By Rajaram Venkataramani and Rohini Chandrasekhar

Performing machine learning (ML) analytics on massive datasets from the Internet of Things (IoT) can generate big payoffs. Analyzing data about tracks, signals and other assets is expected to deliver significant savings and increased efficiency to one of our clients, a rail infrastructure operator, through improved preventive maintenance as well as reduced repair costs and service outages.

However, this success required overcoming significant challenges. …

By Rajaram Venkataramani, Chief Architect

Whether your goal is to improve the customer experience, your operational efficiency or safeguard the public from threats, artificial intelligence and machine learning (AI/ML) can uncover trends and insights that would otherwise be impossible to find. Deploying AI/ML quickly and at scale delivers the fastest and most accurate predictions and prescriptions for business challenges, while allowing you to adapt ML models to most quickly meet changing needs.

Just as DevOps brings new applications to market more quickly by combining the development and operations functions, MLOps encourages collaboration between data scientists and operations professionals to speed…

By Jennifer Herz, Gautham Nagabhushana, Jean-Louis Arsenault, Juan Rosa Medina Touzard and Bharanidharan Sridharan

The handwriting is now on the wall. After lagging industries like life sciences and banking, insurance is fully embracing artificial intelligence/machine learning and exploring the possibilities[i]. Insurers need to take the first step to recognize the power of data, by rationalizing and modernizing the data residing within their ecosystem and beginning to experiment with AI/ML to achieve this. AI/ML unlocks the insights that lie within your data to help you target the right customers with more compelling products at greater efficiency and better margins, fueling growth…

By Nate Greenhunt, Client Solutions Executive

With the rise in bank fraud, fraudsters are targeting bank customers in an attempt to fool them into revealing their confidential banking details, sometimes even using advanced AI algorithms such as evolutionary computation of their own. Scams like ATM card skimming and mobile SIM card swap have already bilked thousands of bank customers worldwide. Now, their scams are carried out in the form of vishing. The fraudster commits this scam entirely through a phone call and before the victim realizes, his or her money is already taken. Fraud takes other forms too, online and…

By Syed Abdul Sathar Syed Allaudeen, Data Scientist & AI Lead

R is one of the oldest and most powerful languages/environments for statistical computing and graphics. Many companies have developed predictive models using R and continue to use them in their operations. As part of cloud migration, these models need to read and write data from Amazon S3 and Amazon Redshift for training, predicting and storing the results for downstream applications such as Tableau, Power BI and web apps. When it comes to displaying meaningful and actionable results with sub-second response, a cloud-based data warehouse like Redshift is critical.


By Risto Miikkulainen, AVP of Evolutionary AI

Neuroevolution — the evolution of neural networks — is a general set of techniques that have been actively researched since the 1990s, well before deep learning.

Many of the ideas in Evolutionary AutoML originate from general neuroevolution research — and now that we have a million times more computing power, they can be applied to deep learning as well. To provide a general context for these ideas, as well as to highlight other results that may be similarly useful in the future, we recently wrote an overview on neuroevolution with Uber AI Labs…

By Karthik Balakrishnan Gopalakrishnan and Avisek Mohanty

The ability to make fast, data-driven decisions has never been more valuable as businesses grapple with the shift toward hyper-personalisation, driven by rapidly changing customer behaviours and expectations.

The pandemic has accelerated the imperative for businesses to invest in Artificial Intelligence (AI) and Machine Learning (ML) so they can replace guesswork with data-powered certainty to reorient strategy and optimize operations for success in an uncertain future.

Nevertheless, enterprises often struggle to integrate these technologies at scale and monetize the benefits. …

By Huw Kwon, Vice-President and Anil Eknath Sonavane, Senior Director

This is the third instalment in a series on how organization’s can embrace AI specifically around conversational AI and hyper-personalization. The first issue discusses AI: Leveraging Superior Customer Experiences and the second focuses on Harnessing AI.

AI-driven analytics has briskly grown as a discipline over recent years. The COVID-19 pandemic has clearly sped up this trend, yet few examples where AI has delivered significant value at scale exist. In fact, 40% of AI projects don’t get off the ground: even after a hypothesis validation, most initiatives miss the bar and…

By Fabian Dupuis, Director AI & Analytics

Second part in our Leveraging Superior Customer Experience series.

To leverage exemplary customer service with AI, businesses need to see the human side of individual customers. The first part of the story, “AI: Leveraging Customer Experiences” can be found here.


What time of day does each of your customers prefer to be approached? What kinds of messages are likely to inspire purchases? These questions get to the heart of what hyper-personalization is all about: understanding individual behaviors and preferences to create profitable customer journeys.

Segmentation and traditional marketing tools cannot meet today’s retention…

By Gregory Verlinden, AVP AI and Analytics

By intelligently combining AI technology and human science, businesses can reinvent their decision-making processes to excite customers and grow revenue.

Many missteps have been made with transformation initiatives over the past decade, partly because businesses struggle with data sourcing and data management. Data reliability is paramount to AI success. Moving forward, then, depends on data-driven digital transformations. The main difference between AI leaders and non-leaders is strategic methodology, so, this article (in part 3) closes with practical actions to achieve sustainable success with AI.

A Clear Trajectory

More than ever, AI is primed to deliver exceptional…

Cognizant AI

We help clients create highly-personalized digital experiences, products and services at every touchpoint of the customer journey.

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