From React Native to Flutter

Reflectly—From React Native toFlutterWhy we moved 500.000+ users toFlutterThe EarlyDaysReflectly was built using React Native in the summer of 2017. At the time React Native was a relatively new and exciting technology. It promised high productivity and cross-platform mobile development with familiar web technologies. Our team had a strong background in web technology and therefore felt immediately at home using React Native to build Reflectly. Following a few intense weeks of development, we submitted the first version of Reflectly for iOS to the App Store.
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AWS On-Demand Capacity Reservation

AWS EC2 On-demand capacity reservation is another new feature has been announced recently. This feature mainly focusing the customers who want to use a specific instance type on a particular availability zone for a long or short period. We just tried it and wanted to explain about how to use this properly. If you are running any massive batch workload on a particular time frame with high-end instance type, then you can think about this feature.
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An end to end implementation of a Machine Learning pipeline

As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relevant to the field. For example – a 15 minute tutorial on Tensorflow using MNIST dataset, or a 10 minute intro to Deep Learning in Keras on Imagenet. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn’t help them at all in using ML for their own purposes.
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What’s the Best Deep Learning Framework?

Deep learning models are large and complex, so instead of writing out every function from the ground up, programmers rely on frameworks and software libraries to build neural networks efficiently. The top deep learning frameworks provide highly optimized, GPU-enabled code that are specific to deep neural network computations. Source: nvidia.com

Curiosity and Procrastination in Reinforcement Learning

Episodic Curiosity through Reachability: Observations are added to memory, reward is computed based on how far the current observation is from the most similar observation in memory. The agent receives more reward for seeing observations which are not yet represented in memory. Source: googleblog.com

Serverless

Running serverless applications on Kubernetes is quite new. Most of the options available are still under major development. You can find the ever growing number of FaaS platforms that run on top of Kubernetes in our FaaS category. At lot of the value proposition people are seeing from using AWS Lambda, to quickly string together batch operations, don’t transfer well to Kubernetes. It is currently quicker and easier to simply use containers for this scenario.
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Preview 7 Open Source Projects from the Uber Open Summit

Open source software pervades the work we do at Uber. On the infrastructure side, we have contributed projects like Jaeger, which lets engineers trace complex architectures, and M3, a metrics platform that works with Prometheus. For front-end development, we built RIBs, a cross-platform architecture for mobile apps, along with Fusion.js, a plugin-based web framework. In the rapidly advancing area of machine learning, we have open source tools such as Horovod, a distributed training framework, and Pyro, a deep probabilistic programming language written in Python.
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Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML models to solve Uber’s most challenging problems (e.g., NLP based smart reply systems, ticket assistance systems, fraud detection, and financial and marketplace forecasting). Once a model type is empirically validated to be best for the task, engineers work closely with data science teams to productionize and make it available for low latency serving at Uber-scale.
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Scaling Kubernetes to 2,500 Nodes

We’ve been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we’ve pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs.
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Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps

High quality map data powers many aspects of the Uber trip experience. Services such as Search, Routing, and Estimated Time of Arrival (ETA) prediction rely on accurate map data to provide a safe, convenient, and efficient experience for riders, drivers, eaters, and delivery-partners. However, map data can become stale over time, reducing its quality. As a customer-obsessed company, Uber reviews and addresses feedback in customer support tickets, which are submitted by riders, driver-partners, eaters, and delivery-partners on the Uber platform.
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