Mosaic Data Science has a long history of using the latest and greatest in AI and ML to help customers determine how to drive impactful outcomes based on their data and processes.
This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Mosaic sees Digital Transformation differently; our view is that while the technology is a critical part of any Digital Transformation, it’s only a part of a greater whole that includes people, process, and culture change that all combine to enable effective use of the technology.
Operating in today’s conditions requires creative thinking, agile decision making and embracing change. AI & Digital Transformation can support all these disciplines.
For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.
Mosaic developed a data-driven alerting solution powered by unsupervised learning to assist a leading energy utility in detecting voltage anomalies & informing optimal grid health decisions.
Mosaic developed an innovative optimization app for the green power sales function at a leading utility, helping them recommend suites of renewable energy products to meet corporate carbon footprint reduction goals within budgetary constraints.
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.
Mosaic is developing a machine learning based tool that assists corporate travel manages and business travelers in making the safest travel decisions possible.
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.