MLOps for Production AI
Operationalize machine learning: reproducible pipelines, model registries, automated retraining, drift detection, and the practices that keep models correct after launch.
Course Overview
The gap between a model that works in a notebook and a model that works in production for a year is where most AI investment is lost. The notebook version has no reproducible training pipeline, no record of which data produced which weights, no way to roll back, and no signal when the world shifts underneath it. MLOps is the discipline that closes that gap, and this course teaches it as engineering practice rather than as a diagram of boxes.
You will build a pipeline that goes from versioned data to a registered, deployable model artifact, with every step reproducible from source control. That covers data and feature versioning, experiment tracking, model registries with staged promotion, and packaging models so the training environment and the serving environment cannot silently diverge.
The second half is about life after deployment, which is where models actually fail. You will implement drift detection for both inputs and predictions, define retraining triggers that are evidence-based rather than calendar-based, and set up shadow deployments and canary releases so a new model version proves itself on real traffic before it takes it. The course closes on incident response: what to do when a model is producing garbage and nobody is sure which version is live.
Duration: 3 days|Delivery: onsite, virtual, or hybrid
Prerequisites
- Experience training machine learning models in Python
- Working knowledge of Docker and containers
- Familiarity with Git and a CI system
- Basic cloud experience on AWS, Azure, or GCP
Who Should Attend
- ML engineers whose models are moving from experiment to production ownership
- Data scientists who need their work to survive contact with a production system
- DevOps and platform engineers now responsible for supporting ML workloads
- Engineering managers standardizing how models ship across multiple teams
Course Outline
- 1Why ML systems fail differently from software: silent degradation and the absence of an exception
- 2Reproducibility from the ground up: data versioning, environment pinning, and seeding
- 3Experiment tracking: what to log so a result can be reconstructed months later
- 4Feature engineering pipelines and the training/serving skew problem
- 5Model registries: versioning, lineage, staged promotion, and approval gates
- 6Packaging models: containers, dependency pinning, and environment parity
- 7CI/CD for machine learning: testing data, testing models, and testing pipelines
- 8Deployment patterns: batch, real-time, streaming, and embedded inference
- 9Shadow deployments, canary releases, and A/B testing for models
- 10Monitoring in production: data drift, concept drift, prediction drift, and performance decay
- 11Retraining strategy: evidence-based triggers, automated pipelines, and human approval points
- 12Rollback and incident response for models behaving badly
- 13Cost management: training spend, inference spend, and knowing which to attack
Learning Outcomes
- Build a training pipeline reproducible from source control alone
- Track experiments so any historical result can be reconstructed
- Operate a model registry with meaningful promotion gates
- Detect and distinguish data drift, concept drift, and prediction drift
- Design retraining triggers based on measured degradation rather than a schedule
- Roll out model versions safely with shadow and canary deployments
- Diagnose and roll back a misbehaving production model quickly
What You Will Build
- An end-to-end reproducible training pipeline with versioned data and artifacts
- A model registry configuration with staged promotion and approval gates
- A drift monitoring setup with alerting thresholds you can defend
- A written runbook for model incident response and rollback
Frequently Asked Questions
- Do we need a dedicated MLOps platform?
- Usually not at first. Most teams get further with versioned data, tracked experiments, a model registry, and their existing CI system than with an expensive platform adopted before the practices are in place. The course covers open-source building blocks such as MLflow and DVC alongside managed options, so you can choose based on your scale rather than on a vendor pitch.
- How is this different from regular DevOps?
- Software fails loudly; models fail silently and stay up while producing worse answers. That difference drives everything: you version data as well as code, you test statistical properties as well as logic, and you monitor prediction distributions rather than only error rates. The course assumes DevOps familiarity and focuses on what is genuinely different.
- Does the course cover LLMs and generative AI operations?
- It covers the parts that generalize, including evaluation, versioning, monitoring, and rollback, and it addresses where LLM operations differ, particularly prompt versioning and the fact that quality is harder to measure automatically. For depth on retrieval systems specifically, pair it with Building Production-Ready RAG Systems.
- How often should models be retrained?
- On evidence, not on a calendar. Retraining a stable model wastes money and introduces risk; leaving a drifting model in place costs accuracy silently. The course covers measuring degradation and setting thresholds that trigger retraining when it is actually warranted.
- Which cloud does the course use?
- Labs are cloud-agnostic and run in a browser-based environment using open-source tooling. Managed equivalents on AWS SageMaker, Azure ML, and Google Vertex AI are covered so you can map the practices onto your platform.
Related Courses
Enterprise CI/CD and MLOps at Scale
Run ML platforms across many teams: pipeline standardization, governance gates, multi-environment promotion, and platform economics that hold at organizational scale.
ML Model Deployment
Serve models reliably: inference APIs, batching, autoscaling, GPU utilization, model optimization, and hitting a latency target without overspending.
Monitoring ML Systems
Master observability and monitoring for production machine learning systems. Learn to track model performance, detect drift, and maintain system health.
Securing AI Pipelines
Secure the infrastructure around your models: data provenance, artifact integrity, secrets, least-privilege access, and audit evidence across the ML lifecycle.
Ready to Get Started?
Contact us to schedule training for your team or inquire about upcoming sessions.