Interactive Digital Tools

TPM/Lean Toolbox : 7 Tools of QC Explained Popularized by Dr. Kaoru Ishikawa, the 7 Quality Control Tools are fundamental techniques used to identify, analyze, and solve quality-related issues. These tools are simple yet highly effective for improving production processes and ensuring consistent quality: 1.Cause-and-Effect Diagrams Identifies potential causes of a problem and organizes them into categories. Helps teams brainstorm and visually map out all possible root causes of an issue. 2.Check Sheets A structured, prepared form used to collect and analyze data systematically. Tracks the frequency of specific events or defects in a process. 3.Control Charts Monitors process stability over time by plotting data points against control limits. Identifies whether a process is in control or affected by special cause variations. 4.Histograms Graphically displays the frequency distribution of data. Shows patterns or trends in data, such as variability or skewness. 5.Pareto Charts A bar graph based on the 80/20 rule, showing which factors contribute most to a problem. Prioritizes the most significant issues for resolution. 6.Scatter Diagrams Displays the relationship between two variables to identify correlations. Determines whether changes in one variable affect another. 7.Flowcharts Maps out the steps in a process to visualize workflows and identify inefficiencies. Clarifies how processes operate and highlights areas for improvement. Digitalization Digital transformation is revolutionizing quality management by integrating advanced technologies into traditional QC tools, making them smarter, faster, and more reliable. 1.Cause-and-Effect Diagrams Use digital platforms like cloud-based collaboration tools (e.g., Miro, Lucidchart) to create interactive diagrams that teams can update in real time. 2.Check Sheets Replace paper with digital forms using mobile apps (e.g., Ideagen Smartforms). Automate data collection through IoT sensors for real-time analysis. 3.Control Charts Software like SPC tools integrated with IoT devices to monitor processes in real time and generate automated alerts when control limits are predicted to be breached. 4.Histograms Data visualization tools like Tableau or Power BI to create dynamic histograms that update automatically real-time. 5.Pareto Charts Cloud analytics platforms to generate Pareto charts automatically from large datasets, highlighting key issues instantly. Machine learning algorithms to predict which factors will likely contribute most to problems. 6.Scatter Diagrams Utilize software Minitab or Python analytics to create scatter plots with regression capabilities for deeper insights into variable relationships. 7.Flowcharts Process mapping tools like Visio or BPMN software integrated with workflow automation to create digital flowcharts that reflect real-time process status. These tools provide a structured approach to problem-solving, ensuring continuous improvement and customer satisfaction.

Embracing Iterative Design with Custom Open-Source Tools in Architecture Architecture is inherently an iterative process layered with complexity. Traditionally, we relied on tracing paper to refine our sketches, adding nuances and details with each overlay. Today, open-source tools empower us to replicate and enhance this iterative approach digitally. I'm excited to share how I utilize three custom-made tools to streamline building design: Tool 1: Graph Packer Algorithm This tool allows me to define a graph with nodes and connections, effectively packing units within it. It helps visualize space allocations, understand corridor and core layouts, and ensures functional requirements are met early in the design process. Tool 2: Digital Tracing Interface By importing sketches from any source and setting them to scale, this tool lets me draw over existing images—much like layering tracing paper over a sketch. It enables precise boundary definitions and facade adjustments by manipulating room locations directly on the digital canvas. Tool 3: CSV-Based Building Generator Using a CSV file that outlines the polygon of the slab edge for the first level, this tool generates a 3D model of the building. It translates the refined 2D sketches into a tangible structure, bridging the gap between concept and realization. By integrating these tools, I can iterate faster, make more informed decisions, and enhance the overall design quality. Embracing open-source technology not only streamlines workflows but also opens up new avenues for creativity and innovation in architecture. Looking forward to hearing your thoughts and experiences with digital tools in architectural design! #Architecture #Design #IterativeDesign #OpenSource #DigitalTools #Innovation #Technology #ArchitecturalDesign #BuildingDesign #GraphAlgorithms #DigitalSketching #3DModeling #CreativeProcess #DesignInnovation #TechInArchitecture #ConstructionTech #Engineering #DesignProcess #SoftwareDevelopment #Architects

If you have an idea around ML, DL, AI, or LLMs — it's never been easier to present it through an interface others can interact with. Whether it's for internal prototyping or external demos, there are now amazing tools that help you go from idea to interactive app pretty fast. And LLMs can help get the v1 to get started in minutes. Here are some of the most widely used libraries and platforms I’ve come across (and used) for building and sharing ML/AI interfaces: Quick Prototyping & ML Interfaces ► Streamlit – Turn Python scripts into interactive web apps — fast and clean. ► Gradio – Build delightful ML apps with drag-and-drop ease, all in Python. ► AI-Gradio – Easily create AI apps powered by various providers. ► Chainlit – Build production-ready conversational AI apps in minutes. ► Voila – Turn Jupyter notebooks into standalone web apps. ► Dash (Plotly) – Great for ML dashboards and visualizations with full control. ► Shiny for Python – A reactive UI framework — originally from R, now in Python too. LLM Workflows + Agents ► LangChain + LangServe – Chain LLM calls and serve them as APIs easily. ► Flowise – Drag-n-drop builder for LLM pipelines — no code needed. ► Superagent – Build and deploy AI agents with a clean web interface. ► AutoGen Studio / OpenDevin – Open-source tools for creating autonomous AI agents with interactive UIs. Deploy, Host & Share ► Hugging Face Spaces – Host Gradio/Streamlit apps with GPUs — community ready. ► Replicate – Run and share ML models with an app-like interface. ► Modal – Serverless compute to run models, tasks, and apps with ease. What tools are you using to quickly go from idea to demo?

🖥️ Transform Data-Driven Insights into Interactive Apps with Python In the Data-Driven Engineering Course, we explore powerful tools to share analysis results effectively with Python. A highlight is Streamlit, a framework that transforms your Python scripts into interactive web apps with no front-end expertise required. 🔧 Why Streamlit? Streamlit is for: • 🖥️ Data Dashboards: Create real-time visualizations and insights. • 🤖 Machine Learning Prototypes: Build interfaces for model inputs/outputs. • 📚 Education: Develop interactive tools for teaching and collaboration. 💡 Example Features: • User Interactivity: Sliders, text inputs, and buttons make apps engaging. • Dynamic Charts: Seamlessly integrate with Matplotlib, Plotly, and more. • Data Visualization: Render tables and graphs directly from Pandas DataFrames. import streamlit as st st.title("Hello, Streamlit!") st.write("This is a simple Streamlit app.") Run the app: streamlit run app.py 🎯 Why This Matters Streamlit is an effective way to share results from your analysis in Python, enabling teams to interact with data and insights dynamically. It’s not just code—it’s storytelling with impact. 🌐 Bring your data to life with Python and Streamlit. https://lnkd.in/gNQ878x7 #DataDrivenEngineering #Python #Streamlit #InteractiveApps #EngineeringInnovation

How do we build data visualization tools for large-scale biomedical data to help scientists surface insights quickly? In my invited keynote talk at ISMB BioVis, I discussed the challenges and opportunities in creating integrated, composable, scalable, and interactive BioVis tools. At Ozette, we embrace these principles to build insightful and intelligent data visualization systems, enabling rapid identification of cellular biomarkers from large-scale single-cell data. Key takeaways from my talk: - Integrating BioVis tools into the compute and data ecosystem is essential for ensuring that insights can be gained fast. - BioVis tools should be composable as complex analyses often require multiple visualizations to explain patterns. - Scalability is essential for handling the vast amounts of data generated in biomedical research. - Bidirectional interactivity is key to creating intelligent visualizations that offer AI/ML-guidance during exploration. **In case you missed it, feel free to browse through my slides. The recording will be available on ISMB’s YouTube channel soon. Some notable software tools that make it easier than ever to build integrated, composable, scalable, and (bidirectionally) interactive software are: • Anywidget: Custom Jupyter widgets made easy. (https://anywidget.dev) • Jupyter Scatter: Explore datasets with millions of data points in Jupyter. (https://lnkd.in/e2ncaxVb) • Comparative Embedding Visualization: Compare two embeddings with shared labels. (https://lnkd.in/eUTxtWbU) • HiGlass: Explore and compare genomic contact matrices. (http://higlass.io) • Gosling: Scalable linked interactive nucleotide graphics. (https://lnkd.in/ex83JQBT) • GenomeSpy: GPU-accelerated rendering for genomic data. (https://genomespy.app) • Upset: Visualize intersecting sets effectively. (https://upset.app) • Vitessce: Explore spatial single-cell experiment data. (http://vitessce.io) • Viv: Interactive visualization of high-resolution bioimaging datasets. (https://lnkd.in/eHqprqDP) • ipylangchat: Serverless Jupyter chat UI for LangChain conversational AIs. (https://lnkd.in/e8wqpcyR) • CandyGraph: Fast 2D plotting for huge datasets. (https://lnkd.in/ekU3NvTA) • Deck.gl: GPU-powered framework for large dataset analysis. (https://deck.gl) • DataShader: Accurate rendering of the largest data. (https://datashader.org) • Mosaic: Extensible framework for scalable data visualization. (https://idl.uw.edu/mosaic/) This list is not exhaustive. If you know other great BioVis tools, please share them! Last but not least, huge shoutouts to Trevor Manz, Ashley Wilson, Nezar Abdennur, PhD, and Arpan Neupane for their feedback on my talk and help with the examples and demos. 🙏 #ISMB #BioVis #SingleCell #Visualization #DataVis #ML #Python #JavaScript #DataExploration #Jupyter #AI