Skip to content
Datamewby Intura
Start free
MLOps: Deploying Machine Learning & Deep Learning Models

ML Systems in Production

Why models degrade after launch, how batch and online inference differ, and what to monitor.

Level
Intermediate
Lessons
4
Total time
1h 4m
Simulations
4
1 free lessonIncluded in MLOps: Deploying Machine Learning & Deep Learning Models · $9
Tools you'll use
  • Python
  • Docker
  • FastAPI
  • Kubernetes
  • GitHub
Skills
  • Data drift
  • Model monitoring
  • ML system design
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Tell covariate drift from concept drift
  • Compute the PSI for one feature
  • Choose between batch and online inference

Before you start

A suggestion, never a lock — you can start here.

  • training-and-evaluation
Modules that help first

Syllabus

4 lessons · 1h 4m

Every lesson's title and summary is public. The lessons themselves open once you own the track. The lesson marked free can be read in full right now, no account needed.

Every lesson in MLOps: Deploying Machine Learning & Deep Learning Models

  1. Why Models Decay in Production

    18 min

    A model is trained on the past and used on the future. When the world shifts, its accuracy falls — often without a single error in the logs.

    FreeRead it free Simulation

  2. Training-Serving Skew

    16 min

    When features are computed differently in training and at prediction time — the most common production ML bug, and the one least likely to show up in a log.

    Included with the track Simulation

  3. Batch vs Online Inference

    14 min

    Scheduled or on-request predictions: this choice decides latency, cost, which features you can use, and how the system fails.

    Included with the track

  4. An ML System Design Checklist

    16 min

    Five questions to answer before a single line of deployment code is written — because the most expensive ML failures are not bugs, but systems built for the wrong question.

    Included with the track Simulation
Lifetime access
$9
Buy the track
More in MLOps: Deploying Machine Learning & Deep Learning Models
Module · Intermediate

Packaging and Serving Models

From notebook to service: saving models, an API with FastAPI, and reproducible Docker containers.

  • 3 lessons
  • 48 min
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

Serving Deep Learning Models and LLMs

GPUs, batching, quantisation and formats like ONNX — making large models fast enough and cheap enough.

  • 4 lessons
  • 1h 6m
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

ML Pipelines and CI/CD

Experiment tracking, model registries, and automated pipelines from raw data to a released model.

  • 3 lessons
  • 47 min
  • 2 simulations
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9
Module · Intermediate

Monitoring and Retraining

Drift detection, performance with delayed labels, retraining strategies, and safe rollouts.

  • 3 lessons
  • 48 min
  • 4 simulations
Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9