The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

Artificial intelligence(AI) has been an atypical technology trend. In a traditional technology cycle, innovation typically begins with startups trying to disrupt industry incumbents. In the case of AI, most of the innovation in the space has been coming from the big corporate labs of companies like Google, Facebook, Uber or Microsoft. Those companies are not only leading impressive tracks of research but also regularly open sourcing new frameworks and tools that streamline the adoption of AI technologies. In that context, Uber has emerged as one of the most active contributors to open source AI technologies in the current ecosystems.

The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

Uber continues its spree of deep learning technology releases. Since last year, the Uber AI Labs team has open sourced different frameworks that enable many of the fundamental building blocks of deep learning solutions. The productivity of the Uber engineering team is nothing short of impressive: Pyro is a framework for probabilistic programming built on top of PyTorch, Horovod is a Tensor-Flow based framework for distributed learning, Manifold focused on visual debugging and interpretability and, of course, Michelangelo is a reference architecture for large scale machine learning solutions. The latest creation of Uber AI Labs is Ludwig, a toolbox for training deep learning models without writing any code. Training is one of the most developer intensive aspects of deep learning applications.