Merge branch 'main' of https://github.com/SkafteNicki/dtu_mlops

README.md

@@ -96,7 +96,7 @@ The lifecycle of production ML can largely be divided into three phases:
     our model is generalizing well.
 
 3. Operations: Based on the model development phase, we now have a model that we want to use. The operations are where
-    create an automatic pipeline that makes sure that whenever we make changes to our codebase they get automatically
+    we create an automatic pipeline that makes sure that whenever we make changes to our codebase they get automatically
     incorporated into our model, such that we do not slow down production. Equally important is the ongoing monitoring
     of already deployed models to make sure that they behave exactly as we specified them.
 
@@ -114,7 +114,7 @@ production setting.
 
 General course objective
 
-> Introduce the student to a number of coding practices that will help them organization, scale,
+> Introduce the student to a number of coding practices that will help them organize, scale,
 > monitor and deploy machine learning models either in a research or production setting. To provide
 > hands-on experience with a number of frameworks, both local and in the cloud, for doing large
 > scale machine learning models.

mkdocs.yml

@@ -105,7 +105,7 @@ nav:
     - M7 - Good coding practice: s2_organisation_and_version_control/good_coding_practice.md
     - M8 - Data version control: s2_organisation_and_version_control/dvc.md
     - M9 - Command Line Interfaces: s2_organisation_and_version_control/cli.md
-  - S3 - Reproduceability ♻️:
+  - S3 - Reproducibility ♻️:
     - s3_reproducibility/README.md
     - M10 - Docker: s3_reproducibility/docker.md
     - M11 - Config Files: s3_reproducibility/config_files.md

s2_organisation_and_version_control/cli.md

@@ -113,7 +113,7 @@ argument here is to give users of our code a single entrypoint to interact with
 multiple scripts. As long as all subcommands are proper documented, then our interface should be simple to interact
 with (again think `git` where each subcommand can be given the `-h` arg to get specific help).
 
-Instead of using `argparse` we are here going to look at the [yyper](https://typer.tiangolo.com/) package. `typer`
+Instead of using `argparse` we are here going to look at the [typer](https://typer.tiangolo.com/) package. `typer`
 extends the functionalities of `argparse` to allow for easy definition of subcommands and many other things, which we
 are not going to touch upon in this module. For completeness we should also mention that `typer` is not the only package
 for doing this, and of other excellent frameworks for creating command line interfaces easily we can mention
@@ -321,7 +321,7 @@ easier.
     returns the current Python interpreter but it works for all operating systems. Check that it works by running:
 
     ```bash
-    invoke hello
+    invoke python
     ```
 
 4. Lets try to create a task that simplifies the process of `git add`, `git commit`, `git push`. Create a task such

s2_organisation_and_version_control/code_structure.md

@@ -109,7 +109,7 @@ a lot of projects using `setup.py + setup.cfg`, so it is good to at least know a
     ```
 
     the `[build-system]` informs `pip`/`python` that to build this Python project it needs the two packages
-    `setuptools` and `wheels` and that it should call the
+    `setuptools` and `wheel` and that it should call the
     [setuptools.build_meta](https://setuptools.pypa.io/en/latest/build_meta.html) function to actually build the
     project. The `[project]` section essentially contains metadata regarding the package, what its called etc. if we
     ever want to publish it to [PyPI](https://pypi.org/).