Machine Learning Engineering & Operations
The key to Machine Learning is accuracy, but ML Engineering makes sure that the lock actually opens a door. We provide a suite of frameworks and patterns that let data science teams build, train, and deploy models at scale with less effort and at a lower cost.
Here's what we do everyday, our bread-and-butter
- Putting ML models into production
- Enabling performance monitoring of ML models in production at scale
- Training and upskilling
- Making it easier to get ML models into production repeatably
- Building automated re-training frameworks for production ML models
- MLOPs
Brands we've worked with
Machine learning engineering at eliiza
ML Engineering (also known as AI Engineering, ML Eng) is the practice of ensuring Machine Learning models integrate successfully into the real world. It combines the capability of Software Engineering, DevOps, Machine Learning, and Cloud Engineering.
At Eliiza, our ML Engineers work closely with Data Scientists to ensure that the characteristics of a model during development are maintained as it is deployed and scaled.
Our ML Engineers also work closely with Data Engineers and Cloud Engineers to ensure that your platforms and processes support the unique requirements of machine learning software and enable Data Scientists to do what they do best.
What we can help you with
ML Engineering covers two core sub-domains, ML Operations (MLOps), which includes everything that’s required to ensure a model becomes and remains operational, and Technical ML or Environment Optimisation, which involves deep diving into specific environments to ensure models are performing optimally.
We also have a range of other services that support your operation throughout the AI development lifecycle.
MLOps
Our ML Engineers specialise in ML Ops, a term which encompasses the additional components required to put ML models into operation.
An ML model alone forms only part of the primary inference pipeline, there is additional work required to integrate source and destination systems, and to ensure the models operate securely, consistently, and understandably.
Beyond inference, we design and build the components required to monitor data drift and model performance, track experiments, version models, and, where appropriate, deploy automatically.
We take a holistic approach to MLOps, engaging your team and updating processes to ensure these components are part of your ML journey from day dot, and not a last minute blow out to your budget – or showstopper to getting your model into production.
Environment Optimisation
Our ML Engineering team brings a wealth of experience across a variety of common (-and more exotic) tech stacks. We are able to dive deep into your production code, identifying and analysing quick wins to improve the latency and throughput of your models.
Depending on the environment, implementing effective caching, minimising in-memory copying, or overhauling algorithms to use more efficient database queries are just some of the approaches we apply.
Our deep expertise with ML models and runtimes uniquely positions us to optimise your model within its environment.
ML Maturity Framework
Every organisation has different levels of maturity when it comes to how they run ML projects and maintain ML models. Our ML Maturity Framework defines basic, intermediate, and comprehensive levels of ML maturity across seven key pillars: Development & Deployment, Data Governance, Reliability, Performance Efficiency, Security, People & Operating Model, and Ethics. We are able to quickly compare your organisation against these, identify your current maturity and present a prioritised roadmap to reach the next level. We can then work with you to deliver this roadmap, ensuring you get the most out of your ML models.
[Questions that may be being asked to prompt for this]- We have a model and want to put it in production
- Our Data Scientists develop models in their own ad hoc ways
- Our Data Scientists have trouble keeping track of models they’ve developed
- We don’t know how our models perform in production
- We suspect our production models are degrading but don’t know what to do
- How can our models learn and become smarter by themselves?
ML PoC/PoVs
If you need an integrated proof of concept for an ML solution to one of your business problems, our ML Engineering team is perfectly placed to help.
We work with you to understand your business case, and can identify models (spanning both the open source community and commercial offerings) to build a solution quickly.
Our experience deploying production ML systems means we are able to ensure the PoC focuses on the parts that need to be proven, and derisks your investment in a production solution.
We can then advise on which components will benefit the most from custom models trained by Data Scientists, ensuring a smooth runway beyond PoC to an operational product.
Enabling Cloud Partner Solutions
We work closely with the major cloud providers, leveraging cloud platforms and products to create industry and function-specific insights and intelligence for businesses.
Cloud providers can support a wide range of ML initiatives, from leveraging common deployment patterns and pipelines, to bespoke containerised CI/CD pipelines and platforms.
We harness the latest cloud solutions to accelerate your AI time-to-market.
Upskilling and training
We work closely with your in-house engineers to ensure your team is comfortable with taking over the solution.
Where necessary, we also provide custom training and knowledge sharing sessions to set the team up for success.
Meet the Machine Learning Engineering team
Brendan Nicholls
-
Head of ML Engineering -
Melbourne
Brendan Nicholls
Head of ML Engineering
Brad Jackson
-
Machine Learning Engineer
-
Melbourne
Brad Jackson
Machine Learning Engineer
Angus White
-
Senior Machine Learning Engineer
-
Melbourne
Angus White
Senior Machine Learning Engineer
Who are ML Engineers?
At Eliiza, we integrate AI and machine learning technologies into our operational structure, to be able to solve unique and challenging problems for our clients. Crucially, Data Scientists, Machine Learning (ML) & Data Engineering teams work closely together to ensure that the solutions built can be deployed, operated, and iterated at scale with a focus on lowering the cost of change.
To do so, the Data and ML engineers ensure all projects are guided by a principle-based assessment structure before delivery of the solution to clients. The ML engineers typically start with an assessment of the machine learning maturity, or readiness, of an organisation within seven pillars:
- Development and deployment
- Data governance
- Reliability
- Performance efficiency
- Security
- People and Operating Model
- Ethics
Post-assessment, ML and Data engineers seek to provide recommendations to the target organisation with an aim to execute the recommendations based upon the maturity level of the organisation.
Our 4-stage hiring process ensures that we hire great people, with high level technical skills across the disciplines of software engineering, data science and machine learning. Furthermore, what differentiates us from our competitors is our multi-step ML engineering practises that provide a suite of frameworks and patterns that enable ML and Data Engineers to streamline building, training, and deployment of models at scale with less effort and at a lower cost. More importantly, we do not follow the one-size-fits-all approach. Solutions the ML and Data engineers don’t believe in one-size-fits-all and our solutions are tailored to customer needs based on their infrastructure platforms, budgets and ambitions.