The Microsoft Azure Machine Learning automated data pipeline cheat sheet helps you navigate through thetechnology you can use to get your data to your Machine Learning web service where it can be scored by your predictive analytics model.
Depending on whether your data is on-premises, in the cloud, or real-time streaming, there are different mechanisms available to move the data to your web service endpoint for scoring.This cheat sheet walks you through the decisions you need to make, and it offers links to articles that can help you develop your solution.
This cheat sheet is free additional material that complements DataCamp's Intro to Python for Data Science course, where you learn by doing. (Above is the printable version of this cheat sheet). We’ve collated a collection of cheat sheets for you to get to grips with the main libraries used in data science. They are grouped into the fields for which each library is designed: Basics, Databases, Data Manipulation, Data Visualization, Analysis, Machine Learning. Data Science Free; Follow @twitter; Home; Data; Demo; Cheat Sheets Tips: 1, Print using high-quality, aka business graphics printing. 2, Laminate the printed cheat sheet with 7mm thickness pouch. Data Science Resources: Cheat Sheets. Data Science Cheat Sheets Quick help to make a data scientist's life easier.
Download the Machine Learning automated data pipeline cheat sheet
Once you download the cheat sheet, you can print it in tabloid size (11 x 17 in.).
Download the cheat sheet here: Microsoft Azure Machine Learning automated data pipeline cheat sheet
More help with Machine Learning Studio
- For an overview of Microsoft Azure Machine Learning, see Introduction to machine learning on Microsoft Azure.
- For an explanation of how to deploy a scoring web service, see Deploy an Azure Machine Learning web service.
- For a discussion of how to consume a scoring web service, see How to consume an Azure Machine Learning Web service.
There are lots of cheat sheets out there of varying quality covering vastly different topics which are all considered to be under the 'data science' banner. Some are great, some are good, many are not worth your time. Occasionally a gem can be found, covering some particular niche to some acceptable level of understanding.
This is where the recent 'Data Science Cheatsheet' by Maverick Lin comes in. It is a relatively broad undertaking at a novice depth of understanding, but it does what it does very well. The 9 page treatment concisely covers such diverse aspects of data science as:
- stats & probability
- data preparation
- feature engineering
- modeling
- machine learning
- deep learning
- SQL
- ...and much more
It's worth stressing that this would not be much immediate value to seasoned veterans of data science, but beginners are encouraged to check it out, as are those brushing up for an interview or just looking for some light refresher reading.
Science Project Data Sheet
You can visit the Github repo for more information, or can download the cheat sheet from this direct download link.
Thanks to Maverick Lin for putting this cheat sheet together, which is an evolving work in progress.
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