refers to changes in the distribution of the input variables, which can also impact the model's accuracy. https://lnkd.in/dwmHnG2q

SuperRAG! Or using LLMs for helping select the best possible grounding data. A great model will not solve for poor data. Using a great model for helping get the best possible data https://lnkd.in/dCMvxzyA

In an era of data overload, discerning meaningful patterns is crucial for business success. Pattern Recognition provides the analytical prowess necessary to transform raw data into strategic intelligence, facilitating smarter, data-driven decisions. Our comprehensive blog delves into how this essential technology enhances capabilities in Computer Vision, Speech Recognition, and beyond. Read the full blog post here: https://lnkd.in/gDexAu9A #PatternRecognition #DataAnalytics #ComputerVision #MachineLearning #ArtificialIntelligence

https://lnkd.in/gS2_vB2g Auto Remediation, a blend of ML and rule-based systems, dramatically improving data platform efficiency. It automates error handling, reduces costs, and optimizes Spark job performance. The Bayesian method for Spark configuration suggestions is especially intriguing.

What is data matching what is fuzzy, logic and fuzzy matching, read on https://lnkd.in/gCP-cE7B