Abstract: A method and apparatus for job submission are described. In one embodiment, the jobs are submitted by a job submission service or gateway that schedules large-scale data processing jobs on remote infrastructure. In one embodiment, the method comprises: receiving a request at a proxy service from a client, via a network communication, to submit a job to a cluster; and managing the job externally to the client, including sending a request to an orchestration system to launch an orchestration system job in a container to start the job running on the cluster via a client process run on a job client in the container and provide state information back to the proxy service regarding the orchestration system job.

Abstract: A method and apparatus for job submission are described. In one embodiment, the jobs are submitted by a job submission service or gateway that schedules large-scale data processing jobs on remote infrastructure. In one embodiment, the method comprises: receiving a request at a proxy service from a first client, via a first network communication, to submit a first job to a cluster; and managing the first job externally to the first client, including sending a request to an orchestration system to launch an orchestration system job in a container to start the first job running on the cluster via a client process run on a job client in the container and provide state information back to the proxy service regarding the orchestration system job.

Abstract: The subject technology receives a first request to deploy a first machine learning model, the first request including information indicating an identifier associated with the first machine learning model. The subject technology determines, based at least in part on the first machine learning model, a set of dependent machine learning models. The subject technology selects a first container environment to deploy the first machine learning model and the set of dependent machine learning models. The subject technology, based on the selected first container environment, executes an instance of an application in the selected first container environment, the first machine learning model and the set of dependent machine learning models being deployed as part of the instance of the application.

Abstract: An active controllable, shear thickening, field responsive device is disclosed and contains a fluid including a carrier component and at least about 40 percent by volume, based on the total volume of the fluid, of a particle component. The fluid can comprise either a field responsive dispersed particle component or a field responsive carrier. The field responsive dispersed phase fluid comprises magnetic- or electrical-responsive particles having a specified average particle size. When subjected to a predetermined shear rate and, optionally, a predetermined magnetic or electrical field, the shear thickening composition undergoes a dramatic and substantial increase in viscosity and shear stress over a very short time period.