Tug: Artificial Intelligence in Healthcare

Anu Ganesan

Artificial Intelligence is revolutionizing every industry you name it. In Healthcare, AI harness algorithms to predict preconditions, customize healthy choices for patients and, assist surgeons with their robotic movements in the operation theatre. The Tug is one such medical robot doing the heavy lifting hauling medical instruments, food, and medications around the hospitals.

Being a mobile robot, Tug uses its sensors, wifi, and built-in map to travel around the hospitals by opening doors and elevators automatically. Did you know that each Tug makes 80 trips, saving 40 hours of travel per day so that healthcare professionals can spend their valuable time taking care of their patients? Tug enables healthcare professionals to cater to patients in need of care and attention. Simple attention can do wonders not just to patients but also to caregivers.

In spite of its charm, Tugs cause chaos and confusion when two or more TUGS wait for each other to clear its way indefinitely. Tug of War among TUGS can be avoided with continuous improvement by experimenting and operationalizing best-suited algorithms. This process of trial and error in Artificial Intelligence mandates Data Scientists to understand the process of deployment and monitoring apart from building and experimenting with ML models. Either it might be enterprises proficient in AI, employing separate MLOps team or enterprises beginning to enter AI revolution, they both struggle by not having a streamlined process guiding their data scientists in each step of the AI lifecycle.

Predera AIQ offers a competitive advantage by enabling Data Scientists in every step of the AI lifecycle with its tool suites to iteratively experiment, operationalize, and monitor ML models. The build toolset of Predera not only allows the Data Scientists to seamlessly experiment with a wide variety of ML stacks it also proposes valuable insights during the training phase by automatically logging metrics, metadata, and hyperparameters. The deploy toolset supports Cloud (GCP, AWS, Azure), on-premise and hybrid deployments along with resource provisioning (CPU, GPU or TPU), commissioning distributed infrastructure, auto-scaling capabilities and integrating well with existing MLOps solutions such as Google ML Engine, AWS Sagemaker, Azure ML Studio, Kubeflow, etc. Continuous Improvement cannot be achieved without monitoring and Predera offers a solution not only to monitor the performance of AI and its resource consumption but also alert on actionable insights and potentially automate the remediation.

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