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From notebook chaos to govern science — every experiment tracked, every result reproducible.

Experiment Hub

Provides a unified registry for tracking hyperparameters, metrics, artifacts, and code versions across all ML experiments with full reproducibility guarantees. Solves the 'notebook chaos' problem endemic to traditional-industry AI teams who lack experiment governance.

Why It Matters

ExperimentHub brings order and governance to the most chaotic part of ML development — experimentation.
It turns scattered notebooks and untracked results into a single, auditable system of record where every model can be traced back to its exact code, data, and parameters.
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Full reproducibility

Every experiment's hyperparameters, metrics, artifacts, and code versions are captured automatically — any result can be recreated on demand, which is critical for regulated industries and audits.

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Ends notebook chaos

Teams stop losing work to untracked notebooks and tribal knowledge; a unified registry makes results searchable, comparable, and shareable across the organization.

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Deeper cloud-native storage integration

Tighter AWS S3 and GCP Cloud Storage integration than Databricks MLflow SaaS gives enterprises control over artifacts in their own cloud accounts.

The Cloudly Advantage

ExperimentHub extends Cloudly’s MLOps story from training infrastructure into ML governance — a natural companion to TrainForge and a strong wedge into enterprises where compliance and reproducibility are board-level concerns.
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Completes the MLOps offering

Paired with TrainForge, Cloudly can pitch the full ML lifecycle — train, track, govern, reproduce — as one integrated engagement.

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Targets regulated industries

Reproducibility guarantees resonate with banking, healthcare, and telecom prospects who face audit and compliance mandates on AI.

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Pulls through AWS services

S3 artifact storage integration creates direct openings for our AWS Migration and Monitoring services.

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Competitive positioning vs. Databricks

A concrete differentiation story ("deeper cloud storage integration than MLflow SaaS") gives BD a sharp talking point in competitive deals.

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Consultancy revenue driver

MLflow, DVC, and PostgreSQL setup and maintenance generate ongoing DevOps consultancy and managed-service contracts.

The Final Takeaway

Low-friction entry point — Experiment tracking is a smaller, cheaper first engagement than full infrastructure projects — ideal for landing new accounts and expanding later.

Powered By

MLflow

Core to the ExperimentHub technology stack.

DVC

Core to the ExperimentHub technology stack.

Git

Core to the ExperimentHub technology stack.

AWS S3

Core to the ExperimentHub technology stack.

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