Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

Abstract: A server system may match a segment of code for a code integration request to metadata about similar segments of code, wherein the metadata qualifies one or more outcomes of previous integration requests. The server may determine usage rights and rules based on the metadata, wherein some of the usage rights and rules have previously have been approved by a multi-approval workflow that enforces a predetermined process to authorize use of the segment of code for code segment integrations. The server may analyze the metadata to predict an integration score based at least in part on the usage rights and rules of the segments of code. If the integration score of the segment of code exceeds a threshold, the system may automatically generate a data structure for deploying the segment of code, wherein the automatically generating the data structure is performed without the multi-approval workflow.

Abstract: The present disclosure relates to systems and methods for a self-adjusting corporation-wide discovery and integration feature of a machine learning system that can review a client's data store, review the labels for the various data schema, and effectively map the client's data schema to classifications used by the machine learning model. The various techniques can automatically select the features that are predictive for each individual use case (i.e., one client), effectively making a machine learning solution client-agnostic for the application developer. A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored. When new data is added to the data store, a matching service can automatically detect which features should be fed into the machine-learning solution based at least in part on the weighted list. The weighted list can be updated as new data is made available to the model.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.

Abstract: The invention includes a stopper placed in a capsule having an external skirt and an internal duct, onto which the stopper is fitted. The lower part of the stopper includes a tapered part made from flexible rubber, in the form of a bellows with straight walls forming a slot functioning as a valve. The valve closes under the effect of the water pressure exerted on the walls, while the fountain tube effects the opening of the valve by penetrating into the slot. Preferably, the stopper consists of a single piece without movable parts.

Abstract: A knife includes a casing and a blade which can have two positions, an extended position in which the blade projects outside the casing and a retracted position in which the blade is disposed within the casing, a blade carrier to which is connected the blade, a housing on which is slidably mounted the blade carrier and that is removably mounted within the casing, and a finger piece-pusher mounted slidably on the casing to move forwardly the blade carrier to place the blade in a cutting position. The blade carrier and finger piece-pusher are returned to the retracted position by return springs. Components are provided to secure the finger piece-pusher and the blade carrier together to place the blade in the extended position. The components are uncoupled by a conjoint action of rotation and extraction of the blade carrier relative to the finger piece-pusher, resulting from the cutting action.

Abstract: The object of the invention is a cutter with a monoblock blade carrier with securement and release of the blade by pressure, characterized in that said blade carrier (1) is made by molding from a semi-rigid plastic material and comprises two substantially flat shells (1a, 1b) folded facing each other, connected by a thin bridge (2) and defining between them a recess for the reception of a blade (3) open at least on its sides, one of the portions (1a) comprising on its internal surface a projection (10) adapted to penetrate a hole (5) in the blade (3) so as to trap it in said recess (8, 8a) and being, adjacent this latter, deformable by pressure (P) so that, by a lever effect, said projection (10) moves away from the opposite surface of the other shell (1b), thereby permitting the free passage of the blade (3) outside its recess (8, 8a).

Abstract: The object of the invention is a knife comprising a casing (2) and a blade (6) which can have two positions, a first so-called extended position, in which the blade (6) projects outside the casing (2), and a second so-called retracted position, in which the blade (6) is disposed integrally within the casing (2), characterized in that it comprises a blade carrier (4) to which is connected the blade (6), a housing (3) on which is slidably mounted said blade carrier (4) removably mounted within the casing (2) and a finger piece-pusher (7) mounted slidably on the casing so as to move forwardly said blade carrier (4) so as to place the blade in cutting position, said blade carrier (4) and finger piece-pusher (7) being returned to the retracted rest position by return springs (8-11), and in that means (7b, 16, 17) are provided to secure the finger piece-pusher (7) and the blade carrier (4) together to place the blade in said first position, said means being uncoupled by a conjoint action of rotation and extraction