
  <rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
    <channel>
      <title>Ashim Sharma</title>
      <link>https://ashimsharma10.github.io/blog</link>
      <description>Software engineer sharing projects, notes, and guides on ML infrastructure.</description>
      <language>en-us</language>
      <managingEditor>sharmaashim00@gmail.com (Ashim Sharma)</managingEditor>
      <webMaster>sharmaashim00@gmail.com (Ashim Sharma)</webMaster>
      <lastBuildDate>Tue, 09 Jun 2026 00:00:00 GMT</lastBuildDate>
      <atom:link href="https://ashimsharma10.github.io/tags/python/feed.xml" rel="self" type="application/rss+xml"/>
      
  <item>
    <guid>https://ashimsharma10.github.io/blog/from-raw-data-to-ml-ready-a-pandas-walkthrough</guid>
    <title>From Raw Data to ML-Ready: A Pandas Walkthrough</title>
    <link>https://ashimsharma10.github.io/blog/from-raw-data-to-ml-ready-a-pandas-walkthrough</link>
    <description>Full ML data prep lifecycle in Pandas: load, clean, impute, engineer features, encode, scale, and ship to model.</description>
    <pubDate>Tue, 09 Jun 2026 00:00:00 GMT</pubDate>
    <author>sharmaashim00@gmail.com (Ashim Sharma)</author>
    <category>pandas</category><category>data-science</category><category>feature-engineering</category><category>python</category><category>guide</category>
  </item>

  <item>
    <guid>https://ashimsharma10.github.io/blog/ml-engineer-comprehensive-technical-prep-guide</guid>
    <title>ML Engineer: Comprehensive Technical Prep Guide</title>
    <link>https://ashimsharma10.github.io/blog/ml-engineer-comprehensive-technical-prep-guide</link>
    <description>ML engineer prep: Python infra, NumPy, PyTorch, distributed training, scientific formats, MLflow, and common coding patterns.</description>
    <pubDate>Sun, 07 Jun 2026 00:00:00 GMT</pubDate>
    <author>sharmaashim00@gmail.com (Ashim Sharma)</author>
    <category>python</category><category>pytorch</category><category>numpy</category><category>distributed-training</category><category>mlops</category><category>guide</category>
  </item>

  <item>
    <guid>https://ashimsharma10.github.io/blog/mlops-tooling-from-experiment-tracking-to-production</guid>
    <title>MLOps Tooling</title>
    <link>https://ashimsharma10.github.io/blog/mlops-tooling-from-experiment-tracking-to-production</link>
    <description>A conceptual guide to MLOps tooling — MLflow, artifact versioning, reproducible pipelines, framework integrations, alternatives, and deploying and improving models in production.</description>
    <pubDate>Fri, 03 Jul 2026 00:00:00 GMT</pubDate>
    <author>sharmaashim00@gmail.com (Ashim Sharma)</author>
    <category>mlops</category><category>python</category><category>deep-learning</category><category>pytorch</category><category>infrastructure</category><category>guide</category>
  </item>

    </channel>
  </rss>
