A sorting engine is an intermediary layer between a multi-threaded engine that obtains batches of training data from the producers in parallel and the underlying machine learning engine. The sorting engine includes a shared buffer that has various slots for storing batches of training data, where the slots are organized in a deterministic order associated with the producers. A batch of training data obtained by a thread from a given producer may be stored only in a corresponding slot in the shared buffer. Further, the sorting engine transmits the batch to the machine learning engine only when a previous batch in the deterministic order has been transmitted from the shared buffer to the machine learning engine.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: providing a shared buffer for storing batches of training data obtained from a plurality of training data producers, the shared buffer configured according to a deterministic order of the plurality of training data producers; receiving a plurality of batches of training data obtained from the plurality of training data producers in parallel; storing the plurality of batches in the shared buffer in the deterministic order, the deterministic order being independent from an order in which the plurality of batches is received; and transmitting the plurality of batches to a training data consumer in the deterministic order. 2. The method of claim 1, wherein the shared buffer includes a different set of slots associated with each of the plurality of training data producers, the sets of slots organized according to the deterministic order. 3. The method of claim 2, wherein storing the plurality of batches in the shared buffer comprises: for a first batch, identifying a first training data producer from which the batch was obtained; identifying a first set of slots in the shared buffer associated with the training data producer; and storing the first batch in a given slot of the first set of slots. 4. The method of claim 3, further comprising determining that the given slot corresponds to the first batch based on a batch index associated with the first batch, the batch index indicating a location within the first training data producer from which the first batch was obtained. 5. The method of claim 1, wherein each of the plurality of batches is obtained by a different thread executing in a multi-threaded execution environment. 6. The method of claim 5, wherein a first batch of the plurality of batches is obtained from a first data producer by a first thread, and further comprising: before transmitting the first batch to the training data consumer, receiving a second batch obtained from the first producer by the first thread; and blocking the first thread until the first batch is transmitted to the training data consumer. 7. The method of claim 6, wherein blocking the first thread comprises: determining that a slot in the shared buffer where the second batch should be stored is occupied by the first batch. 8. The method of claim 2, further comprising: determining that no more batches are to be obtained from a first training data producer in the plurality of training data producers; and storing a dummy batch a first slot included in a first set of slots associated with the first training data producer. 9. The method of claim 1, wherein the training data consumer is a machine learning system configured to process the plurality of batches to identify one or more statistical similarities between data included in the plurality of batches. 10. A computer-readable storage medium containing computer program code for: providing a shared buffer for storing batches of training data obtained from a plurality of training data producers, the shared buffer configured according to a deterministic order of the plurality of training data producers; receiving a plurality of batches of training data obtained from the plurality of training data producers in parallel; storing the plurality of batches in the shared buffer in the deterministic order, the deterministic order being independent from an order in which the plurality of batches is received; and transmitting the plurality of batches to a training data consumer in the deterministic order. 11. The computer-readable storage medium of claim 10, wherein the shared buffer includes a different set of slots associated with each of the plurality of training data producers, the sets of slots organized according to the deterministic order. 12. The computer-readable storage medium of claim 11, wherein storing the plurality of batches in the shared buffer comprises: for a first batch, identifying a first training data producer from which the batch was obtained; identifying a first set of slots in the shared buffer associated with the training data producer; and storing the first batch in a given slot of the first set of slots. 13. The computer-readable storage medium of claim 12, further comprising determining that the given slot corresponds to the first batch based on a batch index associated with the first batch, the batch index indicating a location within the first training data producer from which the first batch was obtained. 14. The computer-readable storage medium of claim 10, wherein each of the plurality of batches is obtained by a different thread executing in a multi-threaded execution environment. 15. The computer-readable storage medium of claim 14, wherein a first batch of the plurality of batches is obtained from a first data producer by a first thread, and further comprising: before transmitting the first batch to the training data consumer, receiving a second batch obtained from the first producer by the first thread; and blocking the first thread until the first batch is transmitted to the training data consumer. 16. The computer-readable storage medium of claim 15, wherein blocking the first thread comprises: determining that a slot in the shared buffer where the second batch should be stored is occupied by the first batch. 17. The computer-readable storage medium of claim 11, further comprising: determining that no more batches are to be obtained from a first training data producer in the plurality of training data producers; and storing a dummy batch a first slot included in a first set of slots associated with the first training data producer. 18. The computer-readable storage medium of claim 10, wherein the training data consumer is a machine learning system configured to process the plurality of batches to identify one or more statistical similarities between data included in the plurality of batches. 19. A computer system, comprising: a plurality of training data producers configured to produce training data; a shared buffer for storing batches of training data obtained from a plurality of training data producers, the shared buffer configured according to a deterministic order of the plurality of training data producers; and a training data storing engine configured to: receive a plurality of batches of training data obtained from the plurality of training data producers in parallel; store the plurality of batches in the shared buffer in the deterministic order, the deterministic order being independent from an order in which the plurality of batches is received; and transmit the plurality of batches to a training data consumer in the deterministic order. 20. The system of claim 19, wherein the shared buffer includes a different set of slots associated with each of the plurality of training data producers, the sets of slots organized according to the deterministic order.