It can take 18 to 24 months before an organization begins to obtain ROI on AI. In that time, they need to identify use cases, evaluate, purchase and configure technology, hire Data Scientists (and often possibly data engineers, solution architects and experience designers), conduct pilots, deploy models, build pipelines and integrate the models into the operational systems where the value of AI can be harvested. Having that team and foundation will likely cost organizations $500k a year or more without known business impact.

Ironside's Ascent.AI de-risks an organization's entrance to AI by eliminating human/technology capital expenditure and offers a step-by-step approach guided by trusted advisors knowledgeable in Data Science best practices and leading technologies. In just weeks you could have a model deployed in production, hosted, and monitored by Ironside on AWS using Machine Learning services like SageMaker. If at any point you desire to own the environment and build your internal Data Science team, Ironside can assist with that as well and transfer the assets to your managed AWS accounts.

Ironside Ascent.AI features:

• Progressive modules that prove out the value at each step. Use cases that aren't valuable can be abandoned early prior to over investment.
• No technology to buy. Our Data Science team is experienced working with Amazon services including Amazon SageMaker and Amazon SageMaker Studio. There is no need to purchase and configure expensive machine learning platforms.
• Pay for partial resources. Ironside provides the data scientists and as needed, the data engineers, user experience designers and solution architects critical to your success. Get the benefits of a loaded data science team without having to hire them.
• Amazon technology offers several benefits including pay as you go pricing models, cutting edge machine learning services, and scalable infrastructure.
• Solutions are transparent. Ascent.AI is Ironside's professional data science services delivered on Amazon. It is not a black box or proprietary model.

Ironside Ascent.AI is delivered in progressive modules starting with the Rapid Viability Assessment (RVA) . This 2 week module will determine if a use case is viable for machine learning to enable the organization to. prioritize use cases that offer potential value and pivot away from those that don't. It includes 3 key activities: profiling relevant data for quality and quantity, evaluating the target field, and testing the relationships and correlations among data points.

Use cases deemed viable are recommended to progress forward to the Machine Learning module, to AI Implementation, Operational Transition, and then Performance Monitoring. In as little as 12 weeks machine learning models can be operational and producing business value - all driven by a minimal investment in Ironsides' Rapid Viability Assessment.