Data Engineering Services
& Machine Learning

We are a boutique consultancy specialising in data engineering, ML and DataOps. Data-Driven AI is where Software Engineering meets Data Science.
We can help you make your ML project a success!

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Value.
It's all about the outcome

We look at the outcomes you want to achieve in your business and the challenges preventing you from achieving them.
We use data and smarts to drive valuable insights and aim to move your organisation up the analytics value chain.

Data Analytics Value Circle

We use proven, scalable cloud technologies

Elastic pricing, low barrier to entry and huge scale architecture provide a powerful platform for advanced analytics driven by Big Data.
DevOps is at the core of every solution to provide rapid, agile iterations.

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Our Services

ML Engineering & MLOps

Machine Learning engineering backed by DevOps (MLOps) and solid software engineering practices to provide training at scale, model deployment and quick iterations

Cost Optimisation

Reduce your monthly Azure spend (and increase profit!) We'll review your PaaS and IaaS services, utilisation trends and use-cases and make cost and process recommendations to reduce costs

DevOps

Every project we do follows our standard DevOps foundation to allow quick iterations, source control and CI/CD. The benefits to the business are lower total cost of ownership

Data Science Enablement

We understand the ML project landscape and can help you to prepare your data, processes, people and environment for your next data science project to make it a success

Advanced Training

We can skill up your existing Team with advanced analytics capabilities and empower them to be able to create and maintain modern data platforms and Big Data pipelines

Data Engineering

We do all aspects of pipeline engineering such as ingestion, preparation, transformations at scale. We believe in PAYG, elastic pricing and huge scale as a foundation
  • Only 20% of the data science and analytics models that get built actually get implemented.

Why do many Data Science projects fail?

An ML project is no different from any Software Engineering project and should be DevOps-centric with agile iterations and quick feedback loops.

Clean, reusable code in source control with CI/CD should be at the core of every software project.

Understanding the different roles in an ML project, the data science workflow and how to breakdown silos whilst focusing on the business outcome is the key to success.

Scalable Technology Platform​

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DevOps driven data engineering
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Databricks Fan?

We are the proud hosts of the Sydney Databricks Engineering User Group.

Come along to the next one to meet other Databricks enthusiast and hear how customers are using Databricks in their solutions.

From the Blog

Rodney Joyce
This is Part 1 of a 2 Part series where we'll be exploring Data Sharing. We'll look at why we want to share data and what the common problems are. ... read more
Rodney Joyce
Machine Learning is a complex subject based on many compound, complex leanings performed over time by many people smarter than most.There are a number of visual editor tools that try ... read more
Rodney Joyce
"IT" This has got me by for the past 20 years when asked by various relatives and friends exactly what it is that I do. It does mean I have to ... read more
Rodney Joyce
Here is the next tech talk in Data Science for Dummies series I am presenting around Sydney:  Part 2 of 9: Titanic survival prediction with Azure Machine Learning Studio + ... read more

Do you have an analytics project you'd like to discuss?

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