Key Facts
V2 AI enabled the scaling of personalised student support, driving improvements in student retention and completion rates by delivering a production-grade MLOps stack on Databricks for Swinburne University.
On-demand insights replaced biannual reporting cycles, allowing for proactive, timely interventions that will improve student academic performance and reduce graduation delays.
Deeper insights enable proactive improvements in curriculum content and delivery, strengthening the university’s overall academic performance and institutional reputation.
Modernisation and optimisation of existing capabilities for the Databricks platform provide long-term performance and maintainability gains, allowing Swinburne to extend data-driven initiatives across departments.
The V2 engagement reduces reliance on third parties and lowers long-term operational costs as engineering uplift and full knowledge transfer empower internal teams to manage and evolve the solution independently.
The Client: Harnessing Predictive Analytics to Transform Tertiary Education
Ranked among the top 1% of universities in the world, Swinburne University of Technology educates over 45,000 students from around the globe in science and innovation disciplines, including physics, engineering, IT, design, health sciences, and other related fields.
Driven by a vision to help every student succeed, Swinburne developed a predictive analytics model that could proactively identify students at risk of not completing their courses. The university sought to identify the barriers to student success and utilise those insights to provide personalised support, enhance learning, and improve outcomes throughout the student journey.
The Challenge: Building a Scalable Foundation for Institution-Wide Impact
Data scientists at Swinburne had developed eight early-stage predictive models capable of identifying students at risk of disengagement. While promising, the initial approach to running the models was largely manual, dependent on key individuals, and resource-intensive. This limited the university’s ability to use and act upon the insights generated.
Hence, Swinburne sought to implement the operational capabilities (MLOps) necessary to support predictive analytics at scale. Their ultimate aim was to establish a robust, unified platform that would facilitate data-driven decision-making across the institution.
The team chose to scale the solution on the Databricks platform, as it provides built-in capabilities for predictive and generative AI development and offers a democratised approach to data management and governance.
Swinburne also needed a capable and experienced technology partner to validate whether the underlying predictive approach could integrate effectively with real-world business processes and justify the investment required for full-scale implementation.
The Solution: Production-Ready Machine Learning on Databricks
Swinburne selected V2 AI as its Databricks implementation partner of choice due to our expertise with the platform and our commitment to thoroughly understanding Swinburne’s business objectives, constraints, and desired outcomes. This included challenging existing approaches and aligning the final technical solution with the university’s long-term vision.
In eight weeks, the V2 team delivered an end-to-end MLOps framework using native Databricks services. This included:
Model repository structuring and permissions
Continuous integration and deployment pipelines
Databricks workspace permissions
Orchestration of feature engineering, model training and inference.
Going much beyond a lift-and-shift, we modernised and optimised Swinburne’s eight initial predictive models to fully leverage Databricks-native capabilities. Refactoring allowed for performance, scalability, and maintainability enhancements across the board.
Swinburne’s system is now in production and usable on demand, putting the University in a strong position to expand its predictive analytics initiatives.
Value Delivered: Scalable Foundations for Student Success and University Growth
V2 AI delivered a significant uplift to Swinburne’s data engineering and Databricks platform capabilities, laying a robust technical foundation for ongoing innovation. This uplift was delivered within a tight timeframe and on a limited budget, while ensuring that essential governance and security controls were in place from the outset.
Measurable business outcomes include:
Improved Student Outcomes
Previously, Swinburne’s early-stage predictive setup was housed on isolated hardware and reports were only generated twice a year, resulting in delayed support mechanisms for at-risk students. With the new solution, the university can now generate predictive insights on demand. Timely interventions help students stay on track to complete their coursework, directly impacting academic success and student well-being.
In the Australian education system, failing a single subject can delay a student’s graduation by a minimum of six months, significantly impacting financial well-being and career prospects. With real-time, data-informed support, the university is now better positioned to improve student retention and outcomes at scale.
Enhanced Education Delivery
The university can now identify and act upon patterns and correlations between lesson delivery and student success at scale and on time. For example, if student disengagement is higher for a particular course, the university can proactively investigate and solve any underlying curriculum or delivery issues. The ability to take timely corrective actions strengthens the university’s long-term academic performance and institutional reputation.
Team and Engineering Uplift
Throughout the engagement, V2 AI worked closely with Swinburne’s data and platform engineering teams to ensure knowledge transfer and shared ownership of the solution. Our engineers guided Swinburne stakeholders on a Databricks adoption journey, explaining the rationale behind specific Databricks configuration choices and how to manage and evolve the solution independently.
The Swinburne team is now able to manage its Databricks environment and predictive models without external support, ensuring that critical knowledge remains within the organisation. Reduced reliance on third parties improves agility and reduces long-term operational costs.
Empowering Universities with Scalable Predictive Intelligence
By transforming early-stage models into a production-ready system on Databricks, V2 AI laid the groundwork for scalable, data-driven student success. The university is now equipped to support students in real time and extend predictive analytics across departments. This project has contributed to university initiatives that strengthen educational outcomes, institutional reputation, and long-term global rankings.
