FoxyAI’s Post

In machine learning, artificial intelligence, and data science, fine-tuning a pre-trained model generally involves adjusting its parameters slightly to tailor it for a specific task that differs from the one originally trained on. In some cases, fine-tuning may only need to be done once.   Here are some scenarios where a single round of fine-tuning might be enough:

"Data processing infrastructures constitute the foundations of high-quality AI products. Mastering the Data Engineering skills needed for their design and implementation holds paramount value." Dive into our latest blog post where we delve into the crucial technical skills, practical examples, and navigate through the challenges and solutions in harnessing AI's potential. 🔥 🔎 Check it out: https://lnkd.in/dfaZ_Ui2

𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗼𝗻 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 The advent of AI and Machine Learning (ML) has revolutionized numerous fields, and database engineering is no exception. In this detailed exploration, we delve into how these groundbreaking technologies are reshaping the landscape of database engineering, transforming the way data is managed, optimized, and utilized. Continue Reading 👉 https://bit.ly/3B7AHax Explore a wealth of educational content or connect with us for business inquiries at Cloudastra Technologies! 🚀🌐 https://bit.ly/46QCLOt #AI #MachineLearning #DatabaseEngineering #DataScience #BigData #AIinDatabases #Automation #DataManagement #Cloudastra #CloudastraTechnologies

As using AI for data analysis becomes common, we’ll see a rise in “Feature Generation”. In Data science, Feature Engineering involves transforming a dataset by combining, calculating or extracting data from existing variables. Feature Generation will take parts of the dataset and enrich it with data from the web, or perform different types of abstractions to create new features for analysis. In this example, I’m using the new AI Analyst (coming very soon) to take an address and add a new column with the US region. Based on a text input it is able to extract the state name, understand what the abbreviation means, and classify it into a set of US Regions. Just imagine how time consuming this would be in traditional data analysis/science.

"..As machine learning grows beyond predictive models to faster, more profound, actionable, and compliant data-driven breakthroughs, several trends will shape the future of data science..": https://lnkd.in/e6nd8fes #ai #innovation #technology #data #futureofwork #digitaltransformation #aistrategy

"A team of computer scientists and AI researchers from FAIR at Meta, INRIA, Université Paris Saclay and Google, has developed a possible means for automating data curation for self-supervised pre-training of AI datasets. The group has written a paper describing their development process, the technique they developed and how well it has worked thus far during testing. It is posted on the arXiv preprint server. As developers and users alike have been learning over the past year, the quality of the data that is used to train AI systems is tied very closely to the accuracy of results. Currently, the best results are obtained with systems that use manually curated data and the worst are obtained from systems that are uncurated." #datacuration #aidatasets

🚀 Transform Your Enterprise Data Landscape in Less Than 90 Days! 🚀   🖥️ Augment and Enhance Your Existing Systems On-Premise with the most advanced Knowledge Graph Neural Network (KGNN).   Automatically connect, cleanse, transform, prepare, and enrich unstructured data for data science, analytics, and AI projects.   ✨ Key Features: 🔹 Automated ETL 🔹 Autonomous Semantic Data Mapping 🔹 Self-Generating Knowledge Graph Construction 🔹 Instantly contextualize ingested data against a global knowledge base, providing immediate context and relevance. 🔍 Why Choose Our KGNN? 🔹 Easy Data Consolidation, Pre-Processing, and Enrichment On-Premise 🔹 Boost and enhance your advanced applications with AI-ready, RAG-ready graph-contextualized data. 🔹 Experience powerful querying and analytics. 💡 Clean, Graph-Contextualized Data on the Fly: 🔹 Minimize manual data handling. 🔹 Fuel your data science, analytics, and AI initiatives with comprehensive, relevant data that provides the whole picture. 🔹 Reduce errors, improve accuracy, reduce bias, increase context, and enhance explainability. Equitus KGNN helps your systems deliver insights faster and more efficiently by automatically transforming your data into real-world, actionable datasets.    #InformationArchitecture #DataManagement #EnterpriseData #SystemEngineering #KnowledgeGraph #AI #DataTransformation

Why use vector database? The AI revolution is transforming industries of all kinds. It comes with a new data challenges. Everybody is searching for something and we want accurate and reliable information, fast!. We also want a computer system that can suggest, recommend, and also influence us to do better things. To make that happen, the best solution right now is using technologies such as large language models and these models are very large, both in terms of data size and context. However, unlike conventional databases which are organized in tables, vector databases uses fixed-dimensional vectors to represent data points, and group their similarities. This approach is very advantageous and leads to faster query response. LLMs or Generative AI applications rely on vector embeddings of these data for understandings and also long term memory so that you can ask follow up questions. Using vector database architecture helps similarity search by efficiently organizing and retrieving data points based on their inherent similarities. There are many services and application that provides vector database capabilities. My favorites ones are Pinecone, Chroma, and FAISS(library). What are some of your favourites? #day31 #365dayschallenge #365daysofAI #vectordatabase #chroma #pinecone #AI #similaritysearch

Is adding “meaning” to data the next big thing in AI? 🤯 I just watched this awesome interview with Ingo Mierswa, the founder of RapidMiner, and Kate Strachnyi. The first time I worked with a Data Scientist in 2008 he was actually using RapidMiner for Machine Learning! It was the perfect solution. Check out RapidMiner: https://bit.ly/4f0D7Xu Now RapidMiner is part of Altair, our sponsor for this post, and it seems the team behind it made the platform even more valuable and feature-rich. Dr. Mierswa explains how Altair’s RapidMiner platform is elevating AI by adding a semantic layer that brings out the true meaning of data. Through knowledge graphs powered by Large Language Models and ontologies, Altair goes beyond traditional data structures. That allows AI and ML models to genuinely “understand” complex relationships. Of course, as an Engineer I don’t have in-depth knowledge about semantic layers and knowledge graphs, but I find this approach super cool. This approach not only makes data exponentially more valuable for insights but also empowers teams across an organization to collaborate and drive impactful, data-driven decisions. So, for anyone serious about leveraging data and AI, I see RapidMiner as an all-in-one solution designed to fit seamlessly into real-world business processes. In short, it’s built for impact—definitely worth checking out if you're looking to elevate your data projects! Again, check out RapidMiner here: https://bit.ly/4f0D7Xu #sponsored #bigdata #dataengineering #datascience #machinelearning #LLM #AI #Altair 

⚙️ Data Preprocessing mastery- the key to Machine Learning Success! 🧠 Before diving into complex algorithms, one very important step for any machine learning project is usually: Data Preprocessing. A clean, well-prepared data set forms the foundation of every accurate and reliable model. Here's how to make your data ML-ready: 🔹 Missing Value Handling: Perform mean/median imputation; perform higher level techniques for filling the gap or drop the rows/columns - whatever is applicable. 🔹 Feature Scaling: Scale your features to be in the same range. This encompasses Normalization, scaling between 0 and 1, and Standardization, scaling based on the z-score. 🔹 Encoding Categorical Data: Transform categorical variables into a numerical format by employing techniques such as One-Hot Encoding or Label Encoding, thereby rendering them comprehensible for algorithms. 🔹 Data Partitioning: One of the primary things to do is to partition your data into training and test sets for appropriate model performance evaluation. 🔹 Outlier Detection: This is the process of identifying anomalies in data, which could distort the results; it employs techniques such as IQR or Z-score to identify extreme values. 🔹 Efficient data preprocessing will ensure that your model captures real patterns in your data and avoids common pitfalls. 🛠️ #MachineLearning #DataScience #DataPreprocessing #AI #TechTips #CleanData