#13 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 5] | Summary and Q&A

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April 20, 2022
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#13 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 5]

TL;DR

This video provides tips for getting started on a machine learning project, including conducting a literature search, considering deployment constraints, and running sanity checks.

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Key Insights

  • 🎰 Machine learning projects involve an iterative process that includes model development, data analysis, error analysis, and improvement.
  • 💝 Prioritizing practicality and efficiency over the latest algorithms and focusing on good data can yield better results.
  • 🥅 Consideration of deployment constraints depends on the project's stage and goals.

Transcript

let me share with you a few tips for getting started on the machine learning project this video will be a little bit of a grab bag of different ideas but i hope nonetheless many of these ideas will be useful to you we've talked about how machine learning is an iterative process where you start with a model data hyper parameters create a model carry... Read More

Questions & Answers

Q: What is the first step when starting a machine learning project?

The first step is to conduct a literature search to explore different algorithms and find a reasonable one to get started quickly. Open source implementations can also be helpful.

Q: Should deployment constraints, such as compute limitations, be considered when picking a model?

If the project is at the stage of establishing a baseline or determining feasibility, it is not necessary to consider deployment constraints. However, if the baseline is already established and deployment is the goal, these constraints should be taken into account.

Q: What are some sanity checks recommended before running the learning algorithm on all data?

Running a few quick sanity checks can help identify code and algorithm issues. These checks may include overfitting a small training dataset or testing the algorithm on one or a few training examples.

Q: Is training a machine learning algorithm on a small subset of data worthwhile for image classification problems?

Yes, training the algorithm on a small subset of images, even if the overall dataset is large, can serve as a useful sanity check. If the algorithm performs poorly on a small subset, it is unlikely to perform well on the full dataset.

Summary & Key Takeaways

  • Machine learning projects follow an iterative process of model development, data analysis, error analysis, and improvement.

  • It is recommended to start with a literature search to explore different possibilities and select a reasonable algorithm to quickly get started.

  • Considering deployment constraints, such as compute limitations, depends on the project's stage, with baseline establishment being a crucial factor.

  • Running sanity checks, like overfitting a small training dataset or fitting one training example, can help identify code and algorithm issues early on.

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