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Jeff Smith covers some of the latest features from PyTorch - the TorchScript JIT compiler, distributed data parallel training, TensorBoard integration, new APIs, and more. He discusses some projects coming out of the PyTorch ecosystem like BoTorch, Ax, and PyTorch BigGraph. He digs into some of the use cases and industries where people are successfully taking PyTorch models to production.
Increased microservices adoption, fueled by the move into the cloud where architectures and infrastructure can flex and be ephemeral, adds complexity every day to the systems we create and maintain. This takes place alongside operating models with autonomous and totally empowered teams, so each distributed system has its own tapestry of technical approaches, languages, and services.
Susan Li shares various NLP feature engineering techniques from Bag-Of-Words to TF-IDF to word embedding that includes feature engineering for both ML models and emerging DL approach. She covers the details including contextual & linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution, which are based on concepts from mathematics, information retrieval, NLP.
In this podcast recorded at QCon London 2019, Shane Hastie, Lead Editor for Culture & Methods, spoke to Randy Shoup, VP of Engineering at WeWork about what is needed to create a high-performance culture.
Brendan Burns talks about the generic interface for service mesh technology. The goal of this abstraction layer is to provide an easy-to-consume API that can be implemented by many different service mesh implementations (e.g. Istio, Linkerd, etc). Users are free to adopt service mesh concepts without being bound to any particular implementation. He covers the SMI specification and implementations.
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