ML & DECISION OPS ASSET

Go from one to a hundred ML models without reinventing MLOps

A framework that glues your existing tooling together, so your team builds, deploys, and monitors models at speed with the control critical infrastructure demands.

TRUSTED BY CLIMATE INNOVATORS

THE BUSINESS PROBLEM

How to crack ML & Decision Ops without reinventing the wheel?

Many tools support the process, but keep the core friction

Keeping all artifacts in sync across environments, models, and data scientists is the key challenge. Data is different in dev vs prod. One DS has a different code version than the other. The process breaks and the velocity stalls.

Optimization algorithms are separated from forecasting

ML Ops scales ML models, but is not made for simulations. When steering energy assets, optimizing routing, or taking decisions based on forecasts in general, the two algorithms are built and evaluated in silos, resulting in underperformance.

Maturing your ML ops practice takes time. Too much time.

Every organization is gradually and slowly making steps in how to go from 1 to 10 to 100 models. And bit by bit adding features to build, deploy, and monitor their ML models. What if you can be feature complete from day one?

THE SOLUTION

Production-grade MLOps for energy & logistics forecasting and decision algorithms

A Python-based framework that glues your existing MLOps, data ops, and simulation tooling into one consistent process: from experimentation, to deployment, to monitoring, back to automated retraining.

ml-decision-ops

ops run experiment.yaml

Spins up an experimentation environment in seconds, with the data and the freedom a data scientist actually needs.

→

Produces: an experiment record — code, data and hyperparameters versioned together.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

ml-decision-ops

no CLI step — runs in your CI/CD

Every run is packaged with the exact code, data and hyperparameters that produced it, so it rebuilds or replays later with no manual reconstruction — no separate "make it production ready" rewrite, and no drift between what you validated and what trains.

→

Produces: a versioned training pipeline, ready to run in CI/CD.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

ml-decision-ops

ops retrain --tag production

Retrains the live model on a new data window, on a schedule. The same command a data scientist types by hand is the one a CI system or an ML agent triggers.

→

Produces: a new model candidate, with lineage back to the exact data and code that trained it.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

ml-decision-ops

ops verify <experiment-id>

ops promote <model-id> --to production

Reproduces the run and checks the candidate against the current champion before anything ships, then turns it into a tagged, deployable artifact — governance gates for compliance, traceability and reproducibility built into the promotion step, not bolted on after.

→

Produces: a packaged model in the target environment.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

ml-decision-ops

ops serve --tag production

model_registry.load(<model-id>)

Stands up a live endpoint, or embeds the same model directly in your own service or batch pipeline. API or batch, it is the artifact you validated.

→

Produces: predictions plus a prediction log you can audit and reuse for retraining.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

ml-decision-ops

no CLI step — runs in your CI/CD

OpenTelemetry traces and metrics feed into the infra you already run, and every prediction is logged for audit and drift analysis — watching the decision the model drives, not accuracy in isolation.

→

Produces: drift and decision signals that inform the next retrain. The loop closes here.

WE ENSURE ALL ARTIFACTS ARE VERSIONED AND IN SYNC

Data & features

Code & config

Training pipeline

Registered model

Serving package

Prediction log

GLUES INTO THE TOOLING YOU ALREADY RUN

WHY IT WORKS

Built to mature fast without the manual rework

Every artifact stays in sync, end to end

What you validated in your experiment is exactly what runs in production: the same data, the same code, the same training pipeline, the same model, the same packaging. Across environments and across models. Whether it serves through an API or a batch pipeline, it is the run you signed off on.

Decisions, not just forecasts

ops verify runs a DecisionEvaluator alongside the model statistics, so a candidate is judged on the decision it drives, not on accuracy in isolation, increasing overall business performance. Next to that, decision algorithms can be iterated on with the same speed and control as forecasting algorithms.

Mature your ML & Decision Ops easily

We ensure a mature setup from day one: monitoring on all models, reproducibility by design, auto retraining to keep speed, and ensuring compliance from day one. We integrate with all toolings and utilize them for what they do best.

Built for ML agents, not just people

Every step is one typed command with a stable interface, so an ML agent can chain ops run → ops verify → ops promote exactly as a person would.

OUR PARTNER

Keep control of all the ML models in your organization

Our partner helps you build and manage your ML operations. In regulated environments, you also need visibility into all the models being used across your organization and who is accountable for them.

One registry for every model and agent, across platforms

Built-in control frameworks (ISO 42001, NIST AI RMF, EU AI Act)

Continuous monitoring, explainability and automatic audit trails

Deeploy is an AI governance platform used in regulated sectors. It registers and monitors AI systems at runtime.

From initial prototype to scalable production systems

100+ companies trusted us to build their data & AI layer since 2018

Shypple

Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.

Mobility & Transport

LLM Systems

Next Sense

Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.

Buildings & Industry

LLM Systems

Beyond Weather

AI infrastructure to commercialize complex weather forecasting models from research to production environment.

Climate Intelligence

SnappCar

New ML Ops setup delivers 50% faster queries and significant cost reduction for peer-to-peer car-sharing platform.

Mobility & Transport

ML Systems

Groendus

Build an AI forecasting system for Groendus that outperforms the benchmark for energy consumption.

Energy & Electrification

Data Systems

CtrlChain

Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.

Mobility & Transport

ML Systems

Next Sense

Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.

Buildings & Industry

LLM Systems

Shypple

Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.

Mobility & Transport

LLM Systems

CtrlChain

Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.

Mobility & Transport

ML Systems

Sendcloud

Aligning sales, marketing, and customer success around a single ML-driven metric to unlock sustainable growth at scale.

Mobility & Transport

ML Systems

Beyond Weather

AI infrastructure to commercialize complex weather forecasting models from research to production environment.

Climate Intelligence

Crisp

Implement a new forecasting system for Crisp that improves prediction accuracy (WAPE) by >20%.

Other

ML Systems

QUESTIONS

Frequently asked questions

Why can't I use a managed tool like Databricks or Vertex AI for this?

You can use heavy tools like these, but they are a big investment and hard to move away from once you start. We recommend them only for genuinely complex setups — and we are happy to advise when that is the right call.

What do I own after this?

You get the Python code. It is readable and easy to adjust, so you can own it yourself and stay in control.

How does this handle high-volume or high-frequency retraining?

[PLACEHOLDER — answer copy pending]

How is this different from tools like MLflow or Vertex AI?

[PLACEHOLDER — answer copy pending]

How long does it take to get our first model live?

[PLACEHOLDER — answer copy pending]

Design Sprint

Accelerator

Partnership

AI-Native Fund Operations

Tech & AI Due Diligence

Portfolio Deepdive

Streaming data

ML Ops

Data Platform

Agentic AI

Energy & Electrification

Mobility & Transport

Climate Intelligence

Buildings & Industry

Team

Careers

Go from one to a hundred ML models without reinventing MLOps

How to crack ML & Decision Ops without reinventing the wheel?

Many tools support the process, but keep the core friction

Optimization algorithms are separated from forecasting

Maturing your ML ops practice takes time. Too much time.

Production-grade MLOps for energy & logistics forecasting and decision algorithms