Async review
Instruction and application
In ProgressRecap core topics:
Unit 2:
Scalability and capacity management.
Unit 3:
Model life cycle management and decommissioning.
Unit 2: Scalability and capacity management
In Unit 2, you explored…
Scalability drivers:
This section discusses how data volume, velocity, variety and quality affect ML system performance, efficiency and resource use.
Data handling strategies:
These include batch vs streaming pipelines, distributed storage, partitioning, feature stores and schema enforcement for diverse data formats.
Capacity planning frameworks:
These include defining operational requirements (throughput, latency, availability, scalability, cost-effectiveness), mapping them across ML pipelines and using workload characterisation, load/stress testing, benchmarking and profiling to prepare for growth.
Scaling strategies:
These include horizontal, vertical and autoscaling approaches, plus load balancing to manage demand surges cost-effectively.
Resource estimation:
This topic demonstrates calculating compute, memory, storage and bandwidth needs for inference and training, validated with load testing.
Supply chain risks:
This section covers vendor lock-in, third-party API reliance, GPU shortages, software dependencies and governance issues, plus strategies such as redundancy, monitoring, contracts, multicloud set-ups and containerisation to mitigate them.
Unit 3: Model life cycle management and decommissioning
In Unit 3, you explored…
Managing change in ML:
How structured processes, versioning (code, data, models), automated testing and staged deployments (shadow, canary, A/B) ensure smooth, traceable and compliant updates.
Logging and monitoring:
Why detailed logs (metadata, deployment records, inference data, audit trails) are critical for auditability, debugging, compliance and reproducibility, supported by tools such as MLflow, model registries, observability and alerting systems.
Decommissioning protocols:
Safe retirement of ML models through dependency mapping, stakeholder communication, traffic diversion and resource de-provisioning while monitoring for disruptions.
Archiving for compliance and reproducibility:
Preserving model artefacts, metadata, training data snapshots, logs and code to meet regulatory requirements (e.g. GDPR, HIPAA, EU AI Act) and support audits, dispute resolution and institutional learning.
Scalability essentials
Throughput
and** latency**define system performance under load.
Scaling strategies:
**Vertical scaling =**add more power to one machine.
Horizontal scaling =
add more machines to share the load.
Autoscaling =
automatic adjustment based on demand.
- Data variety and formats (structured, unstructured, multimodal) affect scalability and resource requirements.
- Data quality issues (missing values, schema changes, noisy inputs) increase compute costs and reduce efficiency.
Supply chain and governance risks
Supply chain risks:
Vendor lock-in, reliance on third-party APIs, GPU shortages.
Change management:
Version control and model registries.
- Staged deployments: Shadow, canary, A/B testing.
- **Logging essentials:**Inference logs, metadata and audit trails for compliance and traceability.
Decommissioning protocols
Dependency mapping:
Identify systems and stakeholders relying on the model.
Traffic diversion:
Safely shift users/services to newer models through staged rollouts.
De-provisioning:
Retire compute and storage resources once dependencies are cleared.
Archiving:
Retain models, datasets, logs and documentation for compliance and traceability.
Communication:
Inform stakeholders of retirement timelines and mitigation plans to maintain business continuity.
Action item: Poll — from scaling to retiring
It's time for a quick life cycle-focused poll. This will help you check your understanding of scalability, governance and model retirement practices in production ML systems. No pressure — just go with your best judgement!