MLflow is open-source software initially developed by DataBricks for managing the “machine learning lifecycle.” It makes the model artifacts and their environment specifications more readily available when assembling ML model applications or for other purposes such as collaborating with teammates
Traditional lending practices are a prime candidate for machine learning improvements. Lenders can make more accurate and faster decisions by shifting decision-making from analysis of individuals to analysis of trends and patterns.
Natural language models have come a long way in the past couple of years. With the advent of the deep learning Transformer architecture, it became possible to generate text that could, plausibly, be passed off as written by a human.
To understand GPT-3, it’s helpful to understand a little bit about the history of language models. The language of computers is numbers. The input to all machine learning algorithms is ultimately numbers as well.
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.
Mosaic is developing a machine learning based tool that assists corporate travel manages and business travelers in making the safest travel decisions possible.
Decision processes in support of jobs that either cannot be or are very difficult to automate are frequently overlooked by out of the box software providers. One such process is the creation of optimal staffing plans for outbound teams loading cartons onto trucks.
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.
Executives thought they could drive even more gasoline sales through further discounting to their members. They needed to measure if dropping the price per gallon drove more sales volume, validating their beliefs. Instead of relying on ‘tribal’ knowledge.
Algorithms can be trained to mimic human behavior, but what happens when the human developing the algorithm inadvertently allows bias into the training process?