Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: A method and apparatus for producing and maintaining a repository of service level test points for network based VPNs is presented. A determination is made regarding whether there is more than one Autonomous System (AS) in a network for one or more VPNs. When there is not more than one AS then next hops are found for the VPN. Next, VPN prefixes associated with the VPN are found and a set of Label Switching Paths (LSPs) in use for the VPN are produced. When the determination is that there is more than one AS then routers in the present AS associated with the VPN are found. VPN label stacks are used to find all Provider Edge (PE) routers associated with the VPN.

Abstract: A mechanism for ASBRs to identify the originating node, or router, in an LSP conversant autonomous system (AS), such as an MPLS VPN environment, maintains the identity of the originating node and successive nodes in subsequent autonomous systems along the path to the node to be pinged. The identity of the transporting nodes is stored in a stack or other object associated with the ping request (ping), such that the pinged node may employ the stored identity as a set of return path routing information. Successive ASBRs store their identity on the stack, in an ordered manner, along the path to the destination. Upon reaching the destination (ping) node, the destination node employs the identity of the first node on the stack to send the acknowledgment, or ping response. Each successive ASBR, therefore, pops (retrieves) the next node identity from the stack and redirects (sends) the ping response to the retrieved node.