FedLearn’s Post

Workplaces in both the public and private sectors are increasingly data-centric. Don't be left behind. Enroll in the FedLearn online course, Data Analysis (AIANA105), to understand the fundamental concepts of data analysis and the key terms to manage, analyze and use data at work—no matter your organization, role or rank. This self-paced, online course is part of our growing artificial intelligence and data science catalog. It is included when purchasing a catalog seat license OR can be bought individually at the SPECIAL price of $49 (for a limited time!). For details, visit https://lnkd.in/e8-ba6f8 #fedlearn #departmentofdefense #dod #intelligencecommunity #govcon #trainingprovider #traininganddevelopment #training #onlinelearning #onlinetraining #onlinecourses #asynchrous #artificialintelligence #ai #skillsgap #datascience #dataliteracy #datasciencetraining

The workplace is increasingly becoming data-driven. How would you rate your data acumen? Enroll in the FedLearn online course, Data Analysis (AIANA105), to understand the fundamental concepts and key terminology of data analysis to manage, analyze and use data at work—no matter your organization, role or rank. For details and to purchase, visit https://lnkd.in/e8-ba6f8 #fedlearn #departmentofdefense #dod #intelligencecommunity #govcon #trainingprovider #adaptivelearnig #traininganddevelopment #onlinetraining #onlinelearning #onlinecourses #asynchrous #artificialintelligence #ai #datascience #dataliteracy #datasciencetraining

Did you know that seat licenses are the most cost-effective way to purchase the FedLearn artificial intelligence and data science course catalog? The courses are strategically aligned with the U.S. Department of Defense strategies and are entry-level in nature. And what better way to learn about the power of AI than by taking courses on an AI-powered, online experience platform? Special rates for Federal government and contractor organizations of all sizes are available. Learn more and purchase at https://lnkd.in/eJQBfqCA #fedlearn #departmentofdefense #dod #intelligencecommunity #govcon #trainingprovider #traininganddevelopment #training #onlinelearning #onlinetraining #onlinecourses #asynchronous #artificialintelligence #ai #aitraining #datascience #datasciencetraining

At Databytes, we're committed to advancing technologies like Federated Learning to protect data privacy while enabling powerful AI solutions!!!    🔍 Ever wondered how machine learning models can be trained while keeping your data private and secure? This is where Federated Learning (FL) comes in—a breakthrough approach that allows collaborative model training without sharing raw data.     To make this advanced concept easy to understand, let's use a simple analogy: The Honey Bees and the Nectar 🐝🍯.     🌸 Traditional Machine Learning is like trying to create honey directly from the flower itself, which is impractical and doesn’t make sense. It involves moving all the data (like nectar) to a central location for processing. This can expose sensitive information and isn’t the most efficient approach.     Now, imagine a smarter way... 🤔     🌍 Federated Learning is like honey bees that fly from flower to flower, collecting only the nectar they need without taking the whole flower. The bees then return to their hive and use the collected nectar to create honey. The flowers (your data) stay untouched in their original place, while the bees (the model) learn and improve from the nectar. This method allows multiple entities or devices to collaborate on training a shared model while keeping all their data secure and local. It’s a revolutionary approach that enhances privacy and efficiency!     👇 Check out the video to see how Federated Learning compares to traditional methods. 👉 Stay tuned to our "Learn Technology with Databytes" series for more simple explanations of complex technologies!    #machinelearning #Dataprivacy #ArtificialIntelligence #TechInnovation #Databytes 

At Databytes, we're committed to advancing technologies like Federated Learning to protect data privacy while enabling powerful AI solutions!!! Ever wondered how machine learning models can be trained while keeping your data private and secure? This is where Federated Learning (FL) comes in: a breakthrough approach that allows collaborative model training without sharing raw data. To make this advanced concept easy to understand, let's use a simple analogy: The Honey Bees and the Nectar 🐝🍯. 🌸 Traditional Machine Learning is like trying to create honey directly from the flower itself, which is impractical and doesn’t make sense. It involves moving all the data (like nectar) to a central location for processing. This can expose sensitive information and isn’t the most efficient approach. Now, imagine a smarter way... 🤔 🌍 Federated Learning is like honey bees that fly from flower to flower, collecting only the nectar they need without taking the whole flower. The bees then return to their hive and use the collected nectar to create honey. The flowers (your data) stay untouched in their original place, while the bees (the model) learn and improve from the nectar. This method allows multiple entities or devices to collaborate on training a shared model while keeping all their data secure and local. It’s a revolutionary approach that enhances privacy and efficiency! Check out the video to see how Federated Learning compares to traditional methods: Stay tuned to our "Learn Technology with DataBytes" series for more simple explanations of complex technologies! #Machinelearning #Federatedlearning #Dataprivacy #ArtificialIntelligence #TechInnovation #Databytes 

Data plays a huge role to facilitate business operations. Over the years, Data has emerged as a science that influences business reshaping across the globe. So the issues related to data protection, data integrity, data based valuations etc have emerged as a new standard for business strategy. And now with the arrival of AI, Data Analytics & Data Visualization has become a real game changer leading the whole business world to a new evolution. Here’s your chance to get the up close insight of the Data Analytics Experts about the world of AI. SIGN UP FOR THE FREE WEBINAR ON 11th May at 7 PM Join us to explore new-age business transformation from a global perspective. Link to sign up https://lnkd.in/gkjbV7Dm #neotiaskilldevelopmentacademy #ambujaneotia #nsda #learn #training #opportunities #employment #jobupdates #upskill #careergrowth #skilldevelopment #chatgpt #openai #dataanalysis #project #datascience #data #deeplearning #aitutor #powerbionlinetraining #powerbi #powerbicourse #CorporateTraining #EnterpriseLearning #ProfessionalDevelopment #DataAnalytics #DataAnalyticsusingAI #AI #webinar #webseminar

Today I am attending Data+AI Summit organised by Data Bricks (https://lnkd.in/dhi7hKGB). You are not late by the way as tomorrow there will be a set of other sessions as well. I wanted to share some of the key takeways I got from today: The main challenge ahead of us in the age of LLMs, will be to move beyond generic models ("of the shelf") from "general intelligence" to "data intelligence" - fine-tuned based on the enterprise data. Turns out relying only on general intelligence gets correct coding answers (think SQL queries or company specific data fetching processes) around 50% of time. By leveraging enterprise data for specific use cases this number can be significantly improved. It is important to evaluate fine-tuned LLMs. But how do we do it? It is more tricky if you compare it to classical ML methods due to nuances of NLP. The evaluation ought to be task specific and sometimes might need to be qualitative relaying on human evaluation. Be careful with AI hallucinations there! Counter-intuitively, For organisations with large amounts of data there is benefit in training LLMs from scratch.

In the rapidly evolving realm of AI and data science, one of the most persistent challenges we face is efficiently managing an ever-growing array of data types. From images and videos to complex documents, the diversity of data demands a solution that's both versatile and powerful. Enter ApertureDB. 🔍 A New Era in Data Handling - ApertureDB isn't just another database. It represents a paradigm shift in how we approach data management for AI and machine learning projects. Its ability to seamlessly handle multimodal data including images, videos, and documents, makes it a unique asset in any data scientist’s toolkit. 🤖 Tailored for AI and ML Workflows - For those immersed in machine learning and AI, ApertureDB is more than just a database; it's a facilitator of innovation. Supporting large-scale ML training and inference operations, it's designed to streamline workflows and enhance efficiency. 💡 Empowering Data Professionals - ApertureDB is not only a tool; it’s a gateway to new possibilities. For data scientists and engineers, it offers a way to save time and focus on the core aspects of their projects, ensuring that their work is not just productive but also innovative. Try ApertureDB for free: https://lnkd.in/ewhPAF8f #DataManagement #AI #MachineLearning #ApertureDB #InnovationInTech #vectorsearch #vectordb #graphdatabase

📚 Want to level up your Prompt Engineering game? Coursera has a PE Specialization course covering basic topics to advanced data analytics use cases! 🔗 Check it out: https://lnkd.in/dgvRiBXR #ai #ml #promptengineering #courses #learning #learningeveryday #llm #datascience

🌍 Combating Model Forgetting in Federated Learning (FL) through Incremental Learning! One of the biggest challenges in Federated Learning (FL) is catastrophic forgetting—where models struggle to retain knowledge from prior updates as new data is integrated. This issue becomes especially prominent in FL, where data distributions can vary significantly across different devices and users. 🔄Enter Incremental Learning: an approach designed to help models retain knowledge while adapting to new data, making it ideal for overcoming forgetting in FL environments. How does Incremental Learning help? 🧠 Continuous Knowledge Retention: Incremental learning helps FL models “remember” previous data patterns and avoid degradation in performance as new updates come in. 📉 Addressing Non-IID Data: Federated Learning often deals with highly non-IID data (data that’s not identically distributed), which makes forgetting more likely. Incremental learning helps stabilize updates so models generalize better across varied user data. 🔒 Preserving Privacy and Adaptability: By adapting locally and incrementally, models can incorporate new data without forgetting, all while respecting the privacy of each device’s data. 💡 Tackling forgetting through incremental learning in FL can greatly improve model performance in real-world applications, from personalized healthcare to adaptive recommendation systems. #FederatedLearning #IncrementalLearning #AI #MachineLearning #DataPrivacy #CatastrophicForgetting #DataScience