The multiple independent processes run in an AI engine on its cloud-based platform. The multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions. The actions to solve AI problems can include 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, 4) deploying and using two or more trained AI models to do predictions on data from one or more data sources, 5) etc. A service handles scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus, comprising: an Artificial Intelligence (“AI”) engine having multiple independent processes on a cloud-based platform, where the multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions selected from a group consisting of 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, and 4) any combination of these three, on the same artificial intelligence engine, where a first service of the independent processes is configured to handle scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes wrapped in its own container as needed, where using additional instances of one or more of the independent processes wrapped in its own container improves the functionality of the AI engine to allow dynamic scaling for calling in additional computing devices and instances of independent processing compared to having to run a middleman daemon trying coordinate between different processes and needing to run resident on each computing device. 2. The apparatus of claim 1, where an instructor process is configured to carrying out a training plan codified in a pedagogical software programming language, and a learner process is configured to carrying out an actual execution of underlying AI learning algorithms during the training session, where the instructor process and the learner process of the independent processes cooperate with one or more data sources to train a new AI model. 3. The apparatus of claim 2, where another process of the independent processes is configured to receive scripted code to create a topology of neural networks, select one or more learning algorithms, and then instantiate a first AI model assigned with a unique identifier based on a description of a problem to solve codified in a pedagogical software programming language. 4. The apparatus of claim 2, where the first service is configured to direct a first CPU computing device to load and run an architect process, a second CPU computing device to load and run an instructor process, a first GPU computing device to load and run a learner module, and then change an amount of computing devices running independent processes by dynamically calling in a third CPU computing device to load and run a second instance of the instructor process, and a second GPU computing device to load and run a second instance of the learner module. 5. The apparatus of claim 1, where a first instance of the independent processes is wrapped into the software container to allow each instance of that independent process to run independently on whatever computing device that instance is running on, and where the first service is configured to dynamically change an amount of computing devices overtime running independent processes and to rapidly change the amount to scale to handle multiple users sending multiple AI models to be trained, where a second service is configured to cause a first instance of an instructor process to be instantiated, loaded onto a CPU computing device, and then run on the CPU computing device. 6. The apparatus of claim 1, where the multiple independent processes carry out four or more separate tasks by interaction with and cooperation between the multiple independent processes, where a first task is creating a shell of an AI model, a second task is loading in a file of scripted code in a programming language to help define a topology of processing nodes in the AI model, a layout of the concepts making up the AI model, and a selection of an appropriate learning algorithm for the AI model, a third task is starting to train the AI model with a data source, and a fourth task is then deploying and using a trained AI model and doing predictions on data from the data source. 7. The apparatus of claim 1, where the independent processes include a watchman service configured to monitor to see if any of 1) when failures occur in any of the other independent processes and 2) when any of the other independent processes exit, and then to tell the independent processes wrapped in its container to exit the training session that they were cooperating in. 8. A non-transitory machine-readable medium configured to store data and instructions, which, when executed by one or more processors on one or more computing device, causes the following operations, comprising: running multiple independent processes in an AI engine on a cloud-based platform, where the multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions selected from a group consisting of 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, and 4) any combination of these three, on the same AI engine; and running a first service of the independent processes to handle scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed. 9. The non-transitory machine-readable medium of claim 8 storing instructions to cause further following operations, comprising: running an instructor process to carrying out a training plan codified in a pedagogical software programming language; and running a learner process to carrying out an actual execution of underlying AI learning algorithms during the training session, where a first instance of the instructor process and the learner process of the independent processes cooperate with a first data source to train a first AI model, where a second instance of the instructor process and the learner process of the independent processes cooperate with a second data source to train a second AI model at the same time on the same AI engine. 10. The non-transitory machine-readable medium of claim 8 storing instructions to cause further following operations, comprising: receiving scripted code by another process of the independent processes from a first client device of a first user over a network interface to create a topology of neural networks, select a learning algorithm, and instantiate a first AI model assigned with a unique identifier, and receiving scripted code by a second process of the independent processes from a second client device of a second user over the network interface to run simulations on the same AI engine at the same time. 11. The non-transitory machine-readable medium of claim 9 storing instructions to cause further following operations, comprising: running the first service to direct a first CPU computing device to load and run an architect process, a second CPU computing device to load and run the instructor process, a first GPU computing device to load and run the learner module, and then change an amount of computing devices running independent processes by dynamically calling in a third CPU computing device to load and run a second instance of the instructor process, and a second GPU computing device to load and run a second instance of the learner module. 12. The non-transitory machine-readable medium of claim 8 storing instructions to cause further following operations, where the independent processes are wrapped into the software container to allow each instance of the independent process to run independently on whatever computing device that instance is running on, and where the first service is configured to dynamically change an amount of computing devices overtime running independent processes and rapidly change the amount to scale to handle multiple users sending multiple AI models to be trained; and where the multiple independent processes are configured to run simultaneous instances of the one or more independent processes to scale to additionally handle one or more users performing actions of 5) deploying and using two or more trained AI models to do predictions on data from one or more data sources and 4) any combination of these four. 13. The non-transitory machine-readable medium of claim 8 storing instructions to cause further following operations, where the independent processes carry out four or more separate tasks by interaction with and cooperation between the multiple independent processes; where a first task is creating a shell of an AI model; a second task is loading in a file of scripted code in a pedagogical software programming language to help define 1) a topology of processing nodes in the AI model, 2) a layout of the concepts making up the AI model, and 3) a selection of an appropriate learning algorithm for the AI model; a third task is starting to train the AI model with a first data source according to a lesson plan and curriculum, and a fourth task is then deploying and using a trained AI model and doing predictions on data from a second data source. 14. The non-transitory machine-readable medium of claim 8 storing instructions to cause further following operations, where the independent processes include a watchman service configured to monitor to see if any of 1) when failures occur in any of the other independent processes and 2) when any of the other independent processes exit, and then to tell the independent processes wrapped in its container to exit the training session that they were cooperating in. 15. A method for an AI engine having multiple independent processes on a cloud-based platform, comprising: running the multiple independent processes in the AI engine, where the multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions selected from a group consisting of 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, and 4) any combination of these three, on the same AI engine; and running a first service of the independent processes to handle scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed. 16. The method of claim 15, comprising: running an instructor process to carrying out a training plan codified in a pedagogical software programming language; and running a learner process to carrying out an actual execution of underlying AI learning algorithms during the training session, where a first instance of the instructor process and the learner process of the independent processes cooperate with a first data source to train a first AI model, where a second instance of the instructor process and the learner process of the independent processes cooperate with a second data source to train a second AI model at the same time on the same AI engine. 17. The method of claim 15, comprising: receiving scripted code by another process of the independent processes from a first client device of a first user over a network interface to create a topology of neural networks, select a learning algorithm, and instantiate a first AI model assigned with a unique identifier, and receiving scripted code by a second process of the independent processes from a second client device of a second user over the network interface to run simulations on the same AI engine at the same time. 18. The method of claim 16, comprising: running the first service to direct a first CPU computing device to load and run an architect process, a second CPU computing device to load and run the instructor process, a first GPU computing device to load and run the learner module, and then change an amount of computing devices running independent processes by dynamically calling in a third CPU computing device to load and run a second instance of the instructor process, and a second GPU computing device to load and run a second instance of the learner module. 19. The method of claim 15, where the independent processes are wrapped into a software container to allow each instance of the independent processes to run independently on whatever computing device that instance is running on, and where the first service is configured to dynamically change an amount of computing devices overtime running independent processes and to rapidly change the amount to scale to handle multiple users sending multiple AI models to be trained; and where the multiple independent processes are configured to run simultaneous instances of the one or more independent processes to scale to additionally handle one or more users performing actions of 5) deploying and using two or more trained AI models to do predictions on data from one or more data sources and 4) any combination of these four. 20. The method of claim 15, where the independent processes carry out four or more separate tasks by interaction with and cooperation between the multiple independent processes; where a first task is creating a shell of an AI model; a second task is loading in a file of scripted code in a pedagogical software programming language to help define 1) a topology of processing nodes in the AI model, 2) a layout of the concepts making up the AI model, and 3) a selection of an appropriate learning algorithm for the AI model; a third task is starting to train the AI model with a first data source according to a lesson plan and curriculum, and a fourth task is then deploying and using a trained AI model and doing predictions on data from a second data source.