A customer centric approach to launching ML products as a core platform capability
By: Shilpa Chidambaram
Tags: Platform Thinking, Platform, Product, Machine Learning, Product Management

A customer centric approach to launching ML products as a core platform capability Shilpa Chidambaram Mar 18·4 min read

Building an effective Machine Learning product is a hard science however it is more difficult to launch it as a platform capability. A platform caters to multiple business verticals and hence needs to be versatile, flexible and easy to use, yet effective in delivering the essential business outcomes keeping in view the data privacy tenants. This was the challenge I faced when we were working on launching offer recommendation machine learning model as a platform capability.

While employing multiple strategies to bring alive the product, the most essential pre-requisite seemed to be taking a customer centric approach. Traditionally customer is only seen as the end consumer who benefits from ML recommendations, however as a core platform capability, we had to entertain various type of users who play a crucial role in defining, tuning and maintaining the machine learning model in the platform. Such users include the operations team who install the infrastructure, data science team who tune the model periodically and analytics team who measure and report the business outcomes of these models.

Keeping in mind all the users of the platform, in my view, the following are the key considerations for any platform product manger to launch a successful machine learning product in the market.

1. Defining compelling business use cases

The first and foremost requirement in devising any ML feature starts with defining a compelling business case with clearly articulated problem statement. The need to write down the use cases with measurable business outcomes cannot be emphasised further for successful product launch. It streamlines and guides all the cross dependent teams towards unified vision and helps the product team to guide, support and channelize the decisions in the ML model throughout the product development lifecycle.

2. Data Management Platform (DMP)

Devising early the strategy to unify, cleanse or organise system data is key to success for any analytics product including machine learning models. In addition, having a structured taxonomy to define and label the data goes a long way in improving predictions for any ML model. While it is not a requirement to get started on building the first ML model, systematic planning for a centralised DMP helps in improving overall organisational goals towards being an AI centric company.

For offer recommendation, we started with defining taxonomy for offer attributes and later pursued the goal of building a unified data mart for all our analytics and machine learning objectives. It helped multiple cross functional teams such as product, data science and business analytics team to work on the common platform reducing confusion thereby errors thus improving overall business outcome.

3. Data Compliance Definition

It is imperative that data compliance is part of the product design right from inception of any ML model. It is essential that organisation is explicit in stating the governing privacy rules they comply with and address such needs in the data structures of the platform so that the subsequent teams are aware of the unavailability of such data.

In addition to the privacy rules that the organisation comply with, it is essential that data platform is also flexible to accommodate any additional data restrictions on the client side. This requires the data science team to be aware of any such possibilities and design the model such that it can self learn with available data and give the best possible predictions.

4. Explainability of AI

Even though Explainability of AI is a new concept and is possible when the ML model is mature enough to generate consistent rules, it is essential that the design of model accommodates for such explanations. The explanation of why a particular recommendation was made helps build trust in the system users and allows for better communication with end customers. It also helps in gathering useful feedback from the end users which can improve the ML outcome overtime.

5. Model parameters interface

During experimentation and post launch phase, it is required that model parameters are available to the in house and client side data scientists to fine tune the model parameters so that they can test the model consistently to improve the model outcomes. Therefore, providing a self service interface that enables data scientists to fine tune the model and deploy the AI resources on demand goes a long way in reducing operational overhead for the company. The parameters may include all or some of the infrastructure, data and machine learning model fields depending on the maturity of the underlying systems.

6. Business metrics and analysis dashboard

The ML feature launch should be treated as a series of experiments rather than a full fledged platform capability. This will ensure that the ML feature scales responsibly based on enough testing and certification from the data science team. To enable consistent communication across board about the success of ML features, a unified dashboard with key metrics is essential to be designed from the beginning. The dashboard should be flexible to zoom in and out of the metrics to enable qualitative discussions among key stakeholders.

7. Ease of Integration

In order to facilitate ease of use to the client, it is essential that the AI infrastructure installation is on demand and automated. The customer should be able to use a switch or a flag to install AI infrastructure and also scale it on demand based on the requirements of ML model and size of the client database.

8. Release Docs and User manual

Thorough documentation internal and external is essential to keep in track the changes over time and observe the aberrations over time. A clearly articulated user manual helps enhance the self service capability in end users to use the platform effectively.