Data science initiatives, especially machine learning projects show huge potential to create value for a modern digitized organization, when successfully implemented. However considerable challenges exist in taking proof-of-concept (POC) machine learning solutions and scaling them to operationalization within real-business applications.
NNIT has co-developed a new offering called "Scale Data Science" in close collaboration with two of our customers, dedicated to tackling the core challenges faced by ambitious organizations on their data science journey.
Together we're building multidisciplinary teams and a strong AI culture within their organizations, and tailoring our battle-tested frameworks and platform to their preferences to produce:
- AI center of excellence
- A multidisciplinary committee, to drive forward AI innovation within an organization, and oversee all aspects of development, use cases, operations, communications etc.
- Personalized recommendations around governance, ethics, GDPR compliance, organizational structure, and more.
- Recommended roles and responsibilities going forward, where existing competencies can be used best and who else may be needed going forward.
- ML-Ops framework
- Standardizing the development, release, and operations of machine learning throughout the organization.
- Addresses the various challenges unique to machine learning development (such as using production data for training, or monitoring model performance, for example).
- Aims to automate as much of the day-to-day operations as possible, freeing valuable staff to undertake new tasks.
- Containerized platform specifically to host fast and reliable ML CI/CD, and to manage data ingestion.
- Cloud, or propriety.
- Many facets such as; administration, patching and CPU/GPU orchestration can be automated or taken care of by NNIT professionals.
- Easy to scale.
Let data scientists do what they do best – and realize the full business potential of your AI initiatives
Commonly we see an organization with AI ambitions might hire one or two data scientists expected to perform all necessary tasks along the road in the research, development, and operationalization of a machine learning project. This has various difficulties, while obviously incredibly skilled and intelligent employees within their field; data scientists are not trained as fully-fledged computer scientists, or system architects, project managers, SMEs, etc. People with a practical understanding of all of the above issues are understandably incredibly rare (often referred to as 'Unicorns'), and highly-competitive to hire. Therefore, organizations need a multidisciplinary approach when embedding AI into their business.
We're excited to provide support to your talented data scientists and their projects, removing the technical and organizational busy-work from their day-to-day allowing them to focus on the issues they we're hired for, and ensuring your hard-won machine learning efforts scale and capture value sustainably, together.