The numbers aren’t exactly encouraging.
According to some estimates, 90 percent of enterprise machine learning (ML) models never even make it to production.
While some of this spectacular failure rate can be blamed on the relative newness of the technology, which often translates into a lack of skills with the specialized hardware and software necessary to deploy ML, that’s only part of the story.
The bigger problem, especially for those enterprises actively working on ML projects, is a disconnect between data science and IT teams.
What’s driving this disconnect? Based on our research, one of the major culprits is the fact that many—if not most—ML models created by data scientists are developed on dedicated workstations or cloud instances that enterprise IT teams don’t actively manage.
As a result, when it comes time to actually move an ML model from the data scientist’s workstation and into production, IT teams are often left scratching their heads about how to deploy the model at scale in a datacenter.
It doesn’t have to be this way
At Redapt, we routinely help enterprises of all sizes break down the silos that lead to inefficiencies and inhibit the adoption of new technologies.
To bridge the gap between data science and IT in organizations, we’ve developed an innovative ML Accelerator program. It is specifically designed to take enterprises, at an accelerated clip, from zero to production ready with ML models.
Simply put, our goal with the program is to help organizations realize value from their ML R&D investment. To that end, we leverage our strong partnerships with hardware providers like Dell EMC to assemble a hardware and software package tailored for enterprise ML adoption. In our engineering jumpstart, we help you apply cloud native and DevOps best practices, adapted to ML workloads, to optimize deployment and operations of the end-to-end ML lifecycle.
This package includes a ready-to-use infrastructure installed and made operational at the datacenter, an ML Platform software stack based on Rancher Kubernetes and Kubeflow, and engineering assistance with building and deploying a first ML model. In addition, enterprises are enabled with a full workflow to take them from experimentation to production and knowledge transfer of best practices.
Designed to be adaptable
Every enterprise is different, with unique needs that place limits on the tech stacks they can adopt.
To remedy this situation, the infrastructure included in our ML Accelerator has been designed for baseline ML needs while still being adaptable. This includes:
- Kubernetes based on Rancher
- Hardware to support ML workloads, including a base configuration that supports both GPU and CPU optimized training
- Workflow management with Kubeflow and ready-built containers
- Self-service Jupyter notebooks for data exploration
- Integration with Nvidia RAPIDS and Spark
- IT monitoring and alerting via Prometheus and Grafana
Combined, the tools in this tech stack provide enterprises with everything they need to get up and running with ML. The stack has also been designed within a minimal footprint to lower initial startup costs, but architected to be production ready and able to scale as additional capacity is needed.
From zero to ML in a matter of months
The unprecedented increase in usable data—much of it unstructured—has inspired a land rush for emerging technologies like ML, as enterprises race to put data to work in an attempt to generate a competitive edge.
As with the adoption of any new technology, however, successfully adopting ML requires a holistic approach. It’s one thing to have an army of data scientists cranking out models, but without the skills, software platform, and physical infrastructure in place, those models will eventually hit a dead end.
The Redapt ML Accelerator has been designed from the ground up to mitigate the risk of models never reaching production. It’s also designed to help enterprises get the right infrastructures in place to rapidly move from having no ML capabilities to production in a compressed time frame.
To learn more about the Redapt ML Accelerator program, contact us today.
Keep up with Redapt
- Enterprise Infrastructure
- Data & Analytics
- Cloud Adoption
- Cloud Native
- Workplace Modernization
- Application Modernization
- Google Cloud Platform (GCP)
- Multi-Cloud Operations
- Tech We Like
- Security & Governance
- Dell EMC
- IoT and Edge
- Business Transformation
- Managed Services
- Microsoft Azure
- Emerging Tech
- Google Resale