CORE
CEO at Tuhin AI Advisory and Professor of Practice at JAGSoM
Company website: https://www.tuhin.ai/
Dr. Tuhin Chattopadhyay is a celebrated technology thought leader among both the academic and corporate fraternity. Recipient of numerous prestigious awards, Tuhin is hailed as India's Top 10 Data Scientists by Analytics India Magazine. Besides driving his consultancy organization Tuhin AI Advisory, Dr. Tuhin also serves as Professor of Practice at JAGSoM, Bengaluru. His professional accomplishments can be explored from https://www.tuhin.ai/, art portfolio from https://tuhin.art/, joie de vivre from https://tuhinism.com/ and adventures with MySon from https://dogfather.rocks/.
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AI/ML
AI Automation Essentials
Getting Started With Large Language Models
Machine Learning Patterns and Anti-Patterns
Neural Network Essentials
Data Engineering
Over a decade ago, DZone welcomed the arrival of its first ever data-centric publication. Since then, the trends surrounding the data movement have held many titles — big data, data science, advanced analytics, business intelligence, data analytics, and quite a few more. Despite its varying vernacular, the purpose has remained the same: to build intelligent, data-driven systems. The industry has come a long way from organizing unstructured data and driving cultural acceptance to adopting today's modern data pipelines and embracing business intelligence capabilities.This year's Data Engineering Trend Report draws all former terminology, advancements, and discoveries into the larger picture, illustrating where we stand today along our unique, evolving data journeys. Within these pages, readers will find the keys to successfully build a foundation for fast and vast data intelligence across their organization. Our goal is for the contents of this report to help guide individual contributors and businesses alike as they strive for mastery of their data environments.
Enterprise AI
Artificial intelligence (AI) has continued to change the way the world views what is technologically possible. Moving from theoretical to implementable, the emergence of technologies like ChatGPT allowed users of all backgrounds to leverage the power of AI. Now, companies across the globe are taking a deeper dive into their own AI and machine learning (ML) capabilities; they’re measuring the modes of success needed to become truly AI-driven, moving beyond baseline business intelligence goals and expanding to more innovative uses in areas such as security, automation, and performance.In DZone’s Enterprise AI Trend Report, we take a pulse on the industry nearly a year after the ChatGPT phenomenon and evaluate where individuals and their organizations stand today. Through our original research that forms the “Key Research Findings” and articles written by technical experts in the DZone Community, readers will find insights on topics like ethical AI, MLOps, generative AI, large language models, and much more.
Observability and Application Performance
Making data-driven decisions, as well as business-critical and technical considerations, first comes down to the accuracy, depth, and usability of the data itself. To build the most performant and resilient applications, teams must stretch beyond monitoring into the world of data, telemetry, and observability. And as a result, you'll gain a far deeper understanding of system performance, enabling you to tackle key challenges that arise from the distributed, modular, and complex nature of modern technical environments.Today, and moving into the future, it's no longer about monitoring logs, metrics, and traces alone — instead, it’s more deeply rooted in a performance-centric team culture, end-to-end monitoring and observability, and the thoughtful usage of data analytics.In DZone's 2023 Observability and Application Performance Trend Report, we delve into emerging trends, covering everything from site reliability and app performance monitoring to observability maturity and AIOps, in our original research. Readers will also find insights from members of the DZone Community, who cover a selection of hand-picked topics, including the benefits and challenges of managing modern application performance, distributed cloud architecture considerations and design patterns for resiliency, observability vs. monitoring and how to practice both effectively, SRE team scalability, and more.
Automated Testing
The broader rise in automation has paved the way for advanced capabilities and time savings for developers and tech professionals, especially when it comes to testing. There are increasingly more conversations around how to transition tests to an automated cadence as well as a deeper push toward better automated testing integration throughout the SDLC. Solutions such as artificial intelligence (AI) and low code play an important role in implementing tests for development and testing teams, expanding test coverage and eliminating time spent on redundant tasks. It's a win-win-win.In DZone's 2023 Automated Testing Trend Report, we further assess current trends related to automated testing, covering everything from architecture and test-driven development to observed benefits of AI and low-code tools. The question is no longer should we automate tests; it's how do we better automate tests and integrate them throughout CI/CD pipelines to ensure high degrees of test coverage? This question will be examined through our original research, expert articles from DZone Community members, and other insightful resources.As part of our December 2023 re-launch, we've added updates to the Solutions Directory and more.
Data Pipelines
Data is at the center of everything we do. As each day passes, more and more of it is collected. With that, there’s a need to improve how we accept, store, and interpret it. What role do data pipelines play in the software profession? How are data pipelines designed? What are some common data pipeline challenges? These are just a few of the questions we address in our research.In DZone’s 2022 Trend Report, "Data Pipelines: Ingestion, Warehousing, and Processing," we review the key components of a data pipeline, explore the differences between ETL, ELT, and reverse ETL, propose solutions to common data pipeline design challenges, dive into engineered decision intelligence, and provide an assessment on the best way to modernize testing with data synthesis. The goal of this Trend Report is to provide insights into and recommendations for the best ways to accept, store, and interpret data.
Enterprise AI
In recent years, artificial intelligence has become less of a buzzword and more of an adopted process across the enterprise. With that, there is a growing need to increase operational efficiency as customer demands arise. AI platforms have become increasingly more sophisticated, and there has become the need to establish guidelines and ownership.In DZone's 2022 Enterprise AI Trend Report, we explore MLOps, explainability, and how to select the best AI platform for your business. We also share a tutorial on how to create a machine learning service using Spring Boot, and how to deploy AI with an event-driven platform. The goal of this Trend Report is to better inform the developer audience on practical tools and design paradigms, new technologies, and the overall operational impact of AI within the business.This is a technology space that's constantly shifting and evolving. As part of our December 2022 re-launch, we've added new articles pertaining to knowledge graphs, a solutions directory for popular AI tools, and more.
