Managing Multiple Environments in Anaconda for Machine Learning

Managing Multiple Environments in Anaconda for Machine Learning

When we have a different Anaconda Environment, it is essential to manage them correctly to utilize when similar projects come up. In this article, I will cover some prominent points of managing Anaconda Environments. Here, I assume that you have Anaconda installed in your machine. Managing Multiple Environments in Anaconda for Machine Learning

Things look good when we organize them properly. While working on a data science project, we need to manage many tech stacks when we work on different projects. Anaconda provides us with that functionality. Anaconda comes up with the Jupyter Notebook, and we usually prefer using the Jupyter Notebook for our data science tasks. Tasks can be of the training model, preparing data, feature engineering, plotting graphs, validating models, or testing our trained model. When we work on different machine learning and deep learning model, then for specific models, we need a set of libraries to use. If we make all the libraries installed in a single Environment, we might end up with many issues.

And those issues are due to the version of the library used or any other compatibility issue. Most notably, when we migrate our model to different Environments like the cloud or different machines, it becomes harder to know what libraries we used for our task. Here comes the need for creating a different Anaconda Environment.

When we have a different Anaconda Environment, it is essential to manage them correctly to utilize when similar projects come up. In this article, I will cover some prominent points of managing Anaconda Environments. Here, I assume that you have Anaconda  installed in your machine.

machine-learning jupyter-notebook deep-learning artificial-intelligence

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