Enterprise organizations have large amounts of data which is utilized by multiple Machine Learning (ML) models over various software frameworks. These models provide trends and insights from the data that can help enterprises define business rules around their processes. However, if certain aspects of this data are removed from the datasets, it could influence the business rules and policies in place. When a user requests data to be removed, the model retraining may be required called Machine Unlearning (MU). Recent research works in the area of MU include different methods of retraining the machine learning models. It turns out that there is lack of work in removing certain aspects of data, and quantifying its impact on the models. This paper aspires to provide a novel methodology IDMU (Impact Driven Machine Unlearning) that performs quantification of the impact of data removal requests while performing MU. Our method provides recommendations for data removal requests, factoring in underlying features of data. The results from the industrial application and evaluation of our method on a financial services dataset are encouraging. The overall IDMU had a mean MAPE of 10.25% over a set of 120 data removal requests. It also saved ~1900 hours of model retraining time by factoring in urgency and impact of data removal requests over a period of three years.

Enterprises are rapidly working on strategies to migrate their applications to cloud. Multi-cloud allows mixing and matching multiple cloud vendors when migrating thousands of applications based on their set of requirements and various different types of constraints. To utilize the advantages of different clouds, achieve maximum flexibility and avoid concentration risk, enterprises spread their applications across cloud providers. But this activity is not trivial as it would require honoring the constraints which the customer has, and at the same time generating the most optimal configuration of cloud resources.There are a few challenges associated with this. Firstly, the applications to be migrated need to be documented well, in order to migrate them successfully. Some applications may be very old (legacy) and need an architect overhaul which means the cloud feasibility needs to be checked. Also, enterprises would like to have an optimal list of cloud vendors that satisfy their need. To overcome these challenges, we propose a smart system for determining multi-cloud pathway for applications. The system identifies cloud feasible applications, understands their requirements and recommends optimal set of cloud vendors honoring their constraints. This is enabled through Reinforcement Learning with Human-in-the-Loop. We show our results with a use case from real world scenario.

When Information Technology (IT) service providers cater services to their customers, there’s a common practice to document the intent of buyer to purchase them through a Purchase Order (PO). The details of a PO document are complex in nature, as they include hierarchical structure of sub-services, price points over the duration of PO, base setup cost, billing frequency, renewal terms etc. In large enterprises with high volume PO’s, the traditional approach of managing PO’s with their invoices involved a great deal of inefficiency and labor-intensive manual work due to lack of automation and disconnected processing. As a result, they often suffered tedious manual monitoring and failed PO renewals, resulting in delays and added costs.In this paper, we propose a joint time-series learning framework that tackles the high dimensionality PO data. It identifies the metrics to monitor, while using time-series prediction coupled with tone analysis to reach out to customers, in order to maximise PO renewals. We demonstrate utility of our approach by implementing our method on a dataset from a global IT service provider that indicates encouraging results.