mljet#
Subpackages#
- mljet.contrib
- mljet.cookie
- mljet.utils
- Submodules
- mljet.utils.conn module
- mljet.utils.logging_ module
RichEmojiFilteredHandler
RichEmojiFilteredHandler.HIGHLIGHTER_CLASS
RichEmojiFilteredHandler.KEYWORDS
RichEmojiFilteredHandler.acquire()
RichEmojiFilteredHandler.addFilter()
RichEmojiFilteredHandler.close()
RichEmojiFilteredHandler.createLock()
RichEmojiFilteredHandler.emit()
RichEmojiFilteredHandler.filter()
RichEmojiFilteredHandler.flush()
RichEmojiFilteredHandler.format()
RichEmojiFilteredHandler.get_level_text()
RichEmojiFilteredHandler.get_name()
RichEmojiFilteredHandler.handle()
RichEmojiFilteredHandler.handleError()
RichEmojiFilteredHandler.name
RichEmojiFilteredHandler.release()
RichEmojiFilteredHandler.removeFilter()
RichEmojiFilteredHandler.render()
RichEmojiFilteredHandler.render_message()
RichEmojiFilteredHandler.setFormatter()
RichEmojiFilteredHandler.setLevel()
RichEmojiFilteredHandler.set_name()
init()
- mljet.utils.names_generator module
- mljet.utils.requirements module
- mljet.utils.types module
- mljet.utils.utils module
- Module contents
Module contents#
MLJET - A simple Open Source mljetnt tool for ML models
If you have been working on ML models, then you have probably faced the task of deploying these models. Perhaps you are participating in a hackathon or want to show your work to management. According to our survey, more than 60% of the data-scientists surveyed faced this task and more than 60% of the respondents spent more than half an hour creating such a service.
The most common solution is to wrap it in some kind of web framework (like Flask).