Corporate MLOps & Production AI Engineering for the Banking & Financial Services Sector
Indonesian banks use AI for credit scoring, fraud detection, AML, and personalization. OJK increasingly attends to AI use that affects financial decisions, pushing transparency & accountability of models. Bank AI teams need MLOps pipelines that can answer auditor questions: which model, trained on which data, when last retrained, how is it performing, and is the logic explainable.
- format
- In-house / online / hybrid
- duration
- 4–5 intensive days or 3–4 month phased program
- participants
- 10–20 per cohort
- language
- Indonesian / English
Why Corporate MLOps & Production AI Engineering is different in Banking & Financial Services
Indonesian banks use AI for credit scoring, fraud detection, AML, and personalization. OJK increasingly attends to AI use that affects financial decisions, pushing transparency & accountability of models. Bank AI teams need MLOps pipelines that can answer auditor questions: which model, trained on which data, when last retrained, how is it performing, and is the logic explainable.
- Production model coverage with complete model cardsNearly all production models
- Drift detection to action MTTRSignificant improvement after monitoring matures
- OJK examination findings on AI governanceNo material findings in the next period
- POJK 11/POJK.03/2022 Commercial Bank IT Risk Management
- SEOJK 29/SEOJK.03/2022 Commercial Bank Cyber Resilience & Security
- UU PDP No. 27/2022 — customer data & automated profiling
- NIST AI RMF 1.0 — Govern/Map/Measure/Manage
- ISO/IEC 42001:2023 AI Management System
- Google Practitioners Guide to MLOps
- Bank Head of Data Science / AI
- Head of MLOps / ML Engineering
- Head of Credit Risk Modelling
- Head of Fraud Analytics
- CISO / Head of IT Risk
- Data Protection Officer (DPO)
- Reproducible credit scoring / fraud pipelines auditable by OJK
- Model cards for every production model (data, metrics, limits, fairness)
- Data drift, prediction drift, and business metric monitoring in place
- Data subject rights (access, correction, automated-decision opt-out) integrated into model lifecycle
- Model governance presented to risk committee / board using NIST AI RMF & ISO 42001
Quick Answer
MLOps & Production AI Engineering training is an in-house program equipping data, engineering, and AI teams to ship ML models to production reliably & accountably — feature store, model registry, drift monitoring, governance — mapped to the Google MLOps Practitioners Guide, Microsoft MLOps maturity, NIST AI RMF 1.0, and ISO/IEC 42001:2023.
Guided by industry-recognized MLOps frameworks
The material follows the Google Practitioners Guide to MLOps, Microsoft MLOps maturity model (levels 0–4), ML Test Score (Eric Breck & Google Research, 28 tests), NIST AI RMF 1.0 (4 functions Govern/Map/Measure/Manage), and ISO/IEC 42001:2023 (the first AI Management System standard). No ad-hoc frameworks.
Most corporate AI models never reach production
Without MLOps discipline, models that look great in notebooks often never reach production or reach it without monitoring. Industry research consistently shows many AI projects stall at proof-of-concept. Reproducible pipelines + model registry + drift monitoring are required so AI investment delivers sustained value.
Healthy adoption: 1 use case end-to-end > 5 half-done
Teams maturing MLOps typically pick 1 high-priority use case and run it end-to-end (data → training → registry → deploy → monitor → governance) as the internal paved-road, then copy the pattern to subsequent use cases. The module guides this pattern explicitly.
MLOps & Production AI Engineering
MLOps & Production AI Engineering is the engineering discipline that brings machine learning models to production reliably, repeatably, observably, and accountably — covering data pipelines, feature store, training, model registry, deployment, drift/performance monitoring, and model governance — mapped to the Google MLOps Practitioners Guide, the Microsoft MLOps maturity model (levels 0–4), the ML Test Score (Eric Breck), NIST AI RMF 1.0 (Govern/Map/Measure/Manage), and ISO/IEC 42001:2023 (AI Management System).
Measurable Outcomes
Expected Outcomes
Indicators mapped to Kirkpatrick levels and Microsoft MLOps maturity — qualitative targets, set during TNA against your team baseline.
- MLOps discipline mastery (Kirkpatrick L2 — Learning)
- Most participants pass pipeline assessment (data → feature → train → registry → deploy → monitor) and can read Google/Microsoft/AWS reference architectures
- Reproducible pipeline (L3 — Behavior)
- Team runs a reproducible training pipeline (versioned code, versioned data via DVC/LakeFS, hyperparameters & metrics logged in MLflow)
- Model registry & deployment (L3 — Behavior)
- One real use case promoted via model registry (staging → production) with auditable rollback
- Drift & performance monitoring
- Data drift, prediction drift, and production performance dashboards available for primary models; actionable alerts on threshold breach
- ML Test Score (Eric Breck) baseline
- Team maps position against the 28 ML Test Score tests (data/model/infra/monitoring) and builds a roadmap to close the gap
- Governance aligned with NIST AI RMF / ISO 42001
- Model cards, data sheets, and decision logs documented as auditable governance evidence
Program Format
Program Format Options
Chosen by team's MLOps maturity position — finalized after TNA.
MLOps Foundations Bootcamp (4–5 days)
Intensive bootcamp: data pipeline, feature store, reproducible training, model registry, deployment (batch/online), basic monitoring, and governance. Hands-on in lab environment.
Production AI Workshop (one real use case)
Consultative workshop: team brings one priority use case, facilitator guides from data pipeline → training → registry → deployment → monitoring, with ML Test Score as checklist.
MLOps Maturity Assessment & Roadmap
Assessment of position on Microsoft MLOps maturity 0–4 + ML Test Score; output: roadmap to close gaps, recommended tooling list, and investment ordering.
Recurring AI Governance Enablement
Recurring program for governance, compliance, and AI lead teams: cadence of model review, model cards, data sheets, drift monitoring, and reporting to leadership/regulator.
Free Consultation
Discuss your AI team's MLOps plan
Start with a free training needs analysis: we map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.
Curriculum
Curriculum Framework
Designed via ADDIE; final modules curated by platform (Vertex AI / Azure ML / SageMaker / on-prem), tooling, and maturity position from TNA.
Comparison
Choosing the Program Format
Concise decision matrix — final recommendation set after training needs analysis.
| Aspect | MLOps Foundations Bootcamp | Production AI Workshop (1 Use Case) | Maturity Assessment & Roadmap | Recurring AI Governance |
|---|---|---|---|---|
| Primary goal | MLOps discipline foundation | One real model to production | Maturity position & investment roadmap | Sustained AI governance discipline |
| Ideal participants | Teams new to AI production | Teams with model not in production | Data/AI leadership exploring | Governance team & multi-model |
| Typical duration | 4–5 intensive days | 2–3 day workshop | 1–2 week consulting | Monthly / quarterly |
| Main output | Fundamentals mastery + labs | Pipeline + registry + monitoring | Maturity position + roadmap | Governance cadence + model cards |
| Core framework | Google MLOps Guide + Microsoft maturity | ML Test Score + end-to-end use case | Microsoft maturity 0–4 + ML Test Score | NIST AI RMF + ISO/IEC 42001:2023 |
For Whom
Who This Program Is For
Designed by role because MLOps challenges differ for data scientist vs ML engineer vs governance.
Data Scientist
Teams building & training models for business use cases.
Common challenges
- Models look good in notebooks but never reach production; depends on ad-hoc handover
- Experiments untracked; reproducibility is weak; hyperparameters lost
- No visibility into production performance after deployment
ML Engineer / MLOps Engineer
Teams bridging data science with production.
Common challenges
- Pipelines manual; every retraining needs engineer intervention
- No feature store yet; training–serving skew happens often
- Drift & performance monitoring not standardized; alert noise high
Platform / Infrastructure Engineer
Teams providing platform for production models.
Common challenges
- No MLOps paved-road; each model team rolls its own pipeline
- GPU & inference resources not cost-allocated per team/model
- Artifact versioning & model rollback not standardized
AI / Data Governance Lead
Teams ensuring AI models comply with regulation & policy.
Common challenges
- No model cards & data sheets; hard to answer audits
- Not yet mapped to NIST AI RMF / ISO 42001
- Data subject rights (UU PDP) not integrated into model lifecycle
Tech / Data Lead / AI Executive
Owner of AI strategy and impact accountability.
Common challenges
- MLOps maturity position unknown; investment unfocused
- Many offline models that never deliver production value
- AI governance not yet presented to the board with formal framework
Industry Context
Industry Applications
One specific use case per industry, naming relevant models, regulations, and MLOps patterns.
MLOps pipeline for credit scoring, fraud detection, and customer propensity models with governance meeting POJK 11/POJK.03/2022 (bank IT), POJK on AI in credit decisions, and UU PDP for customer data — including model cards, audit trails, and ability to answer data subject rights.
MLOps platform for technology companies running many product models (recommendation, ranking, search, personalization, anti-abuse) — so data scientists can experiment fast without endangering production, with a consistent paved-road.
See in Technology & Startups context →AI model governance across BUMN holding subsidiaries with NIST AI RMF & ISO/IEC 42001:2023 framework, standard model cards, and impact reporting accountable to the board & BPK.
See in State-Owned Enterprises (BUMN) context →MLOps pipeline for predictive maintenance, quality inspection (computer vision), and demand forecasting models at the plant — with plant data integration (PLC/SCADA) and models that can run at the edge when latency is critical.
See in Manufacturing context →MLOps platform for demand forecasting, dynamic pricing, recommendation, and churn prediction models in modern retail — with seasonal retraining cadence (Lebaran, Harbolnas) and drift monitoring actionable by the merchandising team.
See in Retail & FMCG context →MLOps pipeline for clinical models (early detection, triage, outcome prediction) at hospitals & health networks — with staged clinical validation, strong governance (specific personal data UU PDP), and KARS readiness for information governance.
See in Healthcare & Pharmaceuticals context →Delivery Method
Delivery
Format adapts to your data & engineering team distribution; all formats hands-on in lab environment + your real use case.
On-site intensive & workshop
Facilitator comes to your office for a 4–5 day bootcamp; labs in your existing environment (Vertex AI / Azure ML / SageMaker / on-prem) with case studies from one priority use case.
Live online + managed labs
Interactive classes via Zoom/Teams; labs run in cloud sandboxes provided by Neksus or your internal accounts with limited scope.
Hybrid
On-site for consultative modules (Maturity Assessment, Governance), online for technical modules — suits multi-location teams.
Engagement Flow
Engagement Path
Follows ADDIE + Google MLOps Practitioners Guide — qualitative durations, scaled to maturity position.
Training Needs Analysis & Maturity Position
Mapping platform (Vertex AI / Azure ML / SageMaker / on-prem), roles, Microsoft MLOps maturity position, ML Test Score baseline, priority use cases, and regulatory obligations.
Initial stageProgram Design by Role (ADDIE)
Drafting measurable learning objectives, role-based syllabi (data scientist/ML engineer/MLOps/governance), lab scenarios, and framework map to NIST AI RMF, ISO 42001.
Pre-deliveryMLOps Foundations Bootcamp
Core 4–5 day session: data pipeline, feature store, reproducible training, registry, deployment, basic monitoring, governance. Hands-on in your environment.
Core weekProduction AI Workshop — One Use Case
Consultative workshop: team brings one priority use case, guided end-to-end (data → training → registry → deploy → monitor) with ML Test Score as checklist.
Post-bootcampMaturity Assessment & Governance Enablement
Assessment of position on Microsoft MLOps maturity & ML Test Score; institutionalization of NIST AI RMF + ISO 42001 governance cadence (model cards, data sheets, periodic review).
Rolling per quarterEvaluation & Maturity Roadmap
Kirkpatrick L1–L4 evaluation (Phillips L5 on request), remapping maturity position, and roadmap for closing the next gap.
Recurring & continuousCase Studies
Typical Outcome Patterns
Illustrative patterns based on similar program structures — no named clients or promised numbers. Microsoft MLOps maturity, ML Test Score, NIST AI RMF and ISO/IEC 42001 are attributed as external sources (Microsoft, Google Research, NIST, ISO).
Financial institution with many credit scoring & fraud models
Intervention
Bootcamp + one real use case workshop + NIST AI RMF governance framework
Result
Reproducible pipelines with complete model cards, active drift monitoring, and governance reportable to OJK
Technology company with many customer-facing product models
Intervention
Maturity assessment + paved-road MLOps platform + recurring governance cadence
Result
Average time from notebook → production decreased and governance posture uniform
Manufacturer with multi-plant predictive maintenance initiative
Intervention
Bootcamp + edge workshop + retraining trigger + data sheet
Result
PdM models active at core plants, drift monitored, and unplanned downtime consistently decreasing
Procurement Info
Information for Procurement & Vendor Management
What procurement, finance, legal, and AI governance teams need.
Indonesian PT under the Selestia ecosystem (Eduprima group); complete NPWP & legal documents; ready for PKS/contracts and vendor onboarding.
Structured proposal: measurable learning objectives, role-based syllabus, framework map (Google MLOps Practitioners Guide / Microsoft MLOps maturity 0–4 / ML Test Score / NIST AI RMF 1.0 / ISO/IEC 42001:2023 / UU PDP / POJK), facilitator profile, schedule, and TNA-based cost detail.
TNA-based — flat per program, per session, per participant, tiered, or custom. Estimate issued after TNA is agreed.
Flexible terms (DP + balance / per-batch installments); tax invoice (PPN) and PO documentation supported.
Familiar with BUMN/government procurement: vendor documentation, e-procurement / SPSE, HPS/offers, and compliance clauses.
Kirkpatrick L1–L3 evaluation reports (attendance, knowledge assessment, lab + real use case behavior); Phillips ROI L5 on finance/risk request.
NDA signing, confidentiality of use cases & internal data used as case studies, and practices aligned with UU PDP and your internal security policy.
Model cards, data sheets, pipeline references, and documents built for your company are yours; usage rights of training materials are agreed in the contract.
FAQ
Frequently Asked Questions
Next Step
Discuss your AI team's MLOps plan
Start with a free training needs analysis: we map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.
- Training needs analysis at no cost — the natural first step
- Proposal, role-based syllabus, and framework map (Google MLOps / Microsoft MLOps / NIST AI RMF / ISO 42001) within a few business days
- Labs in your environment with real use cases; sandbox if needed
- Procurement-ready documents (company profile, NPWP, NDA, PPN tax invoice)
Corporate MLOps & Production AI Engineering training for your Banking & Financial Services team
Start with a free training needs analysis: we map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.
- Training needs analysis at no cost — the natural first step
- Proposal, role-based syllabus, and framework map (Google MLOps / Microsoft MLOps / NIST AI RMF / ISO 42001) within a few business days
- Labs in your environment with real use cases; sandbox if needed
- Procurement-ready documents (company profile, NPWP, NDA, PPN tax invoice)