Machine Learning

Labeling and Annotation

Problem solving in the age of AI

The field of Machine Learning and Artificial Intelligence is evolving into a big multi-functioning model that has expertise to do plenty of things and solve many complex problems.

With a decade of experience in Labeling and Annotation, creating sophisticated training data sets for Machine Learning / AI Algorithms, and enabling machines to learn, GlobalLogic partners with global technology companies to design and build the AI-enabled world. GlobalLogic provides advisory, ideation and implementation services in Labeling and Annotation to companies across verticals, globally.

Machine Learning Methods

We partner and help our clients understand and interpret images, video, sounds, voice, text and all forms of unstructured data to get actionable insights.

Supervised Learning

Historical data predicts likely future events, via methods such as classification, regression, prediction and gradient boosting.

Semi Supervised Learning

The cost associated with labeling is too high to allow for a fully labeled training process.

Unsupervised Learning

The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data.

Reinforcement Learning

Used for robotics, gaming and navigation. The algorithm discovers through trial and error which actions yield the greatest rewards.

Who can adapt to Machine Learning?

Most industries working with large amounts of data have recognized the value of Machine Learning technology and are able to work more efficiently.

Automotive

Education

Energy

Financial Services

Gaming

Governments

Healthcare

Retail

About GlobalLogic

Rooted in Silicon Valley, GlobalLogic operates design studios and engineering centers in 14 countries across 4 continents, extending our deep expertise in strategy & design, DevOps, big data & analytics, AI/ML, etc. We partner with global technology companies to design and build next-gen geospatial solutions that serve their business needs.

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