Meet MutableAI; A Machine Learning Powered Python Code Assistant for Jupyter

Developing and deploying machine learning (ML) models takes a lot of time because ML pipelines involve many different job functions. As a result, it is crucial to streamline operations whenever possible.

With the growth of AI research, the field of natural language processing (NLP) has seen encouraging progress. NLP tools are used to perform a wide range of tasks, such as tokenization, and syntactic and semantic analysis, to name a few. 

A new collaboration between Orchest and MutableAI presents a coding assistant tool that allows programmers to significantly reduce the time required to produce high-quality production code in all languages using AI.

MutableAI team firmly believes that AI-accelerated software development is the way of the future. They began by leveraging AI and metaprogramming to transform low-quality Python prototype code into high-quality production code in accordance with their beliefs.

Currently, MutableAI’s primary features are as follows:

  1. Autocomplete: The tool uses all the previously written code to determine the overall structure of the activity users are performing, which has likely been done numerous times before. This allows the system to produce an accurate auto-completion in much less time by intelligently identifying the underlying patterns and factoring in users’ unique context (variable names, file structure, and scope).
  2. Open-ended transforms: MutableAI provides domain-specific transforms that seamlessly comprehend the user’s code and implement the necessary modifications in response to a high-level directive.
  3. Produce code: After the initial iteration of codes and getting the desired outcome, users may want to clean up to make the code easier to read. To follow import rules and make it simple to identify at a glance which modules this notebook depends on, users might, for instance, arrange all imports at the top. 
  4. Type Annotations: Incremental type annotation, or just adding some types, is supported by many modern programming languages. Recent developments in pattern recognition and NLP approaches have enabled machines to analyze code and add types in a dynamic language context.   

The open-ended inquiry dialogue is one of MutableAI’s most intriguing features. It creates the prospect of increasingly ambitious and intricate inquiries. The team believes that these tools are only touching the surface of what is conceivable because of growing NLP advancements. This will soon make things like telling IDE, “Improve iteration performance by employing additional caching,” possible.


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Tanushree Shenwai is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Bhubaneswar. She is a Data Science enthusiast and has a keen interest in the scope of application of artificial intelligence in various fields. She is passionate about exploring the new advancements in technologies and their real-life application.