Our services.
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The Modern Open Source Platforms
We are experts in configuring and deploying the modern open source stacks including components like Airflow, MLFlow, dbt, Iceberg, Jupyter and many more. Using open source platforms is the best way to prevent unsustainable vendor dependencies.
The modern open stack can be deployed in cloud environments or in traditional on premise environments. We build all services using principles from Everything-As-Code to ensure failover capability and enterprise compliance and observability. -
Legacy Transformations
As part of a platform modernisation you are likely going to need a transition state. We are experienced running transformations in complicated enterprise environments.
This often includes bridging the new platforms and your legacy platforms to ensure that you can run while you rebuild. Our engineering expertise can be used to accelerate transformation - because we understand that not everything works like you see on YouTube when you are running corporate IT.
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MLOps
ML model development, deployment and runtime is software development. Too often, we end up telling gifted data scientists to become platform engineers.
Using DevSecOps principles and modern open source platforms we can help you scale the governance, automation, documentation and deployment of ML models at scale.
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Data & Analytics Engineering
Once you are running on modern open source software we can help you accelerate your business value with data. We can provide blueprints and engineering to automate, scale and manage your data flows.
Understand the value of our approach to engineering
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Why Open Source?
Open source platforms in the modern era give business flexibility and enable a uniform architecture across multiple hosting solutions. With open source platforms you can:
- Run the same platform in multiple environments which supports transition states and ensure business resiliency
- You can avoid getting stuck in complicated license negotiations due to sustainable vendor lock-in
- If you are concerned about data sovereignty, you can retain control
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The Data Platform Engineer
Modern managed services have created a myth that all you need is a managed service and a data scientist.
More and more organizations are discovering that as soon as you scale ML model development or data engineering beyond a few teams, there is a gap.
Without a platform team to centrally configure your platforms you will either end up outside your compliance tolerance, in a fragmented and complicated architecture, or you data engineers will have to spend their time being IT people rather than being close to data - or all of the above.
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AI Engineering is Software Development
We spent years learning DevOps and perfecting disciplines like "Everything as Code" and automation - and yet we struggle to apply these learnings in AI Engineering because we de-couple traditional software development and AI development.
We can help integrate AI engineering into your existing processes and adopt the principles from DevSecOps. With a relentless focus on automation and observability, you can solve your compliance challanges while speeding up delivery.