From 704657cb3638e547532bd0d07ad3646b322c6c7d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 20 Aug 2025 14:21:03 +0100 Subject: [PATCH] Add content to Machine Learning (#9054) * chore: sync content to repo * Update src/data/roadmaps/machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md * Update src/data/roadmaps/machine-learning/what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md --------- Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com> Co-authored-by: Kamran Ahmed --- .../machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md | 3 +++ .../what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 src/data/roadmaps/machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md create mode 100644 src/data/roadmaps/machine-learning/what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md diff --git a/src/data/roadmaps/machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md b/src/data/roadmaps/machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md new file mode 100644 index 000000000..1a77cccb0 --- /dev/null +++ b/src/data/roadmaps/machine-learning/introduction@MEL6y3vwiqwAV6FQihF34.md @@ -0,0 +1,3 @@ +# Introduction to Machine Learning + +Machine learning is about creating computer programs that can learn from data. Instead of being explicitly programmed to perform a task, these programs improve their performance on a specific task as they are exposed to more data. This learning process allows them to make predictions or decisions without being directly told how to do so. \ No newline at end of file diff --git a/src/data/roadmaps/machine-learning/what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md b/src/data/roadmaps/machine-learning/what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md new file mode 100644 index 000000000..c397b7180 --- /dev/null +++ b/src/data/roadmaps/machine-learning/what-is-an-ml-engineer@FgzPlLUfGdlZPvPku0-Xl.md @@ -0,0 +1,3 @@ +# ML Engineer + +An ML Engineer focuses on building, deploying, and maintaining machine learning systems in production. They bridge the gap between data science and software engineering, taking models developed by data scientists and making them scalable, reliable, and efficient for real-world applications. This involves tasks like data pipeline construction, model deployment, performance monitoring, and infrastructure management. \ No newline at end of file