AI Workflow

AI Workflow

Artificial Intelligence is a technology we are excellent at. This isn’t like anything else where you run out of things to explore and experiment with. AI keeps growing and opens up a lot of doors to explore.

The process and steps we follow to build world class AI products are,

Identify Goals

While building an AI Model we first identify the problem to be solved. There are two types of problems that can be addressed are,

Functional Business

An AI model for a functional problem would enhance the product. It could be implementing a new feature, bettering an existing feature, or even enhancing the product itself. This type of AI model is user facing. The customers for your product will be using the model frequently. The leads to a wow factor for the customer and provides an endless pool of data to be learned from.

An AI model for a business problem would lead to better understanding of your users/customer. This could be a model to predict user retention or bounce rates. Insights gained from this model could help decision makers analyze what feature of the product is being enjoyed by the user.

Data Validation

The data sets that are provided by the client will be used to train the AI. Sometimes the data provided might be insufficient to create a solution. In order to quantify the context of the data, our data scientists will need to validate it before hand. For this phase, the entire data set does not have to be presented. A small subset of data is more than sufficient.

Data Authentication andAuthorization

After validating the sample datasets, the client is required to transfer the entire data to Skcript. Data will be transferred and handled only in our secure DMZ. The collected data will not go live but will be stored and be accessible only to authorized users. Only the copy of the data will be held by us, which is also endorsed with a authority accessibility. If insisted we can set a validity date for the data, post which all digital copies of the data will be purged.

Normalizing Data

Data needs to be processed and cleaned before being analyzed by an AI model. Here, data is cleaned up to remove duplicates, fill in missing values, fix invalid data and convert to uniform format. Certain times data is also converted to easily represent outlier values. All these methods followed to normalize training data must also be processed on incoming production data.

Building AI Model

There are many AI models, methods and algorithms to be chosen from. Our AI Engineers will pick the best that suits the provided data. Many times the Normalizing Data and Building AI Model step will keep alternating until convergence is achieved. In a few rare cases, data might need to augmented or processed for higher quality also.

Testing and Training AI Models

The testing step occurs in sync with Building AI Model step. Each AI Model is tested with provided data and accuracy scores for each are tabulated. The top three are chosen to be processed with training data. Once testing and training the AI Model is achieved, it is then validated against production data. At the end of this step three AI models with high accuracy is produced.

Production Integration

Once an accurate AI Model is chosen, it then needs to converted into a process that can run on the server. Proper endpoints to feed data and retrieve results are also added to the service. This process is independent of any language or datastore/database restrictions. However, it will be most compatible on a Linux based server. This process can now either be installed in clients production server or can be hosted on the cloud for better scalability.

Updating learning model and maintenance

As more data is gathered the AI model has to be continually updated and maintained to stay relevant. The updation can occur automatically at set schedules. A small package to rebuild the model with new data will be provided. However, it is best recommended to have our AI Engineers routinely monitor the system.

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