On demand webinar
Is deployment the elephant in the machine learning room?
This webinar tackles the critical challenge of ML/AI deployment — exploring the key blockers, the latest cloud and standalone deployment options, and how to ensure your data science investment delivers real business value.
Deployment is the most critical step in any data science or machine learning process.
It is where our ML/AI models are typically put to work in business and industrial process. Where most of the value of a data science project is derived.
If we can’t deploy then all the cost and effort invested in discovery, data engineering and model development is wasted.
Yet, according to many industry analysts more often then not deployment does not happen. Indeed, 43% of Data Scientists say that 80%, or more, of their built models fail to deploy.
Whether you are:
- Looking to deploy for the first time
- Or have struggled to deploy in some instances
- Or perhaps you are considering new deployment options. Maybe as part of a cloud migration
What we will cover:-
- A high level view of deployment
- Blockers to deployment and how to manage them
- Deployment options in:
- Cloud and premised platforms. Inc. ML/OPS. Including AWS, Azure, IBM WatsonX
- Standalone tools including Python, R, SAS , IBM/SPSS and Dataiku
- The model interoperability standard PMML and new developments in PMML from OpenScoring
Who should attend?
This webinar is for any current practitioners and business stakeholders involved in data science. Or for anyone considering new ML/AI initiatives.