According to Gartner, by the end of 2024, 75% of organisations will be operationalising AI and ML. This transformative shift is set to revolutionise industries, drive innovation, and unlock unprecedented business value.
However, a significant challenge stands in the way: ML operationalisation is not where it should be.
Despite the growing recognition of AI’s potential, the journey from PoC to production remains difficult, with 53% of ML PoCs never making it to production. This high figure is explained in Google’s 2015 seminal paper, “Hidden Technical Debt in Machine Learning Systems,” which highlights that developing the model is just a small piece in a large, complex system.
The real challenge lies in the extensive work required to orchestrate a functional ML pipeline.
Key Requirements for Production-Grade ML Pipelines
To build production-grade pipelines, organisations need:
- Expensive specialist resources.
- Significant effort to productionise and maintain ML models.
These requirements often discourage organisations from fully committing to ML operationalisation. If models are not effectively deployed into production, ROI cannot be achieved.
True business value is realised only through efficient and effective operationalisation.
Efficient and Cost-Effective MLOps with Codex
Can MLOps be done efficiently and cost-effectively? The simple answer is yes!
Codex has developed an AWS-native MLOps Accelerator to deploy a pre-built, scalable, and highly configurable MLOps framework. This user-friendly solution eliminates the need for specialist resources and minimises effort, thereby:
Significantly reducing the costs associated with building and maintaining an MLOps framework.
Accelerating time to value.
Lowering barriers to entry for new customer segments.
Ultimately, this ensures that our clients can:
Focus on delivering value rather than getting distracted by the tedious engineering requirements of operationalization.
Fast track and sustain ROI on AI and ML investments.
Empower their data scientists to focus on model development to drive business value.
Features and Benefits of the Codex MLOps Accelerator
Built on top of SageMaker, our accelerator allows data science teams to effortlessly deploy the end-to-end infrastructure required to operationalise an ML model into a single account. This ensures your teams can focus their attention where it belongs: keeping data clean and validating established pipelines to ensure confident production releases. All that is required is to connect your data, onboard your model, and validate your pipelines before release.
AWS Approved
Today, we are proud to announce that the Codex MLOps Accelerator has passed the AWS Foundational Technical Review. This ensures your organisation can be sure that the framework follows AWS’s renowned Well-Architected best practices. Additionally, you can have confidence that Codex is equipped to efficiently and effectively deliver the service your organisation deserves.
Key Features
Integrate with Your Favourite GIT Provider:
Automated ML Model Deployment, Maintenance, and Infrastructure Configuration.
Management of Data, Model, and Concept Drift.
Proactive Monitoring of Bias in Data and Model Predictions.
Treat Everything ‘As Code’: Versioning and tracking all changes (beyond SageMaker).
Granular Compute Configuration.
Two Available Architectures
- Code Promotion: Ideal for sensitive data scenarios, this architecture allows you to localise data while promoting code changes across environments, making it perfect for industries with stringent data protection requirements.
- Model Promotion: Suitable for a non-sensitive data environment, this architecture focuses on promoting models across different stages of the ML lifecycle, enabling faster iterations and deployment.
Ready to Begin Your MLOps Journey?
If you’re ready to start your MLOps journey, reach out to Codex today!
Adrian Cambpell
Associate Partner, AI
Get in touch to coordinate a meeting with one of our technical experts.
Australia: +61 7 3132 3002.
References
- Onag, G. (2022, February 18). Operationalizing AI: Moving from model to production. FutureCIO. Operationalizing AI
- Costello, K., Rimol, M. (2020, October 19). Gartner Identifies the Top Strategic Technology Trends for 2021. Gartner
- Sculley, D., et al. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.
