Trino Software Foundation’s Post

Astronomer unveils New Capabilities in Astro Know more:-https://lnkd.in/dscVKdCq #aitechparknews #aitechnology #aidevelopment #artificialintelligence #technology #innovation #aitech #machinelearning #python #deeplearning #aitechparknews      

🔍 Exploring Parallelized Pairwise Sorting for Big Data 🔍 In today’s data-driven world, efficiently processing large datasets is crucial. One powerful technique to achieve this is **parallelized pairwise sorting**. Parallelized pairwise sorting leverages the power of multiple processors to split a large dataset into smaller chunks, sort each chunk concurrently, and then merge the sorted chunks. This method can significantly reduce sorting time, making it ideal for big data applications. 🔥 How Does It Work? 1. Divide the Data: Split the dataset into smaller, manageable chunks. 2. Sort Chunks in Parallel: Utilize parallel processing to sort each chunk simultaneously. 3. Merge Sorted Chunks: Combine the sorted chunks to produce a fully sorted dataset. 🔥 Why Use Parallelized Pairwise Sorting? - Speed: Harnessing multiple processors accelerates the sorting process. - Scalability: Efficiently handles large datasets by distributing the load. - Resource Utilization: Makes optimal use of available computing resources. 🔥 Key Takeaways - Efficiency: Reduces sorting time for large datasets. - Scalability: Adaptable to various sizes of data. - Optimization: Enhances resource utilization in multi-core systems. Adopting parallelized sorting techniques can revolutionize how we handle big data, driving faster and more efficient data processing. #BigData #ParallelProcessing #DataScience #Python #MachineLearning #DataEngineering

🚀 Unlock the Power of Data Visualization with Seaborn! 📊 In today's data-driven world, the ability to effectively communicate insights is paramount. That's where Seaborn steps in, offering a robust toolkit that goes beyond basic plotting to deliver compelling, informative visualizations. 💡 Why Seaborn? 🔹 Statistical Sophistication: Dive deep into your data with Seaborn's rich array of statistical plots. From histograms and box plots to violin plots and pair plots, Seaborn provides a comprehensive suite of tools to explore distributions, relationships, and patterns. 🔹 Seamless Integration: Seamlessly integrate Seaborn with your existing data pipelines. Whether you're working with Pandas dataframes or NumPy arrays, Seaborn's interoperability ensures a smooth transition from data wrangling to visualization. 🔹 Aesthetics & Customization: Impress your audience with stunning visualizations that captivate and inform. With Seaborn, you have full control over every aspect of your plots, from colors and styles to annotations and axes formatting. 🔹 Complex Visualization Made Easy: Tackle even the most complex visualization challenges with confidence. Seaborn's intuitive syntax and powerful abstraction layer simplify the creation of multi-panel figures, heatmaps, and more. 🔹 Community & Support: Join a vibrant community of data enthusiasts and practitioners. Whether you're seeking advice, sharing your latest creations, or diving into the source code, the Seaborn community is here to support you every step of the way. Ready to elevate your data visualization game? Whether you're exploring trends, analyzing experiments, or telling stories with data, Seaborn is your trusted companion for turning raw numbers into compelling narratives. 🔍💼 Join the thousands of data professionals worldwide who rely on Seaborn to transform their data into actionable insights. Start your journey today! 💻📈 #DataVisualization #Seaborn #Python #DataScience #Analytics #VisualInsights #DataStorytelling

We did a thing at Prefect.. Excited to share the open-source preview of Prefect 3.0 👇 This new framework is designed to make data workflows resilient by default, with transactional semantics, flexible orchestration, and portability across any infrastructure. Billy Mays ▶ "but wait, there's more"... Introducing ControlFlow, a Python framework for building agentic AI workflows. ControlFlow provides a structured, developer-focused framework for defining workflows and delegating work to LLMs, without sacrificing control or transparency. Link to docs: https://lnkd.in/gaS7q6rZ #opensource #python #data #ai #llm

🌟 Exciting Update: Datasynth is now fuxion! 🌟 Hey everyone! I’m thrilled to share that Datasynth has been renamed to fuxion. This change marks a significant leap forward in our journey, enhancing our library’s capabilities for synthetic data generation and normalization. 🔹 What’s New? With the new name fuxion, we’re not just rebranding but also expanding the toolkit to better serve our users’ evolving needs. 🔹 Get Started in Seconds: You can now set up fuxion with a `quick pip install fuxion`. We’ve made it super easy for anyone to dive in and start using our tools right away. 🔹 We Value Your Input: Your feedback has been instrumental in this transformation. Thanks to your insights, we’re continually improving how our tools work. I’m really excited about this new chapter and can’t wait for you all to try it out. Check out the updates and let me know what you think! Let’s keep pushing the boundaries of opensource machine learning together. You can find all the details on GitHub: https://lnkd.in/dKKAhSNr #DataScience #Python #OpenSource #MachineLearning #Fuxion #TechCommunity

Recommender engines are becoming a popular application of machine learning and AI. Collaborative Filtering is what drives them. If you're wondering how they work, this simple explanation has got you covered! Python takes care of the stats. #uncaibootcamp https://lnkd.in/eGP7S7hA

BentoML stands as a #python library streamlining the deployment process of machine learning models. Notably, it shares considerable similarities with #MLflow. Both frameworks articulate a model packaging structure. BentoML employs a format labeled ".bento," comprising a zipped folder encompassing: ➡ artifacts ➡ pre-/post-processing and serving code ➡ code dependencies Both BentoML and MLflow provide a specified method for encapsulating the model into a Docker Image, facilitating subsequent use in production. The key disparity lies in performance during the serving phase! ➡ MLflow utilizes Flask, adhering to WSGI standards, albeit with limitations for large-scale production use cases. ➡ BentoML, akin to FastAPI, adheres to ASGI and boasts enhanced performance, attributed to its "adaptive batching" capability. Exploring BentoML is undoubtedly a worthwhile endeavor. https://lnkd.in/grE3d7Vy

The Neo4j Meetup is happening tomorrow at 6:00 PM GMT! 🎉 The event will kick off with some networking, followed by presentations, starting with our CTO, Sebastian Müller. He will show how to create interactive graph visualizations from Neo4j databases using #JupyterNotebooks and free tools, followed by a brief Q&A. 🗓️ Date: 12th December 2024 ⏰ Time: 6:00 PM GMT Get more details: https://lnkd.in/enErya6w #Neo4j #GraphDatabase #DataVisualization #GraphTechnology #Jupyter #Notebooks #DataScience #TechCommunity #GraphAnalytics #BigData #InteractiveData #Python

Hello LinkedIn Community! 👋 🚀 Day 72 to Day74 of my Data Science journey ⏳ Just wrapped up a dive into Seaborn, and I'm excited to share what I've learned in just 3 days! 📊✨ **Why Seaborn?** - **Layer of Abstraction**: Simplifies the process of creating complex visualizations by handling many of the details automatically. 🛠️ - **Better Aesthetics**: Provides beautiful default styles and color palettes that make your visualizations look professional with minimal effort. 🎨 - **More Graphs Included**: Offers a wide variety of built-in plot types to meet diverse visualization needs. 📈 **Types of Functions** 1. **Figure Level** 📐: Functions that operate on an entire figure, facilitating the creation of complex visualizations with multiple plots. 2. **Axis Level** 📏: Functions that operate on individual axes within a figure, allowing for more granular control over specific plots. **Main Classifications** 1. **Relational Plot** 🤝   - **`scatterplot`** 📍: Visualizes the relationship between two numerical variables with individual data points.   - **`lineplot`** 📉: Shows the relationship between two numerical variables with a continuous line, ideal for time series data. 2. **Distribution Plot** 📊   - **`histogram`** 📊: Displays the distribution of a single numerical variable by binning data into intervals.   - **`kdeplot`** 📈: Uses kernel density estimation to visualize the probability density of a continuous variable.   - **`rugplot`** 🧵: Adds small vertical lines at each data point along the x-axis to provide a visual representation of data density. 3. **Matrix Plot** 🗺️ **`heatmap`** 🔥: Displays data in a matrix format with color coding to represent different values, useful for correlation matrices.   - **`clustermap`** 🌐: Similar to a heatmap but also performs hierarchical clustering to group similar data points together. Rest 3 more topics are left: i. Categoriacal Plots ii. Reg Plots iii. Multi Plots Loving how powerful and user-friendly Seaborn is for data visualization! #seaborn #python #datavisualization #EDA #datascience #keepmoving #linkedcommunity

Comprehensive Guide to Building Production-Level ML and Data Pipelines with Kedro https://lnkd.in/dhn3yXAT