Nikita Sachdeva

Artificial Intelligence (AI) has introduced major breakthroughs as fintech solutions in the finance industry. Risk management, customer segmentation, credit scoring, personalized banking, process automation, etc. are some of the advantages of AI and its technologies that are leveraged by the finance sector.

Conversational systems that leverage Artificial Intelligence (AI) have helped automate a wide range of business processes, especially those involving interactions with the customer. Natural Language Processing (NLP) comes into play for a majority of these processes, but it is often hindered by functional hurdles. Reinforcement learning is a method for navigating these hurdles to make NLP-driven business processes more seamless.

There are Yottabytes of sensitive data being generated from the interfacing of humans with machines. For cost-effective and optimal enrichment of this data, Machine Learning (ML) algorithms are our best bet. One of the most reliable categories of ML algorithms is clustering algorithms, irrespective of the complexity of data.

Natural Language Processing (NLP) is a pre-eminent AI technology that enables machines to read, decipher, understand, and make sense of human languages. From text prediction and sentiment analysis to speech recognition, NLP is allowing machines to emulate human intelligence and abilities impressively.

Artificial intelligence (AI) has made waves across industries, revolutionizing various fields of work, and the cybersecurity sector is no exception. With its rapid evolution, AI has become a driving force in shaping the future of cybersecurity. As organizations strive to safeguard their sensitive data and protect their systems from the ever-changing threat landscape, the need for AI-driven patch management has…

In a world where machine learning is ubiquitous, the power of this technology is undeniable. From helping diagnose illnesses to predicting customer behavior, machine learning has disrupted countless industries with its endless possibilities. However, with great power comes great responsibility, and one of the greatest challenges facing machine learning is its susceptibility to deception.

As our reliance on technology intensifies, the demand for faster and more powerful solutions becomes increasingly pressing. While classical computers have served us well, they face inherent limitations when tackling complex problems. That's where quantum computing comes in—a paradigm that taps into the enigmatic principles of quantum physics to unlock computational power.

As the popularity of public cloud services soared and businesses rushed to migrate their applications, the practice of monitoring and reporting on cloud expenses swiftly became an essential task for every team. However, what many organizations soon realized was that while a cloud migration may offer savings on data center costs, it also presents a whole new set of financial…

In the vast landscape of e-commerce, where industry giants dominate and small businesses often struggle to find their footing, a beacon of hope has emerged—the Open Network for Digital Commerce (ONDC). Endorsed by the Indian government, ONDC aims to level the playing field by providing small merchants and mom-and-pop stores with the technological capabilities typically reserved for their larger counterparts…

Monitoring application performance has always been a crucial aspect of software development. However, with the rise of distributed systems and cloud-native architectures, it has become increasingly challenging. The complexity of modern software systems has made it difficult to collect and analyze telemetry data effectively. This has led to a growing need for a standardized observability framework that can provide deep…