Production-Grade MLOps: Build Reliable ML Systems with SRE

Name: Production-Grade MLOps: Building Reliable Machine Learning Systems Using SRE Principles
Availability: InStock

Integrate Site Reliability Engineering principles into machine learning workflows to build dependable, observable, and scalable model architectures.

Production-Grade MLOps: Build Reliable ML Systems with SRE

At a glance

Duration: 1 Week
Format: Classroom
Cities: Tokyo, Manama, Amsterdam, Casablanca, Prague, Istanbul and more
Next session: 2 – 6 November 2026, Tokyo
Average fee: 5,700 €

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Production-Grade MLOps: Building Reliable Machine Learning Systems Using SRE Principles

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Overview

Operationalizing machine learning requires moving beyond experimental notebook workflows to establish high-availability, fault-tolerant architectures in live enterprise environments. This professional programme in production-grade MLOps bridges data science workflows with Site Reliability Engineering (SRE) paradigms to ensure statistical models function predictably under dynamic production workloads. Participants master the operationalization of Service Level Indicators (SLIs), Service Level Objectives (SLOs), automated deployment pipelines, and telemetry systems tailored for complex predictive pipelines. This course is delivered by Agile Leaders Training Center.

Who Should Attend

Machine learning engineers seeking to integrate rigorous reliability frameworks into operational environments.
Site reliability engineers tasked with maintaining infrastructure, availability, and error budgets for machine learning workloads.
MLOps engineers building telemetry, packaging systems, and continuous delivery pipelines for intelligent applications.
Data engineers and software developers responsible for building reliable feature stores, serving endpoints, and inference pipelines.
DevOps specialists and AI product managers overseeing operational risk, uptime, and governance across production machine learning systems.

Departments and Industries

This course supports technical teams maintaining critical predictive systems across data-intensive sectors.

Data Science and AI Units in Financial Services
Engineering and DevOps Teams in Telecommunications
IT Operations and Infrastructure Groups in Technology and Cloud Services
Quality Assurance and Risk Divisions in Healthcare and Biotechnology
ML Governance and Compliance Units in Government and the Public Sector
Product Engineering Groups in E-Commerce and Digital Retail

Learning Objectives

By the end of this course, participants will be able to:

Design resilient machine learning architectures grounded in core SRE operational principles.
Construct robust model deployment pipelines using canary releases, shadow deployments, and automated rollbacks.
Define, measure, and enforce ML-specific SLIs and SLOs across training and serving infrastructure.
Implement telemetry and ML observability tools to identify data skew, feature drift, and latency bottlenecks.
Formulate structured incident response playbooks to accelerate post-mortem root cause analysis for model failures.
Enforce governance, reproducibility, and ethical safeguards across enterprise feature stores and data workflows.

Course Agenda

Day 1: Foundations of Reliable ML Systems

Deconstructing the Machine Learning Lifecycle and Inherent Operational Vulnerabilities
Adapting Site Reliability Engineering Tenets to Predictive System Architectures
Data Intake Integrity: Managing Collection, Annotation, and Pipeline Ingestion Risks
Architecting Resilient Pipeline Orchestration for Distributed Model Training
Systematic Analysis of Failure Modes and Silent Faults in Operational Workflows
Balancing Mathematical Complexity against System Maintainability Trade-Offs
Operational Analysis of Workflow Feedback Loops and the YarnIt Case Study

Day 2: Data Management and Governance in ML

Data Durability Architectures, Lineage Versioning, and Cryptographic Access Controls
Feature Store Topology: Metadata Schema Design and Ingestion Latency Management
Security Policies, Data Privacy Protection, and Model Fairness Considerations
Audit Documentation Frameworks for Human Annotation Consistency and Quality Control
Aligning Enterprise Regulatory Compliance Policies with Automated Pipeline Execution
Systematic Triage of Data-Driven Production Failures and Pipeline Edge Cases
Governance Retrospective: Mitigating Architectural Debt and Upstream Data Rot

Day 3: Model Validation, Observability, and Monitoring

Formulating Validation Thresholds for Pre-Production Quality and Statistical Efficacy
Offline Evaluation Protocols: Distribution Shift, Performance Baselines, and Slicing
Online Validation Mechanics: Controlled A/B Experiments and Shadow Inference Traffic
Telemetry Implementation: Telemetry Instrumentation and ML Observability Tools
Defining SLIs and Enforcing Error Budgets for Inference Latency and Model Health
Automated Drift Detection: Monitoring Covariate Shift, Label Skew, and Decay
Observability Architecture: Dashboard Telemetry Configuration and Alert Thresholds

Day 4: Scalable Deployment and Incident Response

Model Serving Topology: Designing High-Throughput Batch, Streaming, and Edge Systems
Progressive Rollout Strategies: Blue/Green Swaps, Canary Pipelines, and Fast Rollbacks
Autoscaling Mechanics, Inference Cache Invalidation, and High-Availability Failover
Developing and Operationalizing Machine Learning Incident Response Playbooks
Post-Mortem Frameworks: Blameless Root Cause Analysis for Algorithmic Failures
Operational Governance: Mitigating Algorithmic Bias and Enforcing Ownership Boundaries
Live Simulation: Outage Mitigation, Circuit Breaking, and Model Resilience Drills

Day 5: Organizational Integration and MLOps Best Practices

Team Topologies: Defining Machine Learning Systems Engineering Roles and Interfaces
Enterprise Operational Paradigms: Centralized MLOps Platforms vs. Embedded Squads
Continuous Training Loops: Triggering Real-Time Pipeline Re-Execution and Validation
Lifecycle Ownership: Auditing Frameworks, Compliance Guardrails, and Ethics Controls
Applied Engineering Case Studies: NLP Inference Load Testing and Ad Click Latency
Systematic Enterprise Auditing and Regulatory Compliance Verification Protocols
Architecture Presentation: Capstone Technical Defense and Reliability Assessment

Practical Exercises

Participants apply technical concepts through hands-on reliability engineering exercises.

Suggested activity: Establish SLIs, SLOs, and an error budget model for a high-volume inference service.
Suggested activity: Configure Prometheus and Grafana dashboards to alert on data drift and distribution divergence.
Suggested activity: Build an incident response playbook addressing sudden inference degradation and serving failure.
Suggested activity: Formulate an automated rollback policy for a canary deployment experiencing feature skew.

FAQs

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

Participants should have a foundational understanding of machine learning concepts, familiarity with software engineering or DevOps methodologies, and working knowledge of cloud environments or machine learning frameworks.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day consists of 4 to 5 hours of instruction, structured discussions, and practical exercises, totalling 20 to 25 hours over the one-week duration.

What is the difference between monitoring ML models and traditional software systems?

Conventional software monitoring tracks infrastructure metrics such as CPU saturation, network throughput, and process uptime. Machine learning monitoring must additionally measure data distribution shifts, feature drift, prediction entropy, label latency, and degradation in mathematical performance that occurs without service crashes.

Conclusion

Scaling machine learning requires replacing manual handoffs with disciplined engineering practices. By embedding SRE methodologies, observability metrics, structured incident response, and continuous deployment workflows into production platforms, cross-functional teams ensure their machine learning systems deliver measurable business reliability and long-term operational resilience.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 79 events

Location	Dates	Duration	Mode	Price
Munich	Week 04, 2027 25 – 29 January 2027	1 Week	Onsite	€5,700
Bali	Week 04, 2027 31 January – 4 February 2027	1 Week	Onsite	€5,700
Frankfurt	Week 06, 2027 8 – 12 February 2027	1 Week	Onsite	€5,700
Nice	Week 07, 2027 15 – 19 February 2027	1 Week	Onsite	€5,700
Accra	Week 07, 2027 21 – 25 February 2027	1 Week	Onsite	€4,100
Singapore	Week 09, 2027 1 – 5 March 2027	1 Week	Onsite	€5,700
Chicago	Week 09, 2027 7 – 11 March 2027	1 Week	Onsite	€12,000
New York	Week 11, 2027 15 – 19 March 2027	1 Week	Onsite	€12,000
Istanbul	Week 12, 2027 22 – 26 March 2027	1 Week	Onsite	€4,500
Porto	Week 13, 2027 29 March – 2 April 2027	1 Week	Onsite	€5,700
Zanzibar	Week 13, 2027 4 – 8 April 2027	1 Week	Onsite	€5,500
Phuket	Week 14, 2027 11 – 15 April 2027	1 Week	Onsite	€6,000
San Diego	Week 16, 2027 19 – 23 April 2027	1 Week	Onsite	€14,000
Lisbon	Week 17, 2027 26 – 30 April 2027	1 Week	Onsite	€5,700
Lagos	Week 17, 2027 2 – 6 May 2027	1 Week	Onsite	€4,100
Nairobi	Week 19, 2027 16 – 20 May 2027	1 Week	Onsite	€4,500
Tashkent	Week 20, 2027 23 – 27 May 2027	1 Week	Onsite	€4,500
Dubai	Week 22, 2027 31 May – 4 June 2027	1 Week	Onsite	€4,500
Dubai	Week 25, 2027 21 – 25 June 2027	1 Week	Onsite	€4,500
Montreux	Week 25, 2027 21 – 25 June 2027	1 Week	Onsite	€7,500

Frequently asked questions

What does this course cover?

OverviewOperationalizing machine learning requires moving beyond experimental notebook workflows to establish high-availability, fault-tolerant architectures in live enterprise environments. This professional programme in production-grade MLOps bridges data science workflows with Site Reliability Engineering (SRE) paradigms to ensure statistical models fu…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Production-Grade MLOps: Build Reliable ML Systems with SRE