Speed up Data Science

Although automating processes and mundane tasks drive digital transformation, the core business outcome is staying relevant and competitive. Artificial Intelligence and Machine Learning (AI and ML) have proven to be great differentiators for companies to stay ahead of competition and provide better customer value.

This has been true across industry verticals such as BSFI, Retail / FMCG, Healthcare, Pharma, Energy as well as Logistics.

Leading companies from each domain have invested in AI and ML. Sales and Marketing teams have been able to improve customer loyalty and manage customer churn with targeted campaigns yielding better ROI. Financial services organizations use it to profile customers, manage risk effectively and even detect / prevent potential frauds.

Sales teams have been able to use this to predict opportunities which are more likely to close, helping them focus and utilize time effectively.

HR teams streamline recruitment, predict attrition and retain talent better. 

Practically every field has a use for AI / ML

Challenges in deploying AI / ML in Enterprises

Enterprise Data is typically scattered across applications and organizations struggle to get all their business-critical data in once place. The next challenge lies in having data scientists to work on the models. There is demand and supply skew for experienced data scientists and you want them to be most productive if you have them.  Teaching data science to business users or IT consultants is not an option as building models requires a deep skillset that takes a while to acquire. The time taken to build each model can stretch to several months which leads to piling up of data science projects leading to user dissatisfaction. The business opportunity passes by.

How to overcome these Challenges

One of the ways adopted by enterprises is to subcontract the work to consulting companies. However this turns out to be not just expensive but has a very limited impact as the consultants have little knowledge about your business and struggle with the data, missing the most important features.

This problem was solved in an elegant way by DataRobot, a company built by Kaggle grandmasters and pioneers in Automated AI/ML. This approach empowers your data scientists to build and choose the best models and monitor them. Even your business analysts can build models with some training, which is very different from the years of learning it takes to be a data scientist.

Fast Track Time to market with low TCO

 Predoole Analytics has been a pioneer in empowering organizations to derive value from their data. 

 Agile deployments with fast time to market and low TCO have been the hallmark of our offerings. Large part of machine learning is the data preparation that happens before model building.  Given our expertise and work in BI and Data Warehousing, Machine Learning is a natural extension. However the shortage of experienced data scientists often delays projects. Also, in order to derive the most value, you need someone who understands your business along with data science. This is a difficult combination. And once you get someone like this, you want him to churn out as many use cases and models as he can. 

 Hence Predoole Analytics has partnered with DataRobot for an automated machine learning platform. DataRobot automates the task of choosing the best models, monitoring them, maintaining records and more, so your Data Scientist (that is in such short supply!) can focus on the art of feature engineering, ensuring that current environment is similar to the training dataset and assessing drift – things that really need business knowledge and human judgement. 

 In short we enable speeding up your modelling pipeline by 10x if not more. Not just that, they can be built by your business analyst as the guard rails and baked-in best practices from the minds of some of the best data scientists ensure that your models are as accurate and practical as possible.

Deployment and Monitoring the Models in production

 Once you choose the most accurate and relevant model, DataRobot MLOps delivers the capabilities Data Science and IT Ops teams need to work together to deploy, monitor, manage, and govern machine learning models in production.

 Teams can also monitor performance of models (data drift, specific metrics), update and refine models which includes testing and validation of the model.  This helps to maintain higher accuracy and continued end user satisfaction.

Advantages of Automated AI/ML with DataRobot and Predoole

Parallel processing of several models and faster go-live with actionable insights

Teams can focus on soft aspects like choosing right modelling dataset, ensuring that predicted situations are similar to the training dataset

Data Scientists improve their productivity and business users can be empowered to become data scientists 

Rapid and accurate deployments enabling lower TCO and faster ROI

Easy monitoring and tuning of model to maintain high accuracy levels through the life of model

Hand holding and Training

Predoole Analytics helps companies to quickly deploy DataRobot in your organization and work closely with your data science / Business analyst team to deliver initial use cases in record time. In this process, the internal teams get familiar with the platform and its optimum use.

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