SCENARIOS FOR IMPROVING ENGINEERING EMAIL

The literature review of the survey results identified three opportunities that identify many opportunities and issues and affect all issues: context, understanding, and behavior. The next section outlines each dimension. In order to effectively convert background knowledge from information to information, it is essential to establish a unified context among the parties involved in communication. One is that it is not always an incomplete process to encode knowledge into information, the second is that the reader has a difference in interpretation when decoding information.

Machine learning fundamentally changes and improves various applications. Security / threat detection, fraud detection, e-commerce recommendation engine, legal document NLP, email, patent search, video / voice / speech recognition, health check, cancer detection, fact confirmation, weather forecast, etc. In order to accelerate innovation such as financial model, Google offers Tensor Flow, a core machine learning framework as open source. Google Cloud also provides access to machine learning APIs for natural language processing, speech recognition, language translation, and visual / image recognition.

Over the past few years, we have made tremendous technological progress. The accuracy of the speech recognition engine has greatly improved, and now it has achieved 95% accuracy. This is slightly higher than human success rate. With the advance of this technology, voice-prioritized infrastructure has become more important, enabling rapid deployment of voice-preferred hardware, software building blocks, and platforms by Amazon, Apple, Google, Microsoft, Baidu. It is time to talk now. The first speech recognition system was based on simple pattern matching. A good example of these early systems is the automation system that utilities use to keep customers away from their meter reading. In this case, the customer's answer to the system is a word or number in a limited list of alternatives, and the computer only has to distinguish a limited number of different sound modes. It does this by comparing each sound block with a similar memory pattern in its memory.

Over the past two years, our platform engineering team has implemented software engineering practices to automate systems, code infrastructure, and improve the reliability of legacy systems. (Development and operation) differences have disappeared in many of the roles the team is currently executing. For the past twelve months, we have raised this question. "Where does the team called DevOps remain when adopting and following the practices generally considered by all development teams as" DevOps "? What is the actual value it offers? Do we also need it? "