Business Analysis and Understanding
We want to help you unlock the power of your data through AI technologies. To better understand your business problems, we will work with you and other stakeholders to formulate questions that will define your goals. We will identify data sources and determine what data you need to help you set out these goals.
The huge efficiencies generated by AI could increase your sales, decrease costs and advance your business goals by anything from 10% to 100%.
Data Acquisition and Understanding
Data Acquisition
Our data engineers will then set in motion processes to move the data from the source to the target locations, where we run analytics operations such as training and predictions. We will move in the following steps.
Data Exploration
First we need to gather a sound understanding of your data. Real-world data sets are often noisy (i.e. corrupt or unstructured), incomplete, or riddled with discrepancies. Our data scientists and engineers use data summarization and visualization to audit the quality of your existing data and help you prepare for the modeling stage.
Through data exploration and visualization, we look for patterns inherent in the data to help you decide what predictive model to use for your target. We look for evidence of how well connected the data is to the target, and then determine whether there is sufficient data to move forward with the next modeling steps.
Data Pipelining
After the initial acquisition and data cleaning, our machine learning engineers will set up an automated workflow (or data pipeline) to extract data from new data sources, or refresh the data regularly as part of an ongoing learning process. We will develop solution architecture of the data pipeline, which is what you need to prepare for the next stage of the data science process.
Modeling
Feature Engineering
For our models to achieve the results we want, our data scientists will probe the information gleaned from the data exploration stage to gain insight into what drives a model. This helps us understand how the features relate to each other and how the machine-learning algorithms will use the features in your data.
Model Training
Our data architects will conceptualize the best algorithm model that will yield the answer to the question or problem that you have identified.
Deployment
OPERATIONAL MODEL
After selecting the best performing model, our AI engineers will operationalize the model. Once it’s ready for production, we will deploy the model and pipeline to a production or a simulated production environment for application consumption.
API INTERFACE
We will expose the model to or enable it to be accessible through an API interface, which allows various applications to use the model in real time.