Data Wrangling

The Positronic data science team helps you collect, label, and analyse your data using deep learning to derive insights and predictions. We’ll isolate the signal in your data that you can leverage to improve business performance. Let us help you align your data with your business objectives.



The first step of the data phase of your AI project is to collect the required data. The rule of thumb of machine learning is that for any neural net to begin developing competency at discriminating between classes it needs to see 10,000 samples of each class. The Positronic data science team can help you calculate precisely how much and what kind of data you’ll need. We can help you collect it, augment it from external sources, and transform it.


The second step of the data phase of your AI project is to label the data. Machine learning works by providing many samples of data that have already been correctly predicted. Those predictions are referred to as the data labels. Labels come in the form of simple tags to mark events in data, precise coordinates of locations within your data, or sometimes lasso-style segments around portions of your data that describe where, within your data, those data events occur. The Positronic data labeling platform helps you track costs, review quality, and manage the performance of your labeling process, capable of helping you label data of any type; video, audio, text, images and even custom schemas.


The third step of the data phase of your AI project is to prepare the data for consumption by the machine learning platform. Optimizing your data storage for the speed at which it can be moved from disk to compute can be the difference between measuring your machine learning spend in thousands versus millions of dollars. In addition to optimizing for speed, the Positronic data platform automatically inspects your data for corruption & contamination and validates that there exists some signal in your data that correlates to your desired predictions. Follow the adage of measuring twice & cutting once to avoid burning through your AI budget and getting poor results.


Once your data is ready to go on to the training phase, bring on the Positronic machine learning team to help. Driving insights into specific workflow steps of a business process is the ultimate value proposition of data science. Prioritize workload based on prediction of higher returns. For example, the California Franchise Tax Board increased revenue by more than $400M in the first two years of operation using a solution that prioritized workload according to how likely each case is to result in payment and how much that payment might be.

Big Data are data sets that are so Fast Data are data sets that Smart Data are data sets that have
voluminous (volume) and stream in at a high velocity. been cleaned (veracity),
complex (variety) that traditional Decreasing time-to-insight helps transformed, and audited to be fit
data-processing apps are companies gain an edge from for machine learning to surface
inadequate. insights about the present or true value that can be applied to
future. business processes.

Case Studies

ATS data recovery
A new way to train, manage, & orchestrate machine learning assets.
Talent Recruitment Automation
Use machine learning to improve recruiter productivity by 500%.

The Positronic data science team helps you convert your big data into smart data.