Abstract: Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.

Abstract: Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.

Abstract: The present disclosure describes techniques and systems to provide focused and gamified active learning for machine learning model development. The present disclosure describes determining an active learning algorithm with which to choose batches of content that correspond to specific categories of content to be annotated. Furthermore, the present disclosure provides that the batches of content, and particularly characteristics of the content can be identified for annotation based on ML model performance, such as an entropy of the ML model.

Abstract: Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.

Abstract: Using computed facial feature points to position a product model relative to a model of a face is disclosed, comprising: obtaining a three-dimensional (3D) model of a user's face, wherein the 3D model of the user's face comprises a plurality of 3D points; determining a face normal that is normal to a plane that is determined based at least in part on a first subset of 3D points from the plurality of 3D points; determining a set of computed bridge points based at least in part on a second subset of 3D points from the plurality of 3D points and the face normal; and using the set of computed bridge points to determine an initial placement of a 3D model of a glasses frame relative to the 3D model of the user's face.

Abstract: Using computed facial feature points to position a product model relative to a model of a face is disclosed, comprising: obtaining a three-dimensional (3D) model of a user's face, wherein the 3D model of the user's face comprises a plurality of 3D points; determining a face normal that is normal to a plane that is determined based at least in part on a first subset of 3D points from the plurality of 3D points; determining a set of computed bridge points based at least in part on a second subset of 3D points from the plurality of 3D points and the face normal; and using the set of computed bridge points to determine an initial placement of a 3D model of a glasses frame relative to the 3D model of the user's face.