In the current industry, where data is the new oil, every organisation is trying to make sense out of data and help to catalyse their business functions. Needless to say, it’s also witnessed that the organisations are also trying to create the right methodology to have data being processed, so they can be utilised in the right as part of the overall cognitive journey.
The customer support industry follows technological advancement with keen interest. The customer functions for giant financial institutions have been early adopters of disruptive technologies like Artificial Intelligence.
Also, what this implies, hiring the right team of data scientists and engineers, and creating the models and algorithms, mostly specific to the type and structure of data, considering the complexities of the domain specific functions.
If you are already profitable with a human-driven process, you likely have the opportunity to build an incredible technology platform on top of that process. In this case, Machine Learning is an operating leverage force multiplier for both your core business as well as a new business as a Technology Platform.
The article would help to understand the key steps and takeaways, w.r.t. what would one product manager need to consider in order to build a generic machine learning platform, i.e. automating the machine learning process. This process would accelerate the complete machine learning building lifecycle in a usable manner, assisting the business team to understand the nuances of the data science process in a more consumable business way.
Business Problem and Product Approach
Customer Success Manager, one would need to (to mention a few):
As product manager, one would try to create a platform/product to answer (to mention a few):
Key Steps for a Product Manager and Design Thought Process
Understand the Data analytics problem
The business problem, based on the need and understanding, need to be converted into an analytics problem, primarily, can be classified into three parts:
In this article, we will restrict our discussion to Descriptive and Predictive Analytics
Descriptive analytics consist of features like:
Predictive analytics problem lies here is both classification and regression. Depending upon the problem statement and target response variable, it will differ. For an example, if the response variable is a binary variable like ‘Call-Success’ which has 2 category,- ‘Yes’ and ‘No’, then it is a binomial classification problem and if the response variable is a continuous one which takes numeric values, such as ‘Call Duration’ or ‘No. of Agents’ then it will be a regression problem.
Approach and Features:
The predictive analysis is subdivided into two parts: Model building and Prediction
Implementation methodology & challenges
With such ML platform, a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost. This would accelerate the ML process and assist developers and data scientists – needless to mention, core business users to make sense out of data and take necessary actions. With usage of micro-service and API based architecture, this can further be fed to a more rich visualization platform