Our AI-ML Development Process
Understanding what the client exactly wants from the business perspective is crucial as it is always helpful to have a clear understanding of all the expectations right from the beginning. Business understanding allows us to determine KPIs – to have clarity whether the customer wishes to make predictions or wants to improve sales or minimize the loss or optimize any particular process etc.
Much of the data is collected from the various processes followed in an enterprise. At many levels, the data is recorded in various software systems used in an organization and it helps in understanding the process followed from the product development to deployment. In addition, transactional data also plays a vital role as it is collected on a daily basis. Thereafter, we apply methods to the data to extract the important information related to the business or project.
Data preparation is the process of structuring and organizing the collected data so it can be used in business intelligence (BI), analytics and data visualization applications in later stages. A few components of data preparation include data preprocessing, profiling, cleansing, validation and transformation.
Exploratory Data Analysis
Exploratory Data Analysis refers to the process of performing initial investigations on collected data in order to discover patterns, to spot abnormality, to test and check assumptions with the help of statistics and graphical representations.
So, we’ve reached a stage where we can prepare a descriptive diagram of relationships between various types of information that are to be stored in a database. One of the primary goals of data modeling is to create the most efficient method of storing information and simultaneously providing complete access and reporting.
Here we use different evaluation metrics to understand a machine learning model’s performance- its strengths and weaknesses. Model evaluation is important to gauge the efficacy of the said model during initial research phases, and it also plays a role in model monitoring. This is the last stage in the data science life cycle and sometimes can be one of the most cumbersome.
Now since we’ve tested the ML model – it’s time to expose the ML model to real use. In this stage, we integrate the machine learning model into the existing production environment to make practical business decisions based on data.