Free Download MLOps Fundamentals The Complete Guide to ML in ProductionPublished 2/2026
Created by Data Universe
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 27 Lectures ( 2h 20m ) |
Size: 1.66 GB
✓ Understand what MLOps is, why it matters in 2026, and how it solves the gap between notebook models and real production value
✓ Explain the key differences between DevOps and MLOps, including data drift, model decay, experiment tracking, and ML-specific testing needs
✓ Describe the full ML lifecycle in production: data preparation, training, deployment, monitoring, and continuous retraining
✓ Analyze business impact metrics for MLOps, such as time‑to‑production, reliability, cost reduction, and ROI for ML initiatives
✓ Design high‑level MLOps architectures with pipelines, CI/CD for ML, model registries, and automated retraining triggers
✓ Compare major MLOps platforms (SageMaker, Vertex AI, Azure ML, MLflow, Kubeflow) and choose what fits a given organization
✓ Define roles and responsibilities in MLOps teams (data scientists, ML engineers, MLOps/platform engineers, product, and business stakeholders).
✓ Interpret real‑world MLOps case studies (recommendations, fraud detection, churn) and connect technical practices to concrete business outcomes.
Requirements● No formal ML or coding background is required; the course is designed for business and technical audiences who want to understand MLOps at a practical, high level
● Basic familiarity with what machine learning is (e.g., what a "model" or "prediction" means) will help, but key concepts are briefly recapped
● Access to a computer with internet and a modern browser to follow along with platform examples and optional hands‑on explorations
DescriptionThis course gives you a clear, non‑fluffy roadmap to understand and implement MLOps, so that machine learning projects stop dying in notebooks and start delivering real value in production. It is designed for both technical and non‑technical profiles: data scientists, engineers, product managers, and business leaders who need a shared language around ML in production.
We start by defining what MLOps is in 2026 and why it has become essential. You will see how MLOps closes the gap between model development and production, and how it differs from traditional DevOps: data dependency, model decay, experimentation at scale, probabilistic testing, and the added complexity of data and model versioning.
Then we walk through the full ML lifecycle from a production point of view: data preparation pipelines (ingestion, validation, cleaning, transformation), experiment tracking and model training, deployment strategies (batch vs real‑time, canary, blue‑green, A/B), and continuous monitoring with automated retraining.
You will also learn how to interpret and use the main metrics that matter: technical metrics (accuracy, latency, drift), business metrics (ROI, cost savings, time‑to‑value), SLAs, and governance KPIs for compliance, fairness, and explainability.
Finally, we cover people and strategy: team roles (data scientists, ML engineers, MLOps engineers, product, business), real‑world case studies (recommendations, fraud detection, churn), and a concrete implementation roadmap so you can start or improve MLOps in your own organization.
Who this course is for■ Analytics professionals and aspiring MLOps practitioners looking for a structured, business‑friendly overview before diving into deeply technical tools and code
■ Curious learners from any background who want to understand how modern AI systems are deployed, monitored, and governed in real companies, beyond simple demos and prototypes
■ Data scientists and ML practitioners who want a clearer view of how their models move from notebooks into reliable, monitored production systems
■ University students, bootcamp graduates, and junior professionals who know the basics of ML or data and want a structured, real‑world introduction to MLOps without heavy math or coding
■ Product managers, tech leads, and engineering managers who must make decisions about MLOps investments, roadmaps, and trade‑offs between speed, risk, and cost.
■ Software, data, and ML engineers who need to understand MLOps concepts, architectures, and platforms to support large‑scale ML deployments
■ Business leaders, innovation managers, and non‑technical stakeholders who want to speak the "language of MLOps" to evaluate ML initiatives and measure real ROI.
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