# TeaPot like thing in Julia

**URL:** <https://discourse.julialang.org/t/teapot-like-thing-in-julia/8561>\
**Category:** Machine Learning\
**Tags:** package\
**Created:** [January 24, 2018, 11:04am UTC](https://discourse.julialang.org/t/teapot-like-thing-in-julia/8561 "2018-01-24T11:04:22Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Arshpreet\_Singh](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/arshpreet_singh/32/2372_2.png) [@Arshpreet\_Singh](https://discourse.julialang.org/u/Arshpreet_Singh)\
**Post date:** [January 24, 2018, 11:04am UTC](https://discourse.julialang.org/t/teapot-like-thing-in-julia/8561/1 "2018-01-24T11:04:22Z")

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I was looking for Teapot like in Julia. Here is Teapot in Python:

> **[Dr. Randal S. Olson | TPOT: A Python tool for automating data science](https://randalolson.com/2016/05/08/tpot-a-python-tool-for-automating-data-science/)**
>
> Randy Olson demonstrates why designing machine learning pipelines is difficult, and how it can be automated using TPOT.

To summarize what we’ve learned so far about effective machine learning system design, we should:

```
Always tune the hyperparameters for our models
Always try out many different models
Always explore numerous feature representations for our data

```

We must also consider the following:

```
There are thousands of possible hyperparameter configurations for every model
There are dozens of popular machine learning models
There are dozens of popular feature preprocessing methods

```

This is why it can be so tedious to design effective machine learning systems. This is also why my collaborators and I created TPOT, an open source Python tool that intelligently automates the entire process.