Events

Sourav will dive into the MERF algorithm including origins, model, principles, mathematical details, and predictions for known and new clusters, provide examples of use cases of mixed effects random forests, and demonstrate MERF performance on synthetic and real data.

With AI and machine learning transforming industries, many companies are looking to take advantage of the recent advances. Rajendra shares our six-step “Lean AI” process, which helps Manifold deliver value to our clients quickly, accelerating their journey towards becoming a data-driven organizations.

Sourav will explain how the mixed effects random forests model marries the world of classical mixed effect modeling with modern machine learning algorithms, and how it can be extended to be used with other advanced modeling techniques.

In this presentation, we will present entity resolution in the context of a detailed example scenario using physician data matching.

Using specific real-world use cases, Sourav details his process for casting business problems as machine learning problems—from identifying which business problem to solve to hypothesis generation, to AB testing.

In this talk, Sourav discusses a number of case studies with the AI + IoT for Triage business pattern, from digital therapeutics to predictive maintenance. He shares specific examples of the challenges that exist when applying machine learning to IoT sensor data streams and of putting humans in the loop.

One of the most common business patterns of artificial intelligence we see in the field is “AI as Triage”. This is where AI is augmenting human decisions with predictions. The pattern is particularly prevalent when AI is coupled with new data for IoT sensors — whether from medical devices or oil fields.

Sourav details Manifold's 6-step Lean AI process for enterprises to implement AI services to their business. He also discusses how to cast business problems as machine learning problems—from identifying which business problem to solve, to hypothesis generation, to A/B testing.

Using specific real-world use cases, Sourav details his process for casting business problems as machine learning problems—from identifying which business problem to solve to hypothesis generation, to AB testing.