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Technology & Startups Sector

AI Models in Production Training for the Technology & Startups Sector

Technology companies often have dozens of production models (recommendation, ranking, anti-abuse, personalization) directly interacting with users. Without an MLOps paved-road, each team builds its own pipeline, monitoring is fragmented, and retraining decisions are ad-hoc. A curated MLOps platform provides shared feature store, central model registry, and consistent monitoring, accelerating iteration without sacrificing production quality.

  • 4.9/5 · 287 reviews
  • 300+ clients nationwide
  • 2,000+ trainers
  • 1 to hundreds of participants
  • Standard · Premium · Private 1-on-1
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format
In-house / online / hybrid
duration
5 intensive days per cohort
participants
10–20 per cohort
language
Indonesian / English

2,000+ trainers and experts

Our group's teaching network, matched to what your team needs.

See class documentation

You see your trainer first

The trainer's profile, track record and field come with the proposal, before you decide.

4.9 out of 5

287 participant reviews.

300+ clients nationwide and 180,000+ private training sessions completed. Among them: MPR RI, Akademi Militer Magelang, DPRD Provinsi Jawa Timur.

One person to hundreds

Private coaching for one participant, a class for one team, or several cohorts at once.

Three budget levels

  • Standard: core class, material fitted to the participants’ work
  • Premium: senior trainer, deeper material design
  • Private 1-on-1: one participant, one trainer, schedule set by the participant
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Technology & Startups Sector Focus

Why Taking AI Models into Everyday Use is different in Technology & Startups

Technology companies often have dozens of production models (recommendation, ranking, anti-abuse, personalization) directly interacting with users. Without an MLOps paved-road, each team builds its own pipeline, monitoring is fragmented, and retraining decisions are ad-hoc. A curated MLOps platform provides shared feature store, central model registry, and consistent monitoring, accelerating iteration without sacrificing production quality.

Sector KPIs
  • Average time from model training → production
    Significant decrease after paved-road is available
  • Production model coverage with drift monitoring
    Nearly all primary models
  • Model-related production incidents (performance regression)
    Significant decrease after closing ML Test Score gaps
Relevant regulations & standards
  • ISO/IEC 42001:2023 AI Management System
  • NIST AI RMF 1.0
  • UU PDP No. 27/2022 — platform user data
  • OWASP LLM Top 10 2025 — for LLM components
  • ML Test Score (Eric Breck) production quality criteria
Target roles in Technology & Startups
  • VP Engineering / Head of AI
  • Head of Data Science
  • Head of MLOps / AI Platform
  • Senior ML Engineer
  • Data Engineering Lead
  • AI Governance Lead
Outcomes commonly requested in Technology & Startups
  • Curated internal MLOps platform: feature store, registry, monitoring
  • Data scientists experiment fast on the paved-road
  • ML Test Score baseline established; gaps prioritized
  • Drift monitoring & automated retraining triggers for primary models
  • AI governance aligned with NIST AI RMF & ISO 42001 for audit / customer
Technology & Startups-specific questions
How to choose managed (Vertex AI / SageMaker / Azure ML) vs self-managed platform?
The module provides criteria: team complexity, existing cloud ecosystem, feature store integration, compliance needs, and cost. Most Indonesian technology companies are more efficient on managed; self-managed makes sense when custom needs are large or on-prem regulator.
Does the module cover LLMOps for generative AI?
Yes. A dedicated LLMOps module covers RAG, prompt versioning, evaluation harness (Ragas/LangSmith), token cost monitoring, and LLM governance (OWASP LLM Top 10 + NIST AI RMF Generative AI Profile).
How does the MLOps platform handle training–serving skew?
The module teaches feature store as a single source for offline & online features (reducing skew by design), schema validation before training, and feature value logging at inference for debugging skew.

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.

Free Consultation

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.

Clients & Documentation

Some of the institutions we have trained

Photos from sessions we have delivered, each one named after the institution it belongs to.

See class documentation (123 photos)

Taking AI Models into Everyday Use

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).

1Designed via training needs analysis (TNA): roles (data scientist, ML engineer, MLOps engineer, platform), priority use case, maturity position
2Mapped explicitly to the Google Practitioners Guide to MLOps & Microsoft MLOps maturity model (level 0 ad hoc → level 4 fully automated)
3Hands-on with mature tooling: Vertex AI / Azure ML / SageMaker, MLflow, Feast feature store, Kubeflow Pipelines, Airflow
4Production quality tested against ML Test Score (Eric Breck): 28 tests across data/model/infra/monitoring
5Model governance aligned with NIST AI RMF 1.0 and ISO/IEC 42001:2023 + UU PDP No. 27/2022 and POJK/OJK obligations where relevant
6Measurable output: paved-road pipeline + model registry + drift dashboard for one real internal use case
7References we work from: the Google MLOps Practitioners Guide, the Microsoft MLOps maturity model, ISO/IEC 42001:2023, and the ML Test Score (Eric Breck) for gauging system readiness.

Measurable Outcomes

Expected Outcomes

Indicators mapped to Kirkpatrick levels and Microsoft MLOps maturity: qualitative targets, set during TNA against your team baseline.

MLOps discipline mastered across the team
Most participants pass pipeline assessment (data → feature → train → registry → deploy → monitor) and can read Google/Microsoft/AWS reference architectures. Assessed through a check of the model workflow closing the class (Kirkpatrick L2).
Pipelines re-run with the same result
Team runs a reproducible training pipeline (versioned code, versioned data via DVC/LakeFS, hyperparameters & metrics logged in MLflow). Seen when a pipeline is re-run and returns the same result (Kirkpatrick L3).
Models catalogued and ready to release
One real use case promoted via model registry (staging → production) with auditable rollback. Visible in the model version records the team tidies up (Kirkpatrick L3).
Model quality slips get caught early
Data drift, prediction drift, and production performance dashboards available for primary models; actionable alerts on threshold breach.
System readiness is measurable
Team maps position against the 28 ML Test Score tests (data/model/infra/monitoring) and builds a roadmap to close the gap.
Model governance is in order
Model cards, data sheets, and decision logs documented as auditable governance evidence. The baselines are NIST AI RMF 1.0 and ISO/IEC 42001:2023.

Program Format

Program Format Options

Chosen by team's MLOps maturity position, finalized after TNA. A single model can be carried to the point of real use inside those five days. Organizations wanting many models running side by side normally carry on through a three to four month phased programme while practice maturity improves.

1

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.

Best for: Data/ML teams targeting Microsoft maturity level 1–2 (ad hoc → automated training)
2

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.

Best for: Teams that have a working model but don't yet know how to get it to accountable production
3

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.

Best for: Data/AI leadership wanting to know starting position before big investment
4

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.

Best for: Heavy-regulation organizations (bank, healthcare, BUMN) with many production models

Free Consultation

Discuss your AI team's MLOps plan

We map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.

Request Proposal

Curriculum

Curriculum Framework

Designed via ADDIE; final modules curated by platform (Vertex AI / Azure ML / SageMaker / on-prem), tooling, and maturity position from TNA.

  • MLOps vs DevOps: definition & scope; why models are living artifacts that depend on changing data
  • Google MLOps Practitioners Guide: reference architecture pipeline + core components
  • Microsoft MLOps maturity model: level 0 (no MLOps) → 4 (fully automated retraining)
  • ML Test Score (Eric Breck): 28 tests across data/model/infra/monitoring as production quality standard
  • Mature tooling: Vertex AI / Azure ML / SageMaker + open-source (MLflow, Kubeflow, Feast, Airflow)

  • Data versioning (DVC, LakeFS) and schema validation (Great Expectations, TFDV)
  • Pipeline orchestration: Airflow, Kubeflow Pipelines, Prefect, Dagster
  • Feature store: Feast (open-source), Tecton, Vertex AI Feature Store (offline vs online)
  • PII handling in feature store: masking, encryption, role-based access (UU PDP)
  • Common anti-patterns: training–serving skew, label leakage, undetected feature drift

  • Logged experiments: MLflow Tracking + parameters, metrics, artifacts, model versioning
  • Hyperparameter tuning: Optuna, Ray Tune, Vertex AI Vizier
  • Reproducibility: environment pinning (Conda/Poetry), seeds, dataset hash
  • Model registry: MLflow Model Registry / Vertex AI / Azure ML (staging vs production)
  • Candidate selection: offline metric vs online A/B test; canary model release

  • Batch inference: BigQuery ML, Spark, scheduled jobs; cost & latency
  • Online inference: FastAPI / TorchServe / TensorFlow Serving + Kubernetes
  • Streaming inference: Kafka + ksqlDB / Flink; low-latency patterns
  • Edge inference: model quantization, ONNX, TensorRT (when it makes sense)
  • Canary, blue-green, shadow deployment: when each applies

  • Data drift: KS test, PSI, evidently AI, Arize, Fiddler (when alerts are actionable)
  • Concept drift: performance degradation from a changing world; retraining triggers
  • Production model performance: ground truth lag, proxy metrics, business metric tie-in
  • Bias & fairness monitoring: demographic groups, disparate impact, fairlearn
  • Observability: Prometheus + Grafana + prediction tracing; actionable alerts

  • NIST AI RMF 1.0 (2023): 4 functions Govern/Map/Measure/Manage + AI RMF Playbook
  • ISO/IEC 42001:2023, the first international AI Management System standard
  • Model cards (Google style) + data sheets for datasets (Gebru et al.)
  • UU PDP No. 27/2022 for models processing personal data; data subject rights
  • Sectoral regulations (POJK, OJK) on AI use in credit/customer-facing decisions

  • LLMOps vs MLOps: differences and equivalents (prompt as 'code', structured evaluation)
  • Retrieval-augmented generation (RAG): index, retriever, evaluator, telemetry
  • Prompt versioning, evaluation harness (Ragas, OpenAI Evals, LangSmith), guardrails
  • Model & cost monitoring for LLM (token cost, latency, cache hit rate)
  • LLM governance: OWASP LLM Top 10 2025, NIST AI RMF Generative AI Profile

Comparison

Choosing the Program Format

Concise decision matrix. Final recommendation set after training needs analysis.

AspectMLOps Foundations BootcampProduction AI Workshop (1 Use Case)Maturity Assessment & RoadmapRecurring AI Governance
Primary goalMLOps discipline foundationOne real model to productionMaturity position & investment roadmapSustained AI governance discipline
Ideal participantsTeams new to AI productionTeams with model not in productionData/AI leadership exploringGovernance team & multi-model
Typical duration4–5 intensive days2–3 day workshop1–2 week consultingMonthly / quarterly
Main outputFundamentals mastery + labsPipeline + registry + monitoringMaturity position + roadmapGovernance cadence + model cards
Core frameworkGoogle MLOps Guide + Microsoft maturityML Test Score + end-to-end use caseMicrosoft maturity 0–4 + ML Test ScoreNIST 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.

Banking & Financial Services

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.

See in Banking & Financial Services context →
Technology & Startups

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.

State-Owned Enterprises (BUMN)

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 →
Manufacturing

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 →
Retail & FMCG

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 →
Healthcare & Pharmaceuticals

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).

Scheduling fits team release & on-call calendar
Materials & labs localized to your platform (Vertex AI / Azure ML / SageMaker / on-prem MLflow + Kubeflow)
Your internal use case used as case study (NDA applies)
Participation certificate + position map against Microsoft MLOps maturity & ML Test Score
Evaluation report & gap-closing roadmap for data/AI leadership

Engagement Flow

Engagement Path

Follows ADDIE + Google MLOps Practitioners Guide: qualitative durations, scaled to maturity position.

1

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 stage
2

Program 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-delivery
3

MLOps Foundations Bootcamp

Core 4–5 day session: data pipeline, feature store, reproducible training, registry, deployment, basic monitoring, governance. Hands-on in your environment.

Core week
4

Production 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-bootcamp
5

Maturity 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 quarter
6

Evaluation & Maturity Roadmap

Kirkpatrick L1–L4 evaluation (Phillips L5 on request), remapping maturity position, and roadmap for closing the next gap.

Recurring & continuous

Case 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.

Proposal

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.

Pricing model

TNA-based: flat per program, per session, per participant, tiered, or custom. Estimate issued after TNA is agreed.

Payment & tax

Flexible terms (DP + balance / per-batch installments); tax invoice (PPN) and PO documentation supported.

BUMN/government procurement

Familiar with BUMN/government procurement: vendor documentation, e-procurement / SPSE, HPS/offers, and compliance clauses.

Measurement

Kirkpatrick L1–L3 evaluation reports (attendance, knowledge assessment, lab + real use case behavior); Phillips ROI L5 on finance/risk request.

Confidentiality & data security

NDA signing, confidentiality of use cases & internal data used as case studies, and practices aligned with UU PDP and your internal security policy.

Material ownership

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

DevOps manages the software lifecycle (code → build → deploy → monitor). MLOps adds data & model dimensions: data versioning, reproducible training, model registry, production drift and performance monitoring, and model governance. Models are living artifacts dependent on changing data, so they need more discipline than DevOps.

Microsoft's MLOps maturity model classifies organizations from level 0 (no MLOps, manual end-to-end) to level 4 (full MLOps automation, automated retraining). Most Indonesian corporates are at levels 1–2: training pipeline exists, but deployment is still semi-manual, drift monitoring limited. Realistic 1-year target: level 2 → 3 (automated deployment with model registry). The module provides a roadmap for closing gaps per level.

ML Test Score is a 28-test rubric proposed by Eric Breck & Google Research to assess production ML system readiness: data tests (schema, distribution, values), model (sanity, infrastructure), infrastructure (reproducibility, integration), and monitoring (drift, freshness, latency). Corporates use it as a production quality checklist. Without a clear score, it's hard to judge when a model is 'production-ready'.

Depends on existing cloud ecosystem, tooling integration, team complexity, and compliance needs. Managed onboards faster, less operational burden. Self-managed (Kubeflow / MLflow + Kubernetes) makes sense for full control / on-prem regulator. The module provides concrete selection criteria based on your case.

A feature store is a system that stores, serves, and versions ML features for training (offline) and inference (online). Needed when many models share features (reducing duplication & training–serving skew), or when regulation requires feature lineage tracing. For small teams with one use case, start with pipeline discipline; feature store is added as scale grows.

Three monitoring layers: (1) data drift: input distribution changes vs training (KS test, PSI); (2) prediction drift: output distribution shifts; (3) model performance: metric vs ground truth when available. Add fairness monitoring across sensitive segments. Alerts must be actionable (connected to retraining/rollback runbook). Dashboards alone are insufficient.

The module teaches mapping of NIST AI RMF's 4 functions (Govern/Map/Measure/Manage) to internal processes, plus documentation required by ISO/IEC 42001:2023 (the first AI Management System standard). Practical output: model cards, data sheets, decision logs, and AI risk framework presentable to the board/risk committee.

Yes. The dedicated LLMOps module covers differences from classical MLOps (prompt as 'code', structured evaluation), RAG (retrieval-augmented generation), prompt versioning, evaluation harness (Ragas, LangSmith, OpenAI Evals), token cost monitoring, and LLM governance (OWASP LLM Top 10 2025, NIST AI RMF Generative AI Profile).

A 4–5 day bootcamp for foundations + 2–3 day workshop for one real use case. For deeper transformation, a 3–4 month phased program: bootcamp → use case workshop → maturity assessment → governance enablement. Ideal batch size is 10–20 participants so labs remain effective.

We use Kirkpatrick: Level 1 (reaction), Level 2 (knowledge via MLOps & governance assessment), Level 3 (behavior: pipelines & model cards produced), Level 4 (results: time-to-production decreased, drift detected fast, audit-readiness). Phillips ROI Level 5 on finance request with estimated model-incident avoidance and regulator readiness. Baseline is set during TNA.

The first determinant is whether the class works on your own models and data, since that requires the environment, data access approvals, and masking of sensitive information before the class starts. Seniority follows, because data scientists, engineers, and operations people hold different roles in the same flow. Then follow data team headcount, cohort numbers, meeting numbers, the compute resources the lab needs, sessions run at your own desks or remotely, plus whichever city hosts them. We build the figure once our training needs analysis workshop ends.

The lab environment is shared by 10 to 20 participants, and a twenty-strong group remain one cohort, and five full days already covers pipelines, the model catalogue, and monitoring. Preparation time is set by the lab environment and data access approvals. If the goal is genuinely lifting one model into daily use, allow extra weeks after the class, because fitting it to the systems already running at your place follows a rhythm of its own.

You can. This class needs an instructor who has genuinely carried models into real use, because the awkward questions live exactly there. Mention it to the programme coordinator; a substitute validated AI engineering instructor then steps in. Lab modules, the practice environment build, and data team progress notes are kept within our operations system, which spares the class from redoing the setup.

Next Step

Discuss your AI team's MLOps plan

We map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.

  • Proposal, role-based syllabus, and framework map (Google MLOps / Microsoft MLOps / NIST AI RMF / ISO 42001)
  • Labs in your environment with real use cases; sandbox if needed
  • Procurement-ready documents (company profile, NPWP, NDA, PPN tax invoice)
Request a ProposalFree Consultation

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Taking AI Models into Everyday Use training for your Technology & Startups team

We map platform, roles, maturity position, and priority use cases, then build a proposal and budget based on real needs.

  • Proposal, role-based syllabus, and framework map (Google MLOps / Microsoft MLOps / NIST AI RMF / ISO 42001)
  • Labs in your environment with real use cases; sandbox if needed
  • Procurement-ready documents (company profile, NPWP, NDA, PPN tax invoice)

Six fields to get started. Our corporate team reads and answers it.

Additional details (optional)

Add these if you already know them. Every field in this section can be left empty, and our team will cover them during the needs-analysis call.