We offer expert data science and engineering services…
Strategic Sourcing - On demand Data Engineers, ML & Data Scientists for short to mid-term needs; Part-time Advisory / Consulting services
Data Engineering- Automated Data/ETL/ELT pipelines; Stream processing & Batch Processing; Workflow monitoring; Cluster Management- running on Cloud / Hybrid environments using CI/CD principles
Artificial Engineering (AI) and Machine Learning (ML) - ML Proofs of Concept; ML Roadmaps; Interpretable ML; Business scalability; ML Pipeline latency reduction; ML Ops Automation; IoT; Turning POCs into finished products
by leveraging a broad spectrum of evolving tools and platforms..
delivered by leaders in their field..
Jeff Henrikson – Machine Learning Principal Engineer
Jeffery Stewart – Cloud & Infrastructure Services (CIS) Executive
which has translated to impressive proven capabilities…
and delivered exceptional value to our customers…
Streaming classification to assist in clinical stroke triage
- Objective
- A stroke clinic certified by The Joint Commission as a comprehensive stroke center would like to maintain their good standing based on “Door to CT scan” and related metrics.
- Challenges and Implications
- Clinical support staff loaded the Epic EHR system with valuable information on potential stroke patients while the patients are being diagnosed in clinic.
- Yet clinicians lack software support for acting on all information known about a patient at a given time.
- Solution
- Analyzing the EHR record for cases in which stroke triage was not as fast as desired, a small real-time data stream was extracted from the Epic EHR “globals”, including orders, ICD codes, and textual notes.
- A sample corpus of the data stream was annotated by experts for cases in which text messages warning for time-sensitive circumstances was desirable.
- A classifier was trained using the annotations and connected to the clinic’s existing text messaging system.
- Up to the second awareness of rare but significant information in the hands of clinicians.
Audit and improvement of struggling ML effort for job descriptions
- Objective
- In a business providing feedback on the compensation structure of other businesses, every new customer’s job descriptions needed to be matched against a set of canonical jobs.
- The matching was performed manually by a small group of expert employees
- Scaling the business meant growing the job matching team
- In a business providing feedback on the compensation structure of other businesses, every new customer’s job descriptions needed to be matched against a set of canonical jobs.
- Challenges and Implications
- Many years of previously onboarded jobs with titles and descriptions.
- Existing machine learning efforts toward automating the job matching task had stalled for reasons that the management did not understand.
- Solution
- Upon investigation, we discovered that optimizing for “Precision@K” was more appropriate and as historical matches were recorded as canonical job titles, not job identifiers, a large portion of historical job matches could no longer be interpreted with certainty. We built a model for recovering the lost matching information, for use in folding together with the newly rescoped efforts of the internal machine learning team.
- Our efforts resulted in growth through automation instead of hiring and dramatically increased productivity of the internal machine learning team toward business objectives.