A tailored course, built for your situation
Final call on ML architecture decisions, no senior review
Own the architectural direction of machine learning systems with documented authority and peer-trusted judgment
$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.
Who this is for
Senior IC in machine learning or data engineering at a product-led tech company, operating at Staff+ level with responsibility for high-visibility system design
Who this is not for
Engineers looking to transition into people management, or those focused exclusively on research prototyping without deployment scope
What you walk away with
- Decision-ready frameworks for model, pipeline, and infra choices specific to your stack
- Templates for documenting architecture decisions that stand up to peer review
- Patterns to identify which calls are yours to make, and which to escalate
- Tactics to build consensus before the meeting, so sign-off is ceremonial
- Precedent library of real-world ML architecture decisions with justification patterns
The 12 modules (with all 144 chapters)
Module 1. Defining your decision boundary
Map which ML architecture decisions fall within your scope based on impact, reusability, and risk profile. Identify current ambiguity and clarify ownership thresholds.
12 chapters in this module
- What counts as architecture
- Staff-level vs. senior staff scope
- Impact threshold for autonomy
- When infra choices become product decisions
- Ownership patterns at top tech firms
- Reviewing escalation paths
- Identifying soft dependencies
- Mapping org decision rights
- Documenting your mandate
- Negotiating scope expansion
- Setting decision precedence
- Tracking boundary drift
Module 2. Model selection authority
Take control of algorithm and model choices with confidence, using structured evaluation criteria that preempt debate and align stakeholders.
12 chapters in this module
- When to build vs. fine-tune
- Evaluating foundation models
- Latency vs. accuracy tradeoffs
- Choosing open-source models
- Versioning model decisions
- Handling model risk tiers
- Documenting fitness for use
- Benchmarking internally
- Justifying non-standard picks
- Peer review triggers
- Revisiting past decisions
- Setting model deprecation rules
Module 3. Pipeline design ownership
Own the structure of data flow and transformation logic, making final calls on modularity, reprocessing needs, and observability design.
12 chapters in this module
- Monolithic vs modular pipelines
- Defining retry logic standards
- Ownership of schema changes
- Setting SLAs for freshness
- Choosing orchestration tools
- Error handling ownership
- Data lineage requirements
- Backfill approval process
- Pipeline testing thresholds
- Cost control decisions
- Observability depth
- Decoupling pipeline stages
Module 4. Infra integration decisions
Make binding choices about where and how models deploy, including serving platforms, autoscaling rules, and dependency contracts.
12 chapters in this module
- On-prem vs cloud serving
- Choosing serverless options
- GPU allocation rules
- Batch vs real-time tradeoffs
- Cold start tolerance
- Model caching strategies
- Dependency pinning
- Version rollout patterns
- Traffic shadowing rules
- Rollback ownership
- SLO ownership
- Capacity planning inputs
Module 5. Documentation that commands trust
Create architecture decision records that reduce scrutiny cycles and establish your judgment as the reference point.
12 chapters in this module
- ADR structure best practices
- Capturing context succinctly
- Linking to business impact
- Including failed options
- Stating assumptions clearly
- Versioning ADRs
- Making ADRs discoverable
- Referencing past decisions
- Updating outdated records
- Sign-off as formality
- Using ADRs in onboarding
- Measuring ADR reuse
Module 6. Pre-consensus building
Align key stakeholders before formal reviews so approval becomes a confirmation, not a negotiation.
12 chapters in this module
- Identifying hidden stakeholders
- Timing informal reviews
- Tailoring technical depth
- Using prototypes effectively
- Sharing early drafts
- Routing to champions
- Handling objections early
- Setting meeting expectations
- Incorporating feedback visibly
- Closing loops pre-meeting
- Reducing meeting time
- Building decision momentum
Module 7. Handling exceptions and edge cases
Navigate novel situations where precedent doesn’t exist, and turn them into future reference points.
12 chapters in this module
- When patterns break down
- Assessing novelty level
- Running time-boxed spikes
- Consulting vs deciding
- Escalation thresholds
- Documenting one-offs
- Generalizing from exceptions
- Flagging for standardization
- Tracking edge case frequency
- Deciding whether to codify
- Involving security early
- Preserving flexibility
Module 8. Calibrating with tech leads
Maintain alignment with adjacent leads without ceding ownership, position your role as the integrator, not the approver.
12 chapters in this module
- Mapping peer domains
- Defining integration contracts
- Handling overlapping scope
- Scheduling syncs
- Sharing roadmaps proactively
- Resolving conflicting priorities
- Co-owning cross-cutting issues
- Setting escalation paths
- Clarifying joint decisions
- Maintaining technical cohesion
- Communicating tradeoffs
- Building mutual trust
Module 9. Setting precedent intentionally
Turn one-off decisions into lasting standards that shape future work across teams.
12 chapters in this module
- Recognizing precedent moments
- Elevating decision visibility
- Writing for reuse
- Teaching through examples
- Presenting to wider groups
- Encouraging adoption
- Measuring influence
- Updating team playbooks
- Sponsoring replication
- Handling deviations
- Archiving retired patterns
- Celebrating adoption
Module 10. Managing technical debt calls
Own the judgment on when to incur, defer, or pay down debt, without waiting for permission.
12 chapters in this module
- Classifying debt severity
- Short-term vs long-term tradeoffs
- Documenting known gaps
- Setting repayment triggers
- Prioritizing cleanup work
- Balancing feature velocity
- Communicating debt decisions
- Tracking debt metrics
- Involving junior engineers
- Using debt in planning
- Revisiting past compromises
- Setting team norms
Module 11. Influencing adjacent domains
Extend your reach into data engineering, product, and security by making decisions that naturally pull others in.
12 chapters in this module
- Anticipating downstream effects
- Designing for extensibility
- Setting input contract rules
- Influencing schema design
- Guiding monitoring setup
- Shaping feature definitions
- Collaborating on risk reviews
- Partnering on compliance
- Defining success metrics
- Coordinating release plans
- Sharing ownership models
- Building cross-team trust
Module 12. Sustaining decision authority
Ensure your command persists over time, even as teams and systems evolve.
12 chapters in this module
- Onboarding new members
- Transferring decision knowledge
- Updating documentation
- Reviewing past choices
- Measuring decision outcomes
- Learning from failures
- Adjusting scope
- Maintaining credibility
- Teaching decision frameworks
- Scaling judgment
- Evolving with the stack
- Renewing mandate
How this maps to your situation
- Defining scope and ownership
- Making high-impact technical calls
- Reducing rework through documentation
- Extending influence across teams
Before vs. after
Before
Decisions bottlenecked by review cycles, inconsistent documentation, and unclear ownership boundaries.
After
You make final calls on ML architecture with confidence, backed by clear reasoning and peer-trusted records.
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 3-4 hours per module, with self-paced progress tracking and implementation checkpoints.
How this compares to the alternatives
Unlike generic courses on ML engineering or leadership, this program focuses exclusively on the decision-making authority of Staff+ ICs, what you own, how you justify it, and how you scale your judgment.
Frequently asked
Is this about becoming a manager?
No. This is for senior individual contributors who lead through technical ownership, not people management.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work if my company doesn’t use the same tools?
Yes. The focus is on decision frameworks, not specific tools, apply the reasoning to any stack.
$199 one-time. Approximately 3-4 hours per module, with self-paced progress tracking and implementation checkpoints..
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