Azure Search vs TensorFlow: What are the differences?
Azure Search: Search-as-a-service for web and mobile app development. Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios; TensorFlow: Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Azure Search belongs to "Search as a Service" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".
"Easy to set up" is the primary reason why developers consider Azure Search over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.
What is Azure Search?
What is TensorFlow?
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At 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.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. 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. 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.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. 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:
(Direct GitHub repo: https://github.com/uber/horovod)
Machine Learning in EECS 445