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MLOps (Machine Learning Operations)
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Bridging the Gap: Master the Lifecycle of Production-Grade AI with MLOps
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.
Who is this for?
- Data Scientists: Looking to move beyond experimental modeling to understand how their code lives in production.
- DevOps Engineers: Professionals aiming to specialize in the unique requirements of machine learning infrastructure and hardware acceleration.
- Machine Learning Engineers: Individuals focused on building robust, automated pipelines for model retraining and deployment.
- Software Architects: Technical leads designing the structural foundation for AI-integrated enterprise applications.
- IT Operations Managers: Leaders tasked with managing the cost, compliance, and reliability of organizational AI assets.
Ready for roles like
- MLOps Engineer: Automating the end-to-end lifecycle of machine learning models and ensuring pipeline stability.
- AI Infrastructure Architect: Designing scalable cloud and hybrid environments for model training and inference.
- LLMOps Specialist: Managing the unique challenges of Large Language Models, including fine-tuning pipelines and RAG orchestration.
- Model Reliability Engineer: Monitoring model health, detecting drift, and implementing automated retraining triggers.
- Data Engineer (ML Focus): Building the feature stores and data pipelines that feed high-performance models.
Course Curriculum
Module 1: The MLOps Framework & Principles +
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.
Module 2: Data & Model Versioning (DVC & MLflow) +
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.
Module 3: Automated Pipelines & CI/CD/CT +
Build the automated engine. Master the implementation of Continuous Training (CT), 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.
Module 4: Model Deployment & Serving Strategies +
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).
Module 5: Monitoring, Drift Detection & LLMOps +
Ensure your models don't "go rogue." Learn to detect Concept Drift and Data Drift 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.
Frequently Asked Questions
How does MLOps differ from standard DevOps?
DevOps focuses on code and infrastructure. MLOps adds a third dimension: Data. 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.
Do I need to be a Ph.D. in Mathematics for this course?
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.
Is this course cloud-specific?
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.
What is LLMOps and is it included?
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.
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- Video lessons with slides and visual diagrams
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MeasureUp Practice Exams
Simulate the real exam before exam day
MeasureUp is the world's leading exam prep platform. 213 questions in the exact format you'll face at the Pearson VUE test center.
- 213 exam-style questions with detailed answer feedback
- Practice mode + full Certification simulation mode
- 75% pass score benchmark — same as the real exam
- 60 days access included with every course
Expert Tutor Support
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Hit a wall? Your personal mentoring team answers course and certification questions via chat or email — around the clock.
- Expert tutor support available 24/7
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- Certification-specific guidance
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Value Comparison
How DiviTrain compares
Same exam. A fraction of the cost. See how this CertKit stacks up against the alternatives.
| Best ValueDiviTrain CertKit | Classroom Training | Pluralsight / LinkedIn | |
|---|---|---|---|
| Price | $329 | €1,500–€2,000 | From $399/year |
| Video training | ✓ | ✓ | ✓ |
| Hands-on labs | ✓Guided, virtual environment | ✓ | Premium only |
| MeasureUp practice exams | ✓60 days included | ✕ | ✕ |
| Expert tutor support | ✓Available 24/7 | ✕ | ✕ |
| Access duration | 365 days | 5 days | While subscribed |
| Study at your own pace | ✓ | Fixed schedule | ✓ |
| Exam voucher included | ✕Book via Pearson VUE | Sometimes | ✕ |
* Prices shown are indicative examples. Actual prices may vary by product, provider and region.
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