AI and ML are not magic, but Newcomp’s data science team knows how to apply these advanced models to go beyond what a human can realistically do. We help spot opportunities for AI and build powerful algorithms that automate and optimize human tasks.
Build continuous delivery and automation pipelines for machine learning models to be deployed and maintained in production reliably and efficiently using MLOps.
Perform pre-processing data wrangling techniques for the addition, deletion, or transformation of training set data involving feature engineering, reduction and cleansing.
Gather data and perform feature engineering to develop ML models using supervised, unsupervised and reinforcement learning techniques.
Provide ML models with training data to learn from, mapping the input attributes to the target variables while identifying weights and biases to minimize loss.
Develop statistical models to predict outcomes, behaviours and provide recommendations including time series, clustering, and classification.
Use linear or non-linear programming to minimize or maximize real functions, optimizing models for an ideal outcome using decision variables and constraints.
Detecting anomalies or important events in text, images or data in any structure.
Forecast future behavior to predict events like student attrition, customer churn and equipment failures.
Deploy automation to scale decision-making like loan approvals, machine operating conditions or shopping basket offers.
Modern data needs a strong architecture to support massive amounts of data and computing power. Newcomp architects know how to start small, and build for the future – from basic data acquisition to the most advanced neural networks.
Data lives everywhere now, and our engineers are trained to capture it. They build pipelines to ingest third party, web, unstructured and machine data, then store, refine, organize and deliver it to consumers.
These teams feel the pain of limited insight or access to data. As our champions, they define the target, and help us validate results along the way.
These groups have many names – Centres of Excellence, data provisioning, data warehousing, etc. Our goal is not to work around these existing pipelines, but to help modernize and remove bottlenecks that are preventing data from flowing smoothly.
Define the starting point and the target, highlighting skills, tools and processes needed to close the gap.
Prove our understanding of the problem in a small, representative model.
Refine the models over time, enhance precision and begin to integrate into day-to-day operations.
Operate the system as part of regular business, track effectiveness and feedback outcomes to continuously train the model.
Build structures and processes to collect, manage, and assure the quality of data assets
Develop data environments for reporting and analysis
Design & develop models, templates & processes for financial & operational planning
Design and build elastic infrastructure to accelerate analytical applications