> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-7-natural-language-processing-part-ii.md).
# Week 8 - Efficient Training Pipelines
### This week you will...
* use reproducible and scalable training workflows.
* learn to use profiling and monitoring tools to diagnose training bottlenecks.
* get to know techniques to implement efficient data loading and preprocessing workflows in PyTorch.
### Learning Resources
{% file src="/files/mkL2NpHLt7GZ5tguph2A" %}
* Hugging Face LLM course with [chapter 5](https://huggingface.co/learn/llm-course/chapter5/1) on the Datasets library
* Thorough [introduction video to TensorBoard](https://www.youtube.com/watch?v=VJW9wU-1n18\&t=1226s) (25 minutes)
### Until next week you should...
* focus on your project work.
* watch the [videos of Module 3](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/2ffyly/transformers) ("Specialized Approaches to Natural Language Processing in Pytorch") of course 3 ("PyTorch: Advanced Architectures and Deployment")
* start with the assignment on using transformers for translation [here](https://colab.research.google.com/github/opencampus-sh/course-material/blob/main/applied-machine-learning/week-07/Week7-Translation-Assignment.ipynb).
{% hint style="warning" %}
**complete the second milestone, that is the definition of an *****evaluation metric and the estimation of a baseline model*****, on Sunday before the feedback session next week!**\
\
**Follow the instructions given in the template repository. We will review your completions via the link to your repository provided in the Google Sheet including the current list of projects.**
{% endhint %}
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-7-natural-language-processing-part-ii.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.