How do we standardize without teams routing around the platform?

Make the standard path the easiest path. Platforms that enforce compliance before delivering value get bypassed. The course covers golden-path templates that remove real work, adoption metrics that tell you whether the paved road is actually being used, and reserving hard enforcement for the small set of controls that genuinely require it.

Kubeflow, Argo, or Airflow?

Airflow is strongest where ML pipelines sit alongside existing data engineering workflows. Argo Workflows is the lighter Kubernetes-native option and composes well with existing GitOps practice. Kubeflow offers the most ML-specific functionality at the highest operational cost. The course compares them on operational burden, team familiarity, and failure modes rather than on feature lists.

What does audit actually require?

In practice: which model version served a given prediction, what data trained it, who approved promotion, and when. Most organizations can answer none of these without a manual investigation. The course covers building lineage that answers them as a query.

How do we handle GPU contention between teams?

Through quota, priority classes, and preemption policy, with the political question of who gets preempted settled before the contention happens rather than during an incident. The course covers the scheduling mechanics and the allocation models that hold up organizationally.

Can this be run for our platform team specifically?

Yes, and it usually is. For private cohorts we work against your actual stack and constraints, and the deliverables become artifacts your team can implement directly.

AdvancedDevOps & MLOps

Enterprise CI/CD and MLOps at Scale

Name: Enterprise CI/CD MLOps At Scale
Availability: InStock

Run ML platforms across many teams: pipeline standardization, governance gates, multi-environment promotion, and platform economics that hold at organizational scale.

Course Overview

One team can ship models on conventions and goodwill. Thirty teams cannot. At organizational scale the problems change shape: every team invents its own pipeline, nobody can say which model is serving a given endpoint, audit requests take weeks, and platform costs grow without anyone able to attribute them. This course is about building the platform and guardrails that make ML delivery consistent without making it slow.

The central design tension is standardization against autonomy. Templates that are too rigid get bypassed; a platform with no opinions produces thirty incompatible pipelines. You will work through golden-path templates, paved-road tooling, and policy-as-code that enforces the rules that genuinely matter while leaving teams free elsewhere. Governance gates, model approval workflows, and complete lineage from data through to serving endpoint are treated as engineering problems with concrete implementations.

The course also covers what platform teams are actually judged on: multi-environment promotion with real isolation, secure handling of credentials and artifacts across boundaries, cost attribution back to the teams generating spend, and the economics of GPU pooling, quota, and prioritization when demand exceeds supply.

Duration: 3 days|Delivery: onsite, virtual, or hybrid

Prerequisites

Solid MLOps experience, or completion of MLOps for Production AI
Strong CI/CD background with a system such as GitHub Actions, GitLab CI, or Jenkins
Kubernetes experience
Exposure to multi-team or platform engineering environments

Who Should Attend

Platform engineers building shared ML infrastructure for an organization
MLOps leads standardizing delivery across multiple product teams
Engineering managers accountable for ML delivery velocity and audit readiness
Architects designing an internal ML platform or consolidating several

Course Outline

1Platform engineering for ML: what to centralize, what to leave to teams, and how to tell
2Golden-path templates and paved-road tooling that teams adopt willingly
3Pipeline orchestration at scale: Kubeflow, Argo, and Airflow operational tradeoffs
4Multi-environment strategy: dev, staging, and production with genuine isolation
5Promotion workflows: automated gates, human approvals, and the audit record
6Policy as code: enforcing standards without blocking delivery
7End-to-end lineage: tracing a serving endpoint back to data, code, and approver
8Secrets, artifacts, and secure promotion across environment boundaries
9GPU fleet management: pooling, quota, prioritization, and preemption
10Cost attribution and chargeback across teams and workloads
11Multi-tenancy: isolation, noisy neighbors, and fair scheduling
12Platform observability: pipeline reliability, delivery lead time, and adoption metrics
13Migration strategy: consolidating existing team pipelines without halting delivery

Learning Outcomes

Decide deliberately what your platform standardizes and what it leaves open
Build golden-path templates teams choose over rolling their own
Implement promotion workflows with governance gates that produce audit evidence
Trace any production model back to its data, code, and approvals
Manage a shared GPU fleet with quota and prioritization under contention
Attribute platform cost accurately to the teams generating it
Plan a migration that consolidates fragmented pipelines incrementally

What You Will Build

A golden-path pipeline template for your organization's stack
A promotion and approval workflow design with audit evidence at each gate
A lineage model connecting serving endpoints to data, code, and approvers
A cost attribution and GPU quota scheme for shared infrastructure

Frequently Asked Questions

How do we standardize without teams routing around the platform?: Make the standard path the easiest path. Platforms that enforce compliance before delivering value get bypassed. The course covers golden-path templates that remove real work, adoption metrics that tell you whether the paved road is actually being used, and reserving hard enforcement for the small set of controls that genuinely require it.
Kubeflow, Argo, or Airflow?: Airflow is strongest where ML pipelines sit alongside existing data engineering workflows. Argo Workflows is the lighter Kubernetes-native option and composes well with existing GitOps practice. Kubeflow offers the most ML-specific functionality at the highest operational cost. The course compares them on operational burden, team familiarity, and failure modes rather than on feature lists.
What does audit actually require?: In practice: which model version served a given prediction, what data trained it, who approved promotion, and when. Most organizations can answer none of these without a manual investigation. The course covers building lineage that answers them as a query.
How do we handle GPU contention between teams?: Through quota, priority classes, and preemption policy, with the political question of who gets preempted settled before the contention happens rather than during an incident. The course covers the scheduling mechanics and the allocation models that hold up organizationally.
Can this be run for our platform team specifically?: Yes, and it usually is. For private cohorts we work against your actual stack and constraints, and the deliverables become artifacts your team can implement directly.

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