Abstract: User data is collected regarding the user's lifestyle, preferences, and medical conditions and used to recommend a cannabis product. The collected user data is clustered into a group with user data from other users to create a user profile, and a package of cannabis products is created and provided to the user based on the user profile. A recommendation engine recommends a strain of cannabis product within the package to the user based on a purpose or occasion provided by the user for the cannabis consumption. To evaluate the effectiveness of the recommendation for the given purpose or occasion, pre-consumption and post-consumption video data of the user is captured and processed by a deep learning system for the selected strain. Feedback data is also collected from the user to feed back to fine tune the clustering and the contents of the package recommended for the user profile.

Abstract: A finite state automata (FSA) may comprise of a plurality of states and a plurality of events that are triggered to transition between the plurality of states to enable the continuous delivery of one or more machine learning (ML) models. Datasets may be uploaded by one or more users to a computing device. Each dataset may include columns of attributes and/or rows of data for each attribute. Each dataset may be analyzed according to respective predefined ML model criteria. Each dataset may be automatically transformed and/or enhanced to meet the predefined ML model criteria. Data analysis may be performed on each dataset to inform the building of the ML models. An algorithm may be received for each ML model. Each ML model may be built based on the respective dataset and the respective algorithm to generate an ML model file. Logs may be stored for tracking the process performed by the FSA.

Abstract: A finite state automata (FSA) may comprise of a plurality of states and a plurality of events that are triggered to transition between the plurality of states to enable the continuous delivery of one or more machine learning (ML) models. Datasets may be uploaded by one or more users to a computing device. Each dataset may include columns of attributes and/or rows of data for each attribute. Each dataset may be analyzed according to respective predefined ML model criteria. Each dataset may be automatically transformed and/or enhanced to meet the predefined ML model criteria. Data analysis may be performed on each dataset to inform the building of the ML models. An algorithm may be received for each ML model. Each ML model may be built based on the respective dataset and the respective algorithm to generate an ML model file. Logs may be stored for tracking the process performed by the FSA.