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The story of Uber's data-driven ascent to the top of the ride-hailing industry has been fascinating! Learn how this tech giant not only revolutionized ride-hailing but also created a product such as UberEats, showcasing the true impact of data-driven growth. 🍔🚗 Explore the intersection of innovation and efficiency in this week's captivating blog. Read Manaal Shuja's thoughts in the blog. Contact us for a free consultation about your business challenge: https://bit.ly/4aLKUpb #Uber #DataScience #TechInnovation #DataPilot

Discover how Uber made experiment evaluations 100x faster! We’ve drastically reduced experiment evaluation latencies from milliseconds to microseconds by shifting from a remote to a local evaluation architecture. This improvement supercharges A/B testing across Uber’s backend systems. Read more here: https://lnkd.in/gqAWeA_M #UberTech #ABTesting #Experimentation #TechInnovation

Below is a nice article which explains how Uber took a seemingly impossible technical problem and solved it by breaking it down into smaller and more manageable problems, simplifying those problems through computer science fundamentals and innovation. https://lnkd.in/dKvZXU3e

How Uber Gets Your Ride ETA Spot On (It's Not Magic!) Ever wonder how Uber predicts your ride's arrival time with such accuracy? It's not a guessing game - it's teamwork between brains and machines! This post explains Uber's secret weapon: a hybrid approach combining: 📌 Routing Engine: Imagine a super map whiz predicting the base travel time based on distance and roads. 📌 Machine Learning: Then, a special program analyzes real-world data like traffic patterns to identify potential delays or smooth sailing. Here's how it works: ▶️ The Machine Learning model predicts the difference between the routing engine's guess and the actual travel time. Think of it as fine-tuning the prediction. ▶️ The final ETA is the sum of the routing engine's prediction and the machine learning's adjustments. It's like adding the base travel time to the potential delays or shortcuts identified by the machine. This clever combo helps Uber give you a more realistic and accurate picture of when your ride will arrive, making your trip planning a breeze. Learn more about deep learning; https://hubs.la/Q02sXGly0 #Uber #MachineLearning #deeplearning

System Design: Uber we often encounter the challenge of designing systems that need to scale, be fault-tolerant, and provide seamless user experiences. One of the most fascinating examples of this is Uber. Key Considerations in Uber's System Design: 1. Scalability: Handling millions of concurrent users worldwide requires a robust architecture. Uber leverages microservices, allowing independent scaling of components like ride-matching, payment processing, and user notifications. 2. Real-Time Processing: Matching drivers with riders in real-time involves complex algorithms and efficient data processing. Uber uses geospatial indexing and real-time analytics to ensure fast and accurate matches. 3. Fault Tolerance: Ensuring reliability in a globally distributed system is critical. Uber employs strategies such as data replication, fallback mechanisms, and distributed databases to maintain high availability. 4. Data Storage and Management: Handling vast amounts of data from ride details, user information, and transactional records is a significant task. Uber uses a mix of SQL and NoSQL databases to optimize for both transactional and analytical workloads. 5. Security and Compliance: With sensitive user data and financial transactions, security is paramount. Uber implements stringent security measures and complies with global data protection regulations to safeguard user information. 6. User Experience: From booking a ride to payment, the user journey must be seamless. Uber’s system ensures low latency, intuitive interface design, and reliable communication channels between drivers and riders. Uber’s system design is a testament to the power of combining innovative technologies with thoughtful architectural choices. It’s a compelling case study for anyone interested in the complexities of building large-scale, real-time systems. #SystemDesign #Uber #Microservices #RealTimeProcessing #TechInnovation #Scalability #FaultTolerance #DataManagement #Security

I’m excited to share insights from a recent talk that perfectly aligns with the principles we’ve been exploring in the “Managing Product Platforms: Delivering Variety and Realizing Synergies” course at MIT Sloan. The speaker delved into strategies for enhancing developer productivity and experience through standardization, modularity, and continuous innovation. Key takeaways include: • Standardization of Tools and Frameworks: By adopting monorepos and unified frameworks across platforms, development becomes more efficient and manageable. This mirrors our course discussions on how standardization can reduce complexity and facilitate scalability. • Measurement and Continuous Improvement: Implementing both quantitative and qualitative metrics to assess developer satisfaction and productivity allows for informed decisions and ongoing enhancements—echoing the course’s emphasis on feedback mechanisms. • Integration of AI for Developer Tools: Leveraging AI for automated debugging, testing, and code suggestions can significantly improve efficiency and reduce manual effort. This showcases how embracing new technologies drives innovation within a platform strategy. This real-world example underscores the power of platform thinking and modular design in delivering a variety of products while realizing operational synergies. It highlights the tangible benefits of aligning platform strategies with business objectives, balancing the need for variety with the efficiencies of standardization. I’m inspired to consider how we can apply these insights to our own work. By focusing on enhancing our developer experience through these principles, we can foster innovation, respond swiftly to market changes, and realize greater synergies in our projects. https://lnkd.in/dxjXwPtQ

🚖 Exciting Project Update: Uber Fare Prediction with Streamlit 🌟 I’m thrilled to share my latest project where I combined machine learning with web development to predict Uber ride fares! 🎉 Project Highlights: 🔍 Machine Learning Model: Developed a predictive model for Uber fares using historical data. 🌐 Streamlit Web Application: Created an intuitive app that allows users to input ride details and get real-time fare estimates. Key Skills Acquired: - Data Cleaning & Preprocessing - Feature Engineering - Regression Modeling & Hyperparameter Tuning - Web App Development with Streamlit - Cloud Deployment on AWS Business Impact: - Accurate Fare Estimates: Helps users plan their rides better. - Dynamic Pricing: Adjusts estimates based on demand and time. - Optimized Fleet Management: Predicts high-demand areas and times. Check out the live application and see the magic yourself : https://lnkd.in/gtDXkU_y #DataScience #MachineLearning #Streamlit #WebDevelopment #AWS #UberFarePrediction #Python #Guvi #zenclass

Both AI and the negative impact on the environment have already been themes at #LeadDevBerlin … and now we have Sarah Hsu talking about #greenops #greensoftwaredevelopment Starts with equating #devops movement with Ford’s assembly lines in the early days of car production. How can stretched engineering teams integrate sustainability into their workflow? “Overprovisioning in the cloud is the equivalent of buying a monster truck when a scooter would do? 🛻 🛵” #FinOps: - inform - optimize - operate #GreenOps: “injecting environmental responsibility into the very heart of how we develop software” - measure & monitor resource consumption - auto-scale and dynamically manage resources - utilise microservices or serverless architectures “In tech we are obsessed with the shiny new thing, but we also need to safeguard our planet”

Wonder why after those AI tech demos, feels like you're getting ghosted? You've spent years crafting the perfect product. Every line of code, every feature is a spot on. You're bursting with excitement to share it with the world! But when you do... crickets. Sound familiar? For AI to gain adoption, you need to know how to put this all in simple terms the customer understands. Creating features excites engineers. But just like feature creep hurts software dev, feature frenzy on a demo overwhelms people who might be customers. Listen to Jonathan Khorsandi, who works with startups and growing tech business in Europe, Asia, and the US. Common sense frameworks to help you improve, whether you're in AI, SAAS, or services.