{
  "$type": "site.standard.document",
  "contributors": [
    {
      "did": "did:plc:igunvse2uemkwmci3igoxhu5",
      "displayName": "Oz Akan",
      "role": "author"
    }
  ],
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  "description": "A comphrensive guide to ML Development Lifecycle with best practices.",
  "path": "/techs/ml-development-lifecycle-extended",
  "publishedAt": "2025-03-02T21:00:00.000Z",
  "site": "at://did:plc:igunvse2uemkwmci3igoxhu5/site.standard.publication/luminary-blog",
  "tags": [
    "aiml"
  ],
  "textContent": "Successful machine learning projects require a structured approach that balances technical rigor with business value. This article explores each stage of the ML Development Lifecycle in detail, from defining business goals and framing ML problems to data processing, model development, deployment, monitoring, and retraining.",
  "title": "The ML Development Lifecycle and Best Practices"
}