{"product_id":"mlops-machine-learning-operations","title":"MLOps (Machine Learning Operations)","description":"\u003ch2 class=\"dt-heading-xl\"\u003eBridging the Gap: Master the Lifecycle of Production-Grade AI with MLOps\u003c\/h2\u003e\n\u003cdiv class=\"dt-body-premium\"\u003e\n    The \"MLOps (Machine Learning Operations)\" program is an elite technical track designed to solve the \"last mile\" problem in artificial intelligence. While many can build a model, few can deploy, monitor, and scale one reliably in a production environment. Powered by Skillsoft, this course provides the engineering framework necessary to transition from experimental notebooks to automated, self-healing pipelines. You will master the 2026 standards for CI\/CD\/CT (Continuous Integration, Deployment, and Training), data versioning, and model observability. By bridging the gap between Data Science and DevOps, this training ensures your models remain accurate, compliant, and performant long after the initial deployment.\n\u003c\/div\u003e\n\n\u003cdiv class=\"dt-grid-v7\"\u003e\n    \u003cdiv class=\"dt-glass-panel-v7\"\u003e\n        \u003ch3 class=\"dt-heading-card\"\u003eWho is this for?\u003c\/h3\u003e\n        \u003cul class=\"dt-list-premium\"\u003e\n            \u003cli\u003e\n\u003cstrong\u003eData Scientists:\u003c\/strong\u003e Looking to move beyond experimental modeling to understand how their code lives in production.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eDevOps Engineers:\u003c\/strong\u003e Professionals aiming to specialize in the unique requirements of machine learning infrastructure and hardware acceleration.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eMachine Learning Engineers:\u003c\/strong\u003e Individuals focused on building robust, automated pipelines for model retraining and deployment.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eSoftware Architects:\u003c\/strong\u003e Technical leads designing the structural foundation for AI-integrated enterprise applications.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eIT Operations Managers:\u003c\/strong\u003e Leaders tasked with managing the cost, compliance, and reliability of organizational AI assets.\u003c\/li\u003e\n        \u003c\/ul\u003e\n    \u003c\/div\u003e\n    \u003cdiv class=\"dt-glass-panel-v7\"\u003e\n        \u003ch3 class=\"dt-heading-card\"\u003eReady for roles like\u003c\/h3\u003e\n        \u003cul class=\"dt-list-premium\"\u003e\n            \u003cli\u003e\n\u003cstrong\u003eMLOps Engineer:\u003c\/strong\u003e Automating the end-to-end lifecycle of machine learning models and ensuring pipeline stability.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eAI Infrastructure Architect:\u003c\/strong\u003e Designing scalable cloud and hybrid environments for model training and inference.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eLLMOps Specialist:\u003c\/strong\u003e Managing the unique challenges of Large Language Models, including fine-tuning pipelines and RAG orchestration.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eModel Reliability Engineer:\u003c\/strong\u003e Monitoring model health, detecting drift, and implementing automated retraining triggers.\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eData Engineer (ML Focus):\u003c\/strong\u003e Building the feature stores and data pipelines that feed high-performance models.\u003c\/li\u003e\n        \u003c\/ul\u003e\n    \u003c\/div\u003e\n\u003c\/div\u003e\n\n\u003ch3 class=\"dt-heading-section\"\u003eCourse Curriculum\u003c\/h3\u003e\n\n\u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eModule 1: The MLOps Framework \u0026amp; Principles \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n        Understand why ML is different from traditional software. Explore the \"Hidden Technical Debt\" in ML systems. Learn the core pillars of MLOps: reproducibility, accountability, and collaborative development. This module introduces the maturity levels of MLOps, from manual processes to fully automated CI\/CD pipelines.\n    \u003c\/div\u003e\n\u003c\/details\u003e\n\n\n\n\u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eModule 2: Data \u0026amp; Model Versioning (DVC \u0026amp; MLflow) \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n        In MLOps, code versioning is not enough. Master tools like DVC (Data Version Control) to track datasets and MLflow for experiment tracking and model registries. Learn to manage the \"Lineage\" of a model, ensuring you know exactly which data and which code produced every production asset.\n    \u003c\/div\u003e\n\u003c\/details\u003e\n\n\u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eModule 3: Automated Pipelines \u0026amp; CI\/CD\/CT \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n        Build the automated engine. Master the implementation of \u003cstrong\u003eContinuous Training (CT)\u003c\/strong\u003e, where pipelines automatically retrain models when new data arrives or performance drops. Learn to use Kubeflow, TFX (TensorFlow Extended), or GitHub Actions to orchestrate complex multi-step ML workflows.\n    \u003c\/div\u003e\n\u003c\/details\u003e\n\n\n\n\u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eModule 4: Model Deployment \u0026amp; Serving Strategies \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n        Get models into the hands of users. Explore deployment patterns including Canary releases, Blue\/Green deployments, and A\/B testing. Learn the nuances of high-performance serving using Seldon Core, NVIDIA Triton, or Kubernetes-based inference services (KServe).\n    \u003c\/div\u003e\n\u003c\/details\u003e\n\n\u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eModule 5: Monitoring, Drift Detection \u0026amp; LLMOps \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n        Ensure your models don't \"go rogue.\" Learn to detect \u003cstrong\u003eConcept Drift\u003c\/strong\u003e and \u003cstrong\u003eData Drift\u003c\/strong\u003e using statistical checks. New for 2026, this module includes LLMOps essentials: monitoring LLM hallucinations, managing vector database health, and auditing generative outputs for safety and compliance.\n    \u003c\/div\u003e\n\u003c\/details\u003e\n\n\n\n\u003ch3 class=\"dt-heading-section\"\u003eFrequently Asked Questions\u003c\/h3\u003e\n\u003cdiv class=\"dt-faq-accordion-v7\"\u003e\n    \u003cdetails class=\"dt-faq-item-v7\"\u003e\n        \u003csummary\u003eHow does MLOps differ from standard DevOps?\u003c\/summary\u003e\n        \u003cdiv class=\"dt-faq-answer\"\u003e\n            DevOps focuses on code and infrastructure. MLOps adds a third dimension: \u003cstrong\u003eData\u003c\/strong\u003e. In ML, even if the code doesn't change, the model's performance can degrade because the real-world data it processes changes (Drift). MLOps introduces Continuous Training (CT) to handle this specific challenge.\n        \u003c\/div\u003e\n    \u003c\/details\u003e\n    \u003cdetails class=\"dt-faq-item-v7\"\u003e\n        \u003csummary\u003eDo I need to be a Ph.D. in Mathematics for this course?\u003c\/summary\u003e\n        \u003cdiv class=\"dt-faq-answer\"\u003e\n            No. This is an engineering-first course. While a basic understanding of how ML models work is required, the focus is on the tooling, automation, and infrastructure (Kubernetes, Docker, CI\/CD tools) rather than deep mathematical theory.\n        \u003c\/div\u003e\n    \u003c\/details\u003e\n    \u003cdetails class=\"dt-faq-item-v7\"\u003e\n        \u003csummary\u003eIs this course cloud-specific?\u003c\/summary\u003e\n        \u003cdiv class=\"dt-faq-answer\"\u003e\n            We focus on \"Cloud-Agnostic\" principles using open-source tools like MLflow, Kubernetes, and DVC. However, we do provide specific implementation guides for the \"Big Three\" (AWS SageMaker, Azure ML, and Google Vertex AI) so you can apply these skills in any enterprise environment.\n        \u003c\/div\u003e\n    \u003c\/details\u003e\n    \u003cdetails class=\"dt-faq-item-v7\"\u003e\n        \u003csummary\u003eWhat is LLMOps and is it included?\u003c\/summary\u003e\n        \u003cdiv class=\"dt-faq-answer\"\u003e\n            LLMOps is a subset of MLOps tailored specifically for Large Language Models. It includes managing prompt versions, RAG (Retrieval-Augmented Generation) pipelines, and cost\/latency optimization for LLM APIs. This is a core part of the 2026 update for this course.\n        \u003c\/div\u003e\n    \u003c\/details\u003e\n\u003c\/div\u003e","brand":"DiviTrain.com","offers":[{"title":"Default Title","offer_id":54757052416325,"sku":null,"price":279.2,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0280\/0350\/0118\/files\/MLops_8c8d1262-a59f-4180-8991-ad3f19123dab.webp?v=1770132033","url":"https:\/\/www.divitrain.com\/en-eu\/products\/mlops-machine-learning-operations","provider":"DiviTrain.com","version":"1.0","type":"link"}