Description

This curriculum spans the breadth of a multi-workshop program that mirrors the iterative, cross-functional efforts seen in enterprise MLOps rollouts, covering everything from initial business alignment and data governance to model maintenance and organizational adoption.

Module 1: Defining Business Objectives and Aligning ML Initiatives

Selecting use cases based on measurable ROI, data availability, and stakeholder buy-in across departments
Negotiating scope boundaries between data science teams and business units to prevent mission creep
Translating ambiguous business problems into testable machine learning hypotheses
Establishing success metrics that balance predictive performance with operational impact
Conducting feasibility assessments that account for latency, infrastructure, and maintenance costs
Documenting decision logs for model purpose, intended use, and off-limits applications
Mapping data lineage from source systems to model inputs to validate business relevance
Aligning model development timelines with fiscal planning and budget cycles

Module 2: Data Strategy and Infrastructure Design

Choosing between batch and real-time data pipelines based on decision latency requirements
Designing feature stores with version control, access policies, and refresh SLAs
Implementing data contracts between engineering and analytics teams to enforce schema consistency
Deciding whether to build internal data labeling pipelines or outsource with quality controls
Allocating storage tiers for raw, processed, and feature data based on access frequency and cost
Integrating third-party data sources while managing licensing, refresh rates, and drift monitoring
Configuring data retention policies that comply with legal holds and model retraining needs
Designing metadata repositories to track feature definitions, ownership, and usage

Module 3: Feature Engineering and Data Quality Management

Implementing automated data validation checks for missing values, outliers, and distribution shifts
Creating derived features that balance predictive power with interpretability for stakeholders
Managing feature leakage by auditing temporal consistency in training and serving data
Standardizing feature scaling and encoding methods across development and production environments
Handling entity resolution when merging data from disparate systems with inconsistent keys
Versioning feature transformations to ensure reproducibility across model iterations
Monitoring feature stability and deprecating underperforming or redundant variables
Applying differential privacy techniques when engineering features from sensitive data

Module 4: Model Development and Validation Frameworks

Selecting algorithms based on interpretability requirements, data size, and update frequency
Designing cross-validation strategies that respect temporal, geographical, or hierarchical data structure
Implementing backtesting procedures that simulate real-world deployment conditions
Calibrating probability outputs for models used in risk-sensitive decision contexts
Conducting ablation studies to quantify the impact of individual features or data sources
Validating model performance across subpopulations to detect unintended bias
Building shadow mode evaluation systems to compare new models against production baselines
Documenting model assumptions, limitations, and known failure modes in technical specifications

Module 5: Model Deployment and MLOps Integration

Choosing between containerized microservices and serverless functions for model serving
Implementing canary rollouts with automated rollback triggers based on performance thresholds
Integrating model inference with existing business applications via REST or gRPC APIs
Configuring autoscaling policies based on query volume and latency SLAs
Versioning models, code, and environment configurations using CI/CD pipelines
Managing dependencies and compatibility across Python, library, and hardware versions
Designing stateless inference services to ensure horizontal scalability and fault tolerance
Implementing health checks and readiness probes for orchestration platforms like Kubernetes

Module 6: Monitoring, Drift Detection, and Model Maintenance

Setting up real-time dashboards for prediction volume, latency, and error rates
Defining statistical thresholds for data drift using Kolmogorov-Smirnov or PSI metrics
Implementing concept drift detection through residual analysis and performance decay tracking
Scheduling automated retraining pipelines with triggers based on drift or calendar intervals
Managing model decay in regulatory environments where updates require re-approval
Logging prediction inputs and outputs for auditability while managing storage costs
Establishing incident response protocols for model degradation or failure
Rotating model ownership and maintenance responsibilities across team members

Module 7: Governance, Compliance, and Ethical Oversight

Conducting model risk assessments aligned with regulatory frameworks like SR 11-7 or GDPR
Implementing access controls and audit trails for model development and deployment systems
Documenting model cards that include performance metrics, limitations, and usage restrictions
Performing bias audits using fairness metrics across protected attributes
Negotiating data use agreements that restrict model applications to approved domains
Designing human-in-the-loop workflows for high-stakes decisions with model uncertainty
Establishing escalation paths for model misuse or unintended consequences
Archiving models and data to support regulatory examinations and legal discovery

Module 8: Organizational Integration and Change Management

Designing training programs for non-technical stakeholders to interpret model outputs
Integrating model insights into existing decision workflows without disrupting operations
Building feedback loops where business outcomes inform model performance evaluation
Assigning model stewards to bridge communication between technical and business teams
Managing resistance from domain experts whose judgment is augmented by automation
Aligning incentive structures to reward data-driven decisions, not just model accuracy
Conducting post-implementation reviews to assess actual business impact vs. projections
Scaling successful pilots by standardizing tooling, documentation, and approval processes

Module 9: Advanced Topics in Scalable Decision Systems

Designing multi-armed bandit systems for continuous learning in dynamic environments
Implementing reinforcement learning frameworks with reward shaping and safety constraints
Orchestrating ensemble systems where multiple models serve different decision contexts
Building counterfactual analysis tools to support "what-if" scenario planning
Integrating causal inference methods to distinguish correlation from actionable insight
Managing model portfolios with centralized monitoring and lifecycle tracking
Applying active learning strategies to prioritize data labeling efforts
Designing fallback logic and default rules for model downtime or edge cases