Fluree’s Post

Movie Recommendation System Using TMDB Dataset Made a Machine Learning Project  Project Overview This project involves building a movie recommendation system using a dataset of 5000 movies from The Movie Database (TMDB). The project is divided into several key stages: data processing, vectorization, model building, front-end development, and deployment. The final application is hosted on Streamlit Cloud. Key Stages 1.⁠ ⁠Data Processing Data Collection: Acquire the dataset containing details of 5000 movies from TMDB. Data Cleaning: Handle missing values, remove duplicates, and standardize data formats. Feature Engineering: Extract and create relevant features (e.g., genres, cast, director, keywords). 2.⁠ ⁠Vectorization Text Vectorization: Convert textual data (e.g., movie descriptions, genres) into numerical vectors using techniques like TF-IDF, Count Vectorizer, or Word2Vec. Similarity Calculation: Compute similarity scores between movies using cosine similarity or other relevant metrics. 3.⁠ ⁠Model Building Recommendation Algorithm: Implement a recommendation algorithm that suggests movies based on the similarity scores. Collaborative filtering and content-based filtering can be used. Evaluation: Test the model using appropriate metrics to ensure accuracy and relevance of recommendations. 4.⁠ ⁠Front-End Development Streamlit: Build an interactive web interface using Streamlit. User Input: Allow users to input a movie title to get recommendations. Display Results: Show recommended movies with details like title, genre, and poster. Additional Features: Include features such as filtering by genre, rating, or release year. 5.⁠ ⁠Deployment Streamlit Cloud: Deploy the Streamlit application on Streamlit Cloud for easy access and sharing. You can check this out at: https://lnkd.in/e33tzf_K The code for this project is available on my Github.

🔗 Chat with your Confluence - A Guide to Building AI Chatbots for Confluence Our latest blog post explores the process of creating an AI-powered chatbot that integrates with Confluence data, aiming to improve how teams access and interact with their knowledge base. We begin by discussing Airbyte, an open-source data integration platform that provides more than 300 connectors, allowing for amazing integration scenarios. The integration of PGVector, an extension for efficient vector operations, optimizes the system for executing large language model (LLM) queries. The guide also covers LangChain, a tool that connects different technologies, enabling smooth data workflows. This integration is essential for developing a chatbot powered by LLMs like GPT-4, which can understand complex queries and provide accurate, context-aware responses. We provide step-by-step instructions to help you get started, from setting up your environment to using PyAirbyte (an incredible python module for running Airbyte jobs in any python script) for data syncing and leveraging Langchain's capabilities for a dynamic AI interaction layer. This guide offers both theoretical knowledge and practical skills to help you implement your own AI chatbot, improving data retrieval and utilization within your organization. For those interested in combining AI with data management—including software developers and data enthusiasts—this guide serves as a starting point for developing advanced chatbot solutions that push the boundaries of AI in business processes. Check out the full guide to learn more! 👉 Read the full guide here: https://lnkd.in/dSWqMqzC #AI #DataManagement #Confluence #Chatbot #Airbyte #Langchain #PGVector #TechnologyIntegration #Optimization

Are you ready for your OWN No-Code LOCAL AI for FREE with out-of-the-box ability to make Hive of Agents (will explain below, it's mega-cool, trust me)? I’ll tell you more… Listen → I’ve been using this masterpiece for last 2 month. It’ super easy: 1. Insert your OpenAI Key , 2. Create your own Agents (or use default, they are great!) , 3. Create your own Materials ( Text files or code with api… AI Console will help to create them… Just ask ;) ) And you UNLOCK a POWER TO CONTROL Your AI! You did a setup once. I added, as an example, a Product Manager Agent with Top Frameworks, a Management Consultant Agent + Guides how to structure any business problem, Project Manager Agent with information about the top Project Frameworks, Pre-Sales Engineer Agent that is power by OUR INTERNAL COMPANY’S DOCUMENTS (and I’m sleeping well, everything is stored on the local machine, no safety concerns at all!!!)… And what I need to do is just to DROP the TASK into the chat and BOOM… bunch of Agents work on my task iteratively (deciding on the right Agent, I don’t need to worry at all). Like I can tell the console, that I have a new customer, send the call log and voila 1️⃣ Product manager agent will evaluate customer requests and our latest experience as a worldwide company. And will create a Product Epics and Features Suggestions 2️⃣ Pre-Sales Engineer will come to have a look and evaluate the timelines and prices 3️⃣ Project Manager Agent will cover the gaps (like draft questions to clarify with customer) and create a Project Roadmap proposal. The sweet thing is that they (Agents) work in one chat together. I don’t need to switch from chat to chat. Just sit and look at how they are communicating. We have there Programmer, Automator, Planner … Lots of guys out-of-the-box. You could ask to create a Python program to combine all hour pdfs into one big file (to use it as a knowledge base, as an example) and AGENTS COLLABORATIVELY not only will write a code but also will be able to execute it right in the console → so your next message will be the PATH to your folder, where pdf to combine are. BOOM - it’s ready. SOOOOO I can tell you more and more. But better go right now to the https://aiconsole.ai/ download this UNIQE thing for FREE and try it yourself. And you could always ping me for help 😎 #businessautomation , #businessboost , #business , #ai #chatgpt #ml #gpt4 #businessinsights

Exciting news! We just launched AIConsole, a super cool product from 10Clouds AI Labs. It's a personalised #AI tool that learns your workflows and respects your privacy by operating locally on your machine. AIConsole's key features: - Keeps your data #private with local operation - Gets #smarter with time through skill retention - Rock-solid #precision with expert-level #promptengineering - We're #opensource and committed to privacy We're actively developing AIConsole to include an awesome IDE-like experience and support for open-source language models. Try AIConsole, improve your workflow, and help us shape its future. Your thoughts and feedback are super valuable!

#generativeai is the hype of the moment, and #Copilot integrates best with Microsoft’s extensive technology portfolio. This has put #coding, application use, #automation, #analytics and #cloudcomputing right at the fingertips of all users, with natural language prompts generating the desired computing output. The 10 best ways to use Copilot in Microsoft Technologies Analytics and BI Deep technical skills are no longer required for Analytics and Business Intelligence. Use natural language in Power BI to generate dashboards, perform predictive analytics and more, all aimed at novice users. Just describe the desired output and request the results – all done by Copilot. Automation Process automation is now child’s play with Power Automate. Just define the workflow requirements and see automation appear immediately. Use Copilot for a wide range of workflows in your organization. App Development Need a custom app? Copilot takes care of it for you. Say goodbye to complex coding and dependence on technical resources. It is now possible for anyone to create an app without coding skills. Chatbots Virtual assistants or chatbots are now the norm. With Copilot, you can create a chatbot based on your specifications. Your chatbot becomes essential to your business thanks to built-in data mining and natural language processing features. Website Development Forget complex web page coding. Describe your page and let Copilot create it for you. Add forms and database tables effortlessly. Modern Workplace Standard office tasks are a thing of the past. Copilot integrates into Microsoft 365 and provides advanced features such as document idea generation, email summarization and complex spreadsheet creation. Communications Teams has redefined the way we communicate. Copilot takes this experience to the next level with features such as call summaries and automatic generation of action items. Cloud Computing AI models, content moderation and more are now more accessible than ever. Work seamlessly with Azure and reap the benefits of cloud computing with Copilot. Security Cybersecurity is now easier thanks to Microsoft Security Copilot. Detect threats and identify the best strategies with the help of AI. AI Pair Programming with GitHub Copilot Coding without technical skills is now possible. Let GitHub Copilot generate your code or make suggestions using OpenAI Codex. For more information on Microsoft Copilot, contact us and our experts will be happy to help. F9 INFOTECH #analytics #automation #websitedevelopment

What if you could build machine learning models without writing a single line of code? Sounds too good to be true, right? Well, it's not! In our latest article, we explore the benefits and challenges of no-code machine learning, and how it can empower anyone to become a seasoned a machine learning engineer. #machinelearning #nocode #machinelearningengineer

Just completed my first CI/CD for Machine Learning course on DataCamp and it was an enlightening journey! Excited to leverage this new skill in AI projects and explore more opportunities in the future. #MachineLearning #ContinuousIntegration #DataCamp

🚀 The GenAI Stack Journey: Realizing Open-Source Power for Developers 🚀 In a landscape dominated by Large Language Models (LLMs), the road to adopting AI has been anything but smooth. For developers and organizations alike, the hurdles have been numerous, from navigating the maze of data privacy concerns to deciphering the complexities of embedding raw domain data into usable applications. And let's not forget the quirkiness of LLMs, often caught "hallucinating" and generating less-than-accurate content, especially in business contexts. As a core contributor I'm really proud of the team work, the effort, the late nighters that we pulled off to make this happen. 🚀 Introducing GenAI Stack: For Developers, by Developers 🚀 Today, I'm thrilled to introduce GenAI Stack, a project that's been a rollercoaster ride for our development team. Our mission was simple yet daunting: make AI accessible to developers, with all the warts and wonders it entails. GenAI Stack isn't just another tool; it's a comprehensive framework designed to seamlessly integrate LLMs into your applications. And here's the kicker - you can deploy it on your infrastructure, keeping your data under lock and key. The best part? It's now open-source, so you can dive into the nuts and bolts. 🌟Features of GenAI Stack: 🔹 ETL Simplified: Navigate data processing complexities effortlessly. 🔹 Hallucination-Free Inference: Trustworthy AI-generated content for decision-making and research. 🔹 Seamless Integration: Easy adoption, whether you're a pro or just starting. 🔹 Customization and Control: Tailor processes to your project's needs. 🌟 Applications of GenAI Stack: 🔹Enhance search engines 🔹 Quick and dynamic knowledge base Q&A 🔹 Real-time sentiment analysis 🔹 Efficient customer support chatbots 🔹Streamlined information retrieval Contributions are welcomed: https://lnkd.in/gKWPpDWQ #ai #opensource #opensourcecommunity #machinelearning #largelanguagemodels #llms #datascience

How can a highly scalable and fault-tolerant system that supports high-throughput ingestion and interactive query latencies be designed? A Meta team designed Logarithm, a logging engine for AI training workflows and services. It's a hosted, serverless, multitenant service used only internally at Meta that consumes and indexes these logs and provides an interactive query interface to retrieve and view logs. It provides strong guarantees on availability, durability, freshness, completeness, and query latency. At a high level, Logarithm comprises the following components: - 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 emit logs using logging APIs—the APIs support emitting unstructured log lines along with typed metadata key-value pairs (per line). - 𝐀 𝐡𝐨𝐬𝐭-𝐬𝐢𝐝𝐞 𝐚𝐠𝐞𝐧𝐭 discovers the format of lines and parses lines for common fields, such as timestamp, severity, process ID, and callsite. - The resulting object is buffered and written to a 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐪𝐮𝐞𝐮𝐞 (for that log stream), providing durability guarantees with days of object lifetime. - 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬 read objects from queues and support additional parsing based on user-defined regex extraction rules – the extracted key-value pairs are written in the line's metadata. - 𝐐𝐮𝐞𝐫𝐲 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬 support interactive and bulk queries on one or more log streams with predicate filters on log text and metadata. The Logarithm design has centered around simplicity for scalability guarantees. The team continuously builds domain-specific and agnostic log analytics capabilities within or layered on Logarithm with appropriate pushdowns for performance optimizations. They also invest in storage and query-time improvements, such as lightweight disaggregated inverted indices for text search, storage layouts optimized for queries, and distributed debugging UI primitives for AI systems. Complete post: https://lnkd.in/dtnTN-jb