AI For Digital Transformations

Digital transformation continues to be a top priority for global enterprises and must be powered by AI.Below are the top factors for successful Digital transformation using AI.

1. Prioritize high-ROI use cases.

2.Insist on comprehensive access to enterprise data.

Separate data engineering tasks from model building tasks to make model building faster, more focused on business use cases

3. Go faster with Auto ML

            AutoML is the chainsaw of data science. Data science teams can become 1,000% more productive if they use AutoML.

4. Know when to quit.

Machine learning is not guaranteed to work. Hence, identify more than one potential use case. If the data doesn’t fit, it’s time to quit.

5. Operationalize with ModelOps.

Data science teams build the ML models and development teams must infuse ML models into business applications. ModelOps is a repeatable, scale process to deploy, monitor, and govern model assets for production consumption.ModelOps enables repeatable and efficient operationalization of ML at scale. ML models must be monitored, retrained, often remodeled.

6. Make digital decisions.

Machine learning models make predictions, but decisions about predictions are often complicated due to constraints. Mathematical optimization (MO) determines the best decision based on real-world constraints. Decision models encapsulate human-expressed decision processes.

7. Make AI infrastructure bottlenecks disappear.AI infrastructure used to train ML models must be available in perpetuity.

8. AI models must integrate with existing business process applications. Infuse AI models into existing business processes with RPA, service calls, or embedded code.

9.Decisions are smartest when driven by both humans and machines.