Challenges to the operationalisation of data and AI/Machine Learning projects.
It has been suggested that as many as 88% of AI initiatives struggle to move their machine learning models beyond testing stages. Machine learning coding is indeed difficult, but scaling an algorithm beyond a proof-of-concept into a thriving part of a living organization presents a variety of technical and organizational challenges that cannot be solved with data skills alone. In this white paper, William explores the challenges our own customers have faced when operationalizing their machine learning models, and exhibits NNIT’s experience and solutions to the methodological, tooling, platform, governance and cultural issues that accompany every AI project which tries to moves beyond research.
This information was originally presented by William as part of a guest lecture at IT university of Copenhagen, it was intended to prepare the young data scientists there for the real-world challenges that they will face when working within organizations, and encompassed such topics as:
• ML-Ops and how do we do DevOps for machine learning models.
• Choosing and scaling your first data pipeline architecture.
• Building multi-disciplinary teams for AI projects.
• Defining business impact and technical KPIs for ML models.
• And much more!
Now this knowledge is compounded into a free whitepaper, accessible to all, after simply filling out the form below.
