![]() We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability. We decided between Redis and memcached because they are two of the most popular open-source cache engines. They have very user friendly documentation on their official website which we find easy to learn from. We decided to use the React-based library Victory to visualize the data. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries. We decided to use Redux to manage the state of the application since it works naturally to React. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability. These tools combined will help us learn the properties and characteristics of our data. These include NumPy, Pandas, and matplotlib. Some common Python libraries will be used to analyze our data. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. ![]() We decided to go with PyTorch for machine learning since it is one of the most popular libraries. ![]() Postman will be used for creating and testing APIs due to its convenience. Flask is easy to use and we all have experience with it. We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. It also has a lot of support due to its large user base. We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start-and speed up-distributed deep learning projects with TensorFlow: Uber has introduced Michelangelo ( ), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. ![]() It also combines high performance with an ability to tinker with low-level model details-for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. Why we built an open source, distributed training framework for TensorFlow, Keras, and PyTorch:Īt Uber, we apply deep learning across our business from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
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