"MLOps" meaning in All languages combined

{
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    {
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        "2": "ML",
        "3": "DevOps",
        "t1": "machine learning"
      },
      "expansion": "Blend of ML (“machine learning”) + DevOps",
      "name": "blend"
    }
  ],
  "etymology_text": "Blend of ML (“machine learning”) + DevOps.",
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "MLOps (uncountable)",
      "name": "en-noun"
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  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English blends",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
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          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Artificial intelligence",
          "orig": "en:Artificial intelligence",
          "parents": [
            "Computer science",
            "Cybernetics",
            "Computing",
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            "Applied mathematics",
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      ],
      "examples": [
        {
          "ref": "2021, Hien Luu, Beginning Apache Spark 3, 2nd edition, Apress",
          "text": "The goal of MLOps is to minimize the technical friction to get the model from an idea to production in the shortest possible time with high-quality predictive power and with as little risk as possible.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently."
      ],
      "id": "en-MLOps-en-noun-c122vgqd",
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "paradigm",
          "paradigm"
        ],
        [
          "model",
          "model"
        ]
      ],
      "qualifier": "machine learning",
      "raw_glosses": [
        "(machine learning) A paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently."
      ],
      "synonyms": [
        {
          "word": "ML Ops"
        }
      ],
      "tags": [
        "uncountable"
      ]
    }
  ],
  "word": "MLOps"
}

{
  "etymology_templates": [
    {
      "args": {
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        "2": "ML",
        "3": "DevOps",
        "t1": "machine learning"
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      "expansion": "Blend of ML (“machine learning”) + DevOps",
      "name": "blend"
    }
  ],
  "etymology_text": "Blend of ML (“machine learning”) + DevOps.",
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "MLOps (uncountable)",
      "name": "en-noun"
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  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        "English blends",
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        "en:Artificial intelligence"
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      "examples": [
        {
          "ref": "2021, Hien Luu, Beginning Apache Spark 3, 2nd edition, Apress",
          "text": "The goal of MLOps is to minimize the technical friction to get the model from an idea to production in the shortest possible time with high-quality predictive power and with as little risk as possible.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently."
      ],
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "paradigm",
          "paradigm"
        ],
        [
          "model",
          "model"
        ]
      ],
      "qualifier": "machine learning",
      "raw_glosses": [
        "(machine learning) A paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently."
      ],
      "tags": [
        "uncountable"
      ]
    }
  ],
  "synonyms": [
    {
      "word": "ML Ops"
    }
  ],
  "word": "MLOps"
}