Production-Grade MLOps: Build Reliable ML Systems with SRE

Inspired by: Chen et al.'s Reliable Machine Learning: Applying SRE Principles to ML in Production

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Production-Grade MLOps: Build Reliable ML Systems with SRE

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Course Overview:

This advanced training program connects machine learning engineering with site reliability engineering (SRE) to create reliable, scalable, and production-ready ML systems. The course covers best practices from software engineering and DevOps throughout the ML lifecycle.

Participants will explore key topics such as ML model monitoring, data reliability, model serving strategies, and incident response, aligned with industry standards like MLOps best practices, machine learning system design, and ML deployment strategies.

Target Audience:

Machine Learning Engineers
MLOps Engineers
Site Reliability Engineers
Data Scientists
Data Engineers
Software Developers integrating ML
AI Product Managers
DevOps Professionals entering ML environments

Targeted Organisational Departments:

Data Science & AI Units
Engineering & DevOps
IT Operations & Infrastructure
Quality Assurance and Risk
Product and Innovation Teams
ML Governance & Compliance

Targeted Industries:

Financial Services
Healthcare
E-commerce & Retail
Telecommunications
Technology & Cloud Services
Government & Public Sector

Course Offerings:

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

Design reliable ML systems using SRE principles
Build scalable ML production pipelines
Apply ML observability tools for monitoring and validation
Define SLOs and SLIs for ML workflows
Implement robust ML deployment strategies
Mitigate ML model reproducibility issues and data drift
Address ML incident response and recovery using structured playbooks
Apply privacy, fairness, and ethical ML design considerations

Training Methodology:

This program combines instructor-led sessions, peer discussions, case studies, and simulation labs. Participants will work in small groups to design machine learning system architectures, analyse model failures, and establish Service Level Objectives (SLOs) and Service Level Indicators (SLIs).

Course Toolbox:

Course ebook and system design templates
Access to monitoring and observability sandbox (e.g., Prometheus, Grafana for ML)
Sample datasets for model training and validation
Checklists for ML reproducibility and ethical AI assessment
Templates for SLOs and incident response planning

Course Agenda:

Day 1: Foundations of Reliable ML Systems

Topic 1: Understanding the ML Lifecycle and Reliability Challenges
Topic 2: Core Principles of Site Reliability Engineering for ML Systems
Topic 3: Data Collection, Labeling, and Governance Issues
Topic 4: Building Robust ML Training Pipelines
Topic 5: Failure Modes and Production Risks in ML Workflows
Topic 6: Model Development vs. System Design Trade-offs
Reflection & Review: Lessons from the ML Loop and YarnIt Case Study

Day 2: Data Management and Governance in ML

Topic 1: Designing for Data Durability, Versioning, and Access Control
Topic 2: Feature Stores, Metadata, and Labeling Infrastructure
Topic 3: Data Privacy, Security, and Fairness Considerations
Topic 4: Documentation Practices for Human Annotation and Label Quality
Topic 5: Policy and Compliance Impacts on ML Pipelines
Topic 6: Debugging Data-Driven Failures and Edge Cases
Reflection & Review: Review of Governance Failures and Preventive Design

Day 3: Model Validation, Observability, and Monitoring

Topic 1: Defining Quality Metrics for Model Validity and Effectiveness
Topic 2: Offline Evaluation: Metrics, Distributions, and Benchmarks
Topic 3: Online Evaluation: A/B Testing and Shadow Deployment
Topic 4: Building and Using ML Observability Tools
Topic 5: Designing and Measuring ML-specific SLOs and SLIs
Topic 6: Monitoring for Feature Drift, Data Skew, and Model Degradation
Reflection & Review: Observability Strategy and Dashboard Use Cases

Day 4: Scalable Deployment and Incident Response

Topic 1: Model Serving Architectures: Batch, Online, and Edge
Topic 2: Model Deployment Strategies: Blue/Green, Canary, and Rollbacks
Topic 3: Autoscaling, Caching, and Disaster Recovery Patterns
Topic 4: Developing and Executing Incident Response Playbooks
Topic 5: Root Cause Analysis and Postmortems in ML Contexts
Topic 6: Ethical Risks, Bias Failures, and Operational Accountability
Reflection & Review: Simulation of Outage Response and Model Resilience

Day 5: Organizational Integration and MLOps Best Practices

Topic 1: Designing ML Teams and Roles Across the Organization
Topic 2: Organizational Patterns for ML Integration: Centralized vs. Decentralized
Topic 3: Continuous ML Systems and Real-Time Model Updates
Topic 4: Governance, Ethics, and Lifecycle Ownership
Topic 5: Practical Case Studies: NLP Load Testing, Privacy-Aware Pipelines, Ad Click Prediction
Topic 6: Auditing and Compliance in Enterprise MLOps
Reflection & Review: Capstone Presentations and Peer Feedback

FAQ:

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

Basic understanding of ML concepts, familiarity with DevOps or software engineering practices, and some experience with cloud platforms or ML frameworks (e.g., TensorFlow, PyTorch) are recommended.

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

Each day's session is generally structured to last around 4-5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20-25 hours of instruction.

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

Monitoring ML models goes beyond basic metrics like uptime and latency. It involves tracking model accuracy, feature drift, data skew, and SLO violations. Reliable Machine Learning emphasises the need for specialised observability strategies that address ML-specific failure modes.

How This Course is Different from Other Production-Grade MLOps Courses:

Unlike typical MLOps training, this course emphasises operational excellence. It combines reliable machine learning principles with software engineering practices and real-world case studies of ML failures, model drift, and incident recovery.

Incorporating Site Reliability Engineering (SRE) concepts like Service Level Objectives (SLOs) and observability, participants learn to effectively build, deploy, and manage machine learning models in complex environments. The course also addresses ethical considerations, feature store design, and continuous deployment, making it a modern choice for professionals seeking scalable and high-performing machine learning systems.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Location	Dates	Duration	Mode	Price
Rome	Week 30, 2026 20 - 24 Jul 2026	5 Days	Onsite	€5,700
Barcelona	Week 30, 2026 20 - 24 Jul 2026	5 Days	Onsite	€5,700
Dubai	Week 31, 2026 27 - 31 Jul 2026	5 Days	Onsite	€4,500
Madrid	Week 31, 2026 27 - 31 Jul 2026	5 Days	Onsite	€5,700
Abu Dhabi	Week 32, 2026 03 - 07 Aug 2026	5 Days	Onsite	€4,500
London	Week 32, 2026 03 - 07 Aug 2026	5 Days	Onsite	€5,700
Paris	Week 33, 2026 10 - 14 Aug 2026	5 Days	Onsite	€5,700
Seoul	Week 33, 2026 10 - 14 Aug 2026	5 Days	Onsite	€10,000
Cape town	Week 33, 2026 16 - 20 Aug 2026	5 Days	Onsite	€6,000
Cairo	Week 34, 2026 17 - 21 Aug 2026	5 Days	Onsite	€4,100
Dubai	Week 34, 2026 17 - 21 Aug 2026	5 Days	Onsite	€4,500
Baku	Week 35, 2026 24 - 28 Aug 2026	5 Days	Onsite	€5,000
Istanbul	Week 35, 2026 24 - 28 Aug 2026	5 Days	Onsite	€4,500
Istanbul	Week 35, 2026 24 - 28 Aug 2026	5 Days	Onsite	€4,500
Manama	Week 35, 2026 30 Aug - 03 Sep 2026	5 Days	Onsite	€4,700
Vienna	Week 36, 2026 31 Aug - 04 Sep 2026	5 Days	Onsite	€5,700
Kuwait	Week 36, 2026 06 - 10 Sep 2026	5 Days	Onsite	€5,500
Sharm El-Sheikh	Week 37, 2026 07 - 11 Sep 2026	5 Days	Onsite	€4,100
Johannesburg	Week 37, 2026 13 - 17 Sep 2026	5 Days	Onsite	€6,000
Amsterdam	Week 38, 2026 14 - 18 Sep 2026	5 Days	Onsite	€5,700