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As companies start on their machine learning journey, it is critical that they identify, assess, and implement the right technology, people, and processes to maximise success.
Amazon SageMaker is a fully managed Machine Learning service built by Amazon Web Services that allows you to build, train, debug, and deploy your models at scale, effectively cutting down the technical barriers to entry. SageMaker allows companies to leverage Amazon Web Service’s technology that has been built based on the best practices and lessons learned in operating machine learning at Amazon.com, which has then been tested and refined by thousands of organisations through their use of the platform.
Are you looking to get more out of your data science team?
Are your data scientists stuck on building infrastructure and platforms rather than solving business problems?
Are your data scientists struggling to put their first model into production?
Don’t worry, you aren’t alone. These are common challenges for companies that are building and managing data science teams. We have extensive experience in working with your data scientist teams to help them understand the value of Machine Learning Operations (MLOps) using Amazon SageMaker. Whether it be improved collaboration, faster model training, and deployment or monitor models in production, we can help your data scientists improve their productivity.
Are you looking to rapidly prototype and prove the value of a machine learning solution using Amazon SageMaker?
Our jumpstart program is designed to identify a high-value, low complexity machine learning use case that can be built end-to-end on Amazon SageMaker in weeks that demonstrates data science, engineering, and SageMaker best practice. It is designed to show organisations the value of machine learning while educating different teams on our processes and delivery frameworks.
Has your organisation invested in expensive legacy machine learning technologies that are failing to demonstrate value?
Are you looking to move towards a state-of-the-art platform that is continuously improving over time with a low total cost of ownership (TCO)?
We have helped some of Australia’s leading Energy and FSI businesses move from their legacy machine learning solutions to Amazon SageMaker saving them hundreds of thousands in licensing costs while modernising their machine learning solutions and upskilling their analytics teams.
We help our clients’ get the most value out of their machine learning platforms by starting with problems, not solutions.
We consult with your different stakeholders to understand their most pressing challenges and requirements.
Once captured, we work iteratively and dynamically with your team to architect and implement the best possible solution.
Through our demonstration of outcomes for customers as well as our technical frameworks and skills, we have been recognised by Amazon Web Services by being awarded the AWS Partner of the Year for Data, Analytics & Machine Learning, and 1 of 2 Machine Learning Competency Partners in Australia and New Zealand.
Our data scientists and engineers have been working on and with Amazon SageMaker since Day 1. In the process of delivering tens of machine learning projects using Amazon SageMaker, our team has learned the platform inside and out.
We pride ourselves on our ability to deliver results quickly. We do this by leveraging our experienced consultants, Intellectual Property, and unique delivery frameworks.