Python Essentials for MLOps
You will learn a lot from this course: Python language itself,
Numpy, a few Frameworks and Cloud APIs. I can not agree more that some many skills together are needed to succeed in an MLOps role.
The course first covers basics of programming languages (like data types, functions, modules, etc), then it teaches how to write test cases using
PyTest. Parameterizing and Fixture will help save lots of time and make test cases more organized. Python debugger (PDB) is really an advanced topic, but it pays off to master how to use it. Also there are a few useful frameworks will make your life much easier: ArgParse, Click, SetupTools, Flask, and FastAPI.
Pandas is so flexible to allow you to read and write to many different formats and destinations. One of its main tasks is to perform exploratory analysis to find what is useful or potentially wrong.
DataFrame is almost the most important data structure. When it comes to filter data, I personally feel
df[df["ratings"] > 95] is hard to use, but query liek
df.query("ratings > 95") is more straight forward.
DataFrame can definitely be more capable, it is quite powerful to help manipulate text, apply functions and visualize data.
NumPy is the library that actually Pandas uses to do all of those fancy operations. The content of this course reminds me of the good old days. Long time ago when I just started learning Numpy, I was so confused by some functions which use two pair of parentheses like
np.zeros((3, 4)) to create a matrix, but some other functions use only one pair like
np.random.rand(3, 4). Of course now I am not confused any more.
Finally the course is about something big: Cloud APIs and SDKs. The course covers the Azure CLI
az, Azure ML Studio extension, Azure Open Datasets, Hugging Face Transformers and Datasets. Now you can put everything learned in this course together. Let’s build a powerful automation using existing machine learning models, or build your own machine learning APIs.
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