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ML Model Migration & Modernisation
Move Smarter. Deploy Faster. Scale Confidently.
Shipshape Data helps you migrate and modernise machine learning models, moving them to scalable, compliant, cloud-native environments without disruption.
From Legacy to Leading-Edge
Your models shouldn’t be trapped by old infrastructure.
We move them to modern cloud environments built for scale, performance, and observability, giving your teams more control and less maintenance.
This isn’t just migration – it’s optimisation: better runtime efficiency, reproducibility, and governance across every deployment.
Operational efficiency
Streamlined migration pipelines reduce deployment time and complexity.
Cost optimisation
Lower infrastructure spend through right-sized, cloud-native architecture.
Enhanced efficiency
Models run faster and scale seamlessly across environments with elastic compute resources.
Governed operations
Every model is versioned, monitored, and compliant with enterprise standards.
Outcomes That Drive Real Business Impact
We deliver migrations that strengthen reliability, accelerate innovation, and maximise return on your AI investment.
Faster Decisions
Deploy models in modern environments with reduced latency and improved responsiveness.
Lower
Costs
Reduce infrastructure overhead by right-sizing compute and automating lifecycle management.
Improved Accuracy
Maintain or enhance model fidelity during transition with robust validation and testing.
Is your business ready for AI?
Our free AI Readiness Assessment helps you uncover how prepared your organisation really is, so you can identify gaps, strengthen your foundation, and confidently move toward AI-driven growth.
“Working with the team at Shipshape has been really positive. The team were very flexible in addressing our requests. When we encountered problems, they rolled up their sleeves and found solutions for us”
Edt Goris
Partner & Country GM, Slimstock
Slimstock Elevates Website Engagement With Custom AI Chat Interface
Our Approach: From Migration to Modernisation
Every migration follows a structured process to ensure speed, safety, and sustained performance.
Step 1
Assess and Plan
We audit your existing models, dependencies, and environments to identify the best migration path.
Step 2
Prepare and Containerise
We package models, dependencies, and data pipelines for seamless transfer and compatibility.
Step 3
Deploy to Target Platform
We migrate your models to the optimal environment for your workloads or chosen stack.
Step 4
Validate and Optimise
We benchmark latency, throughput, and accuracy to ensure your model performs as intended, or better.
Step 5
Monitor and Scale
We implement MLOps practices, observability dashboards, and automation for future releases.
Built on the Platforms You Trust
We deliver model migrations using best-in-class infrastructure, frameworks, and orchestration tools to ensure stability, security, and scalability.
Platforms
Azure
AWS
Google Cloud
Frameworks
LangChain
Hugging Face
Tensorflow
Databases
Snowflake
Supabase
Databricks
Monitoring
PowerBI
Streamlit
Dataiku
Frequently Asked Questions
We handle everything from traditional ML models to advanced neural networks and fine-tuned LLMs.
No, every model undergoes validation testing to ensure equivalent or improved performance post-migration.
Yes. We design flexible architectures that support cloud, hybrid, or on-prem environments.
Typical migrations complete within 2–6 weeks, depending on complexity and platform choice.
Yes, we integrate observability and governance as standard, ensuring models remain compliant and reliable.