Mission-critical #streamingdata apps, like for stock trading or real-time #analytics, can't afford to wait for synchronous replies and risk hindering performance and the user experience. Happily enough, the asynchronous request-reply pattern enables a system to handle multiple requests concurrently, maintain high throughput, and ensure responsiveness 🙌 In this tutorial, we show you how this handy pattern works, popular use cases, and how to implement it in an async #Python app. https://lnkd.in/gjj5uqn4
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Create an operational OR-Tools decision app in minutes. Separate your operational data from your model, automate it as a decision service, and interact with it using simple API calls. Follow tutorial >> https://hubs.la/Q02r3j5H0 #decisionscience #datascience #orms #logistics #decisionops #ortools #python #java
How to run your OR-Tools model as an automated decision service
nextmv.io
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Data Scientist | Proficient in .NET, Python, SQL, FastAPI, NLP & ML algorithms | Generative AI | Langchain, OpenAI & ACS | Chatbot & LiveAgent App Developer | Docker, Kubernetes | MLOps | OpenToWork | Serving NP
Explore OmniQ Hub: A Versatile Platform Leveraging Cutting-Edge Technologies Are you ready to dive into the world of OmniQ Hub, a comprehensive platform that seamlessly integrates a myriad of cutting-edge technologies? Let's embark on a journey through the heart of this innovative repository, showcasing its diverse features and the powerful technologies it leverages. Unveiling the Technologies OmniQ Hub is a testament to the fusion of technology and creativity, employing a rich stack of tools and frameworks. Here's a glimpse of what powers this versatile platform: - Python: Driving the backend logic and scripting. - FastAPI: Fueling fast and modern APIs with Python. - .NET 8: Enabling robust frontend components. - Microsoft Blazor: Crafting interactive web UIs using C# and HTML. - C#: Powering the backend logic of frontend components. - Microsoft SQL Server: Serving as the database management system. - Azure Function with Python: Harnessing the power of serverless computing. - Docker: Containerizing the application for seamless deployment. - Azurite: Simulating Azure Storage services for testing. - Azure Search AI: Implementing intelligent search functionality. - Azure Blob Storage: Storing vast amounts of unstructured data. - Google Gemini Pro: Enhancing specific functionalities within the application. - LangChain: Enabling language-related functionalities for enhanced user experiences. Witnessing OmniQ Hub in Action Curious to see how these technologies come together to create a powerful platform? Check out this sample demo video showcasing OmniQ Hub's features and capabilities: Embrace the Future with OmniQ Hub OmniQ Hub isn't just a platform—it's a glimpse into the future of technology-driven solutions. Whether you're a developer looking to explore the latest tools or a business seeking innovative solutions, OmniQ Hub has something to offer. Ready to embark on your journey with OmniQ Hub? Clone the repository, dive into the code, and unleash the power of cutting-edge technologies today! [OmniQ Hub] (https://lnkd.in/gqcD2MuG) Stay tuned for more updates, tutorials, and insights as we continue to push the boundaries of innovation with OmniQ Hub!
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#Data #engineering in business is becoming increasingly important as we rely more on software with #API systems that do not speak to each other outside the wheelhouse of a full-stack #engineer. Even with companies spawning that are paid to tunnel information from the API, most have specific limitations; it’s more of the same problems as creating it internally; one problem most see is this becoming a significant expense over ten years because the price continues to increase! The choice of programming languages, like #Python or #nodejs, can significantly impact a company’s efficiency, scalability, and competitive edge. Python has long been a favorite in data engineering because it has a lot of ‘make it easier’ methods for #datascience, #ML, and #AI… like the Pandas DataFrames is an incredible solution within Python that is difficult to avoid… but wait, have you heard about nodejs? Currently, you can’t #Google #JavaScript without finding the nodejs.org website. The rise of Node.js prompts a shift that savvy businesses can’t afford to ignore. This article delves into why transitioning from Python to Node.js, especially in API-driven data pipelines, is a strategic move for forward-thinking companies eager to take advantage of open-source tools. DEV3LOPCOM, LLC / Canopys.io https://lnkd.in/g2GVhTMv
Embracing Node.js: Future Data Engineering for Businesses
https://dev3lop.com
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Tutorial 👉 A simple way to build a Node.js application with DataStax Astra DB (and vector search) support by using stargate-mongoose and a JSON API. via DZone #VectorSearch #JSON #Mongoose #Stargate #Developers #GenAI https://dtsx.io/3LlwpOS
Build Text and Image Search NodeJS AI App - DZone
dzone.com
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Create simple and verifiable Terminal User Interfaces (TUI) in Python effortlessly using: pip install databricks-labs-blueprint Explore more in this article: https://lnkd.in/eUaJDCHh #databrickslabs #python #tui
Trivial Terminal User Interfaces (TUI) with Databricks Labs Blueprint
medium.com
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OpenAI's Custom GPTs are an insanely fast way to create and share chat assistants for various purposes. We at Robocorp have made the best way to add more Actions to the Custom GPTs using #python. We call it the Action Server. Write a piece of code (maybe using AI code gen assistants), run one command, and it's instantly available to be used by your assistant. In the past weeks, we have released a lot of goodness that helps people build more powerful AI assistants that can interact with any system and data. 💪 Use custom Pydantic models as inputs/outputs. This is a massive improvement as LLMs seem pretty solid in generating JSON input for actions. Now, you can also receive the JSON. 😎 As a tiny detail, but in my opinion really cool: if you use Annotated and describe your data model, the Action Server automatically exposes those natural language descriptions to the assistant. Fancy! 💪 Our VS Code extension now supports developer workflows for Actions, with all the debugging features and the ability to start local Action Servers. 💪 We've radically simplified the config format to be a single file that houses all necessary things for your Actions, including the Python environment and dependencies. Note: our tooling actually builds the entire environment to your prescription, not just dependencies. It. Just. Works!
Action Server: VS Code support, new package format and pydantic models
updates.robocorp.com
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Code Intelligence Unveils CI Spark: AI-Driven Software Security Companion #AI #AIassistant #artificialintelligence #attacksurfaces #C/C++ #CISpark #CodeIntelligence #commercialprojects #CVEs #Java #JavaScript #llm #machinelearning #Software #softwaresecuritytesting #testcodegeneration #TypeScript #whiteboxtesting
Code Intelligence Unveils CI Spark: AI-Driven Software Security Companion
https://multiplatform.ai
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Want to intercept incoming requests before they are processed and outgoing responses before returning them middleware is the best choice. Princewill Inyang explained the middleware part in the FastAPI world and also implemented one to understand better in this article. #python #Programming #PythonProgramming #DataScience #MachineLearning #SoftwareDevelopment #WebDevelopment #TechNews #OpenSource https://lnkd.in/dhWsT8BH
Building Custom Middleware in FastAPI - Semaphore
https://semaphoreci.com
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Streamline your Fast API development with dependency injection! ⚡️ Building robust and scalable APIs can be a challenge. Fast API offers a powerful framework, but managing dependencies can quickly become complex. In this post, I'll delve into the world of Fast API dependency injection, exploring how it can: ⭐ Simplify dependency management ⭐ Improve code organization ⭐ Enhance testability https://lnkd.in/gShFpcF9 #FastAPI #dependencyinjection #python #programming #APIdevelopment
Building Scalable and Maintainable FastAPI Applications: Part-2 Dependancy Injection
medium.com
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REST API Vs. GraphQL When it comes to API design, REST and GraphQL each have their own strengths and weaknesses. REST - Uses standard HTTP methods like GET, POST, PUT, DELETE for CRUD operations. - Works well when you need simple, uniform interfaces between separate services/applications. - Caching strategies are straightforward to implement. - The downside is it may require multiple roundtrips to assemble related data from separate endpoints. GraphQL - Provides a single endpoint for clients to query for precisely the data they need. - Clients specify the exact fields required in nested queries, and the server returns optimized payloads containing just those fields. - Supports Mutations for modifying data and Subscriptions for real-time notifications. - Great for aggregating data from multiple sources and works well with rapidly evolving frontend requirements. - However, it shifts complexity to the client side and can allow abusive queries if not properly safeguarded - Caching strategies can be more complicated than REST. The best choice between REST and GraphQL depends on the specific requirements of the application and development team. GraphQL is a good fit for complex or frequently changing frontend needs, while REST suits applications where simple and consistent contracts are preferred. Source : Alex Xu Follow Brain Community for more #DataScience #LearningResources #DataAnalysis #Python #MachineLearning #LinkedInLearning #free #like
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