A tailored course, built for your situation
Architecting Data and ML Platforms for Scale
A 12-module mastery path for engineering leaders building modern data and machine learning infrastructure
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Building data and ML systems in isolation leads to fragmentation, technical debt, and stalled deployments
The situation this course is for
Even high-performing engineering teams struggle to unify data pipelines, model lifecycle management, and platform governance. Without a coherent architecture, efforts become siloed, compliance lags, and time-to-value stretches. The gap isn’t skill, it’s structure. Practitioners know pieces of the stack but lack a proven blueprint to integrate them at scale.
Who this is for
Senior engineers, platform leads, and ML architects in tech companies building internal data and AI infrastructure
Who this is not for
Individual contributors focused only on analytics dashboards or isolated model training without platform ownership
What you walk away with
- Design scalable, secure, and maintainable data and ML platforms
- Integrate governance, observability, and compliance by design
- Accelerate model deployment cycles with reusable infrastructure patterns
- Align platform capabilities with product and business outcomes
- Lead cross-functional adoption of unified data and ML ecosystems
The 12 modules (with all 144 chapters)
Module 1. Foundations of Modern Data Platforms
Establish core principles for data platform design including scalability, interoperability, and team enablement. Learn how leading organizations structure ownership, define success, and align technical architecture with business velocity.
12 chapters in this module
- Defining the platform mindset
- From silos to shared infrastructure
- Core components of data platforms
- Role of data governance
- Designing for self-service
- Balancing flexibility and control
- Team topology patterns
- Evaluating vendor vs build
- Cost modeling fundamentals
- Security by design
- Compliance integration
- Roadmap prioritization
Module 2. Data Architecture Patterns at Scale
Explore proven architectural models including data meshes, lakes, warehouses, and streaming backbones. Understand trade-offs in consistency, latency, and complexity for enterprise-grade systems.
12 chapters in this module
- Data mesh principles
- Domain-driven ownership
- Decentralized governance
- Event-driven architecture
- Real-time data pipelines
- Batch vs stream balance
- Schema evolution strategies
- Data versioning patterns
- Metadata management
- Query optimization
- Partitioning strategies
- Cross-region replication
Module 3. ML Lifecycle Orchestration
Master end-to-end machine learning workflows from experiment tracking to reproducible training and model registry. Implement systems that ensure auditability and reduce rework.
12 chapters in this module
- Phases of ML lifecycle
- Experiment tracking setup
- Model registry design
- Reproducible training
- Hyperparameter tuning
- Model lineage tracking
- Versioning data and code
- Automated retraining
- Model performance decay
- Drift detection patterns
- Shadow mode deployment
- Canary release strategies
Module 4. Feature Engineering at Enterprise Scale
Design and operationalize feature stores that serve multiple teams while ensuring consistency, freshness, and governance across use cases.
12 chapters in this module
- What is a feature store
- Online vs offline serving
- Feature discovery
- Consistency guarantees
- Freshness SLAs
- Access control models
- Feature reuse patterns
- Monitoring feature quality
- Backfilling pipelines
- Versioning semantics
- Cross-team collaboration
- Cost attribution
Module 5. Model Deployment and Serving
Implement robust model serving infrastructure that supports A/B testing, multi-tenancy, and performance optimization across diverse workloads.
12 chapters in this module
- Model serving patterns
- GPU vs CPU tradeoffs
- Serverless inference
- Model packaging
- API design for models
- Latency optimization
- Load testing models
- Auto-scaling strategies
- Multi-model containers
- Model warmup techniques
- Caching predictions
- Edge deployment
Module 6. Observability for Data and ML
Build comprehensive monitoring systems that detect data drift, pipeline failures, and model degradation before they impact downstream users.
12 chapters in this module
- Three pillars of observability
- Data quality checks
- Pipeline health metrics
- Model performance dashboards
- Alerting strategies
- Distributed tracing
- Log aggregation
- Anomaly detection
- Root cause analysis
- Feedback loops
- SLOs for ML
- Incident response
Module 7. Data Governance and Compliance
Embed compliance, access control, and audit readiness into platform design without sacrificing developer velocity.
12 chapters in this module
- Data classification
- Role-based access control
- Audit trail requirements
- Data retention policies
- PII detection
- Consent management
- GDPR readiness
- HIPAA considerations
- SOC 2 alignment
- Data lineage
- Policy as code
- Automated enforcement
Module 8. Platform Developer Experience
Design intuitive interfaces, APIs, and tooling that reduce cognitive load and accelerate onboarding for data scientists and engineers.
12 chapters in this module
- Developer journey mapping
- Onboarding workflows
- CLI tool design
- API documentation
- Sandbox environments
- Template repositories
- Error messaging
- Feedback mechanisms
- Community building
- Internal support channels
- Self-service guides
- Knowledge sharing
Module 9. Cost Management and Optimization
Implement cost visibility, allocation, and optimization strategies across cloud data and ML workloads to ensure sustainable growth.
12 chapters in this module
- Cloud cost breakdown
- Resource tagging
- Cost attribution
- Budget alerts
- Spot instance usage
- Storage tiering
- Compute optimization
- Auto-pausing clusters
- Right-sizing models
- Query cost analysis
- Idle resource detection
- FinOps integration
Module 10. Security in Data Platforms
Integrate zero-trust principles, encryption, and threat modeling into data platform architecture to protect sensitive assets.
12 chapters in this module
- Zero-trust architecture
- Network segmentation
- Encryption at rest
- Encryption in transit
- Secrets management
- Threat modeling
- Vulnerability scanning
- IAM best practices
- Attack surface analysis
- Penetration testing
- Incident response plan
- Security audits
Module 11. Leading Platform Adoption
Drive cross-functional alignment and organizational buy-in for platform initiatives through communication, roadmap clarity, and value demonstration.
12 chapters in this module
- Stakeholder mapping
- Roadmap communication
- Pilot programs
- Feedback loops
- Change management
- Internal evangelism
- Success metrics
- Team enablement
- Documentation culture
- Support workflows
- Roadblock resolution
- Scaling adoption
Module 12. Future-Proofing Your Platform
Anticipate emerging trends in AI, data privacy, and compute infrastructure to ensure long-term relevance and adaptability.
12 chapters in this module
- AI ethics considerations
- Regulatory horizon scanning
- Quantum readiness
- Federated learning
- Differential privacy
- Synthetic data
- Green computing
- Open source strategy
- Vendor lock-in avoidance
- Modular architecture
- API-first design
- Platform extensibility
How this maps to your situation
- You're designing or evolving a data and ML platform
- You're leading a team that owns platform infrastructure
- You're scaling AI initiatives across multiple teams
- You're bridging engineering, data science, and product
Before vs. after
Before
Fragmented systems, slow deployment cycles, and governance gaps hinder platform impact.
After
A unified, scalable, and secure data and ML platform that accelerates innovation across the organization.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 45, 60 minutes per module, designed for busy engineering leaders to progress at their own pace.
If nothing changes
Without a structured approach, data and ML initiatives remain isolated, costly, and difficult to govern, limiting organizational impact and career growth for platform leaders.
How this compares to the alternatives
Unlike generic data engineering courses, this program is tailored for senior practitioners building platforms at scale, focusing on real-world architecture, cross-team dynamics, and operational excellence rather than isolated tools or tutorials.
Frequently asked
Who is this course for?
Senior engineers, platform leads, and ML architects building internal data and machine learning infrastructure at tech companies.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, upon finishing all modules and assessments, a digital certificate is issued.
$199 one-time. Approximately 45, 60 minutes per module, designed for busy engineering leaders to progress at their own pace..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours