Machine Learning & MLOps
Accelerate the path from experimentation to production. We help your teams build, deploy, and operate machine learning models at scale with confidence and repeatability
From Notebooks to Production
Many organisations have talented data scientists building impressive models, but struggle to move those models reliably into production. Without a disciplined MLOps practice, teams face slow deployment cycles, model drift, inconsistent environments, and a lack of visibility into model performance. Innablr bridges the gap between data science and engineering, embedding software engineering rigour into every stage of the ML lifecycle.
We leverage the Databricks Data Intelligence Platform — including MLflow for experiment tracking, Unity Catalog for model governance, and Mosaic AI for scalable model serving — to deliver a unified, production-grade ML platform tailored to your organisation.
Challenges We Solve
Many organisations face a common set of barriers on their ML journey. Notebook-heavy workflows make collaboration and versioning difficult. Lack of standardised infrastructure means models trained in development fail to perform in production. Without automated monitoring, model drift goes undetected until it impacts business outcomes. Innablr addresses each of these challenges with a structured, platform-driven approach.
Our ML & MLOps Capabilities
ML Platform Engineering on Databricks
We design and build production-grade ML platforms on Databricks, leveraging Mosaic AI, MLflow, and Unity Catalog to create a unified environment for the entire ML lifecycle — from data preparation and feature engineering through to model serving and monitoring.
Feature Engineering & Feature Stores
Building and managing centralised feature stores that ensure consistency between training and serving environments, reduce duplication across teams, and accelerate model development cycles.
Experiment Tracking & Model Registry
Implementing MLflow-based experiment tracking and model registries so your teams can reproduce results, compare model versions, and promote models through staging to production with full auditability.
Automated Model Training & CI/CD for ML
Establishing automated retraining pipelines and CI/CD workflows for ML that treat model code, data, and configuration as first-class citizens — enabling rapid, reliable model updates
Model Serving & Scalable Inference
Deploying models to Databricks Model Serving endpoints for real-time and batch inference, ensuring low-latency, high-availability predictions that scale with your business demands.
Model Monitoring & Drift Detection
mplementing continuous monitoring of model performance and data drift in production, with automated alerting and retraining triggers to ensure your models remain accurate over time.
Responsible AI & Governance
Embedding model governance, lineage tracking, and explainability frameworks within Unity Catalog to ensure your ML initiatives are auditable, compliant, and aligned with responsible AI principles
Why MLOps Matters
A mature MLOps practice dramatically reduces the time and cost of deploying new models, improves model reliability in production, and enables your data science teams to focus on innovation rather than infrastructure. Organisations with strong MLOps capabilities are able to iterate faster, respond to changing business conditions more quickly, and derive sustained competitive advantage from their AI investments.
Deployment
Monitoring