Conf42 Machine Learning 2025
2025-05-08
-- online --
[Conf42 Machine Learning 2021, Conf42 Machine Learning 2022, Conf42 Machine Learning 2023, Conf42 Machine Learning 2025, Conf42 Machine Learning 2026]
| title | author | length |
|---|---|---|
| Fighting Harmful Content with AI: Real-World Safety | Georgina Tryfou | 16m |
Gcorp builds a moderation pipeline that detects CSAM and high-risk abuse at scale, scanning against a backdrop of class imbalance and ~500 hours of video per… | ||
| Speaking the Same Language: Let AI (Finally) Understand You | Laly Bar-Ilan | 22m |
Claims that teaching AI to work with components lets it compose full products instead of spitting out snippets, using an architecture‑first flow that (1)… | ||
| Harnessing the Power of Generative AI for Intelligent Applications | Shivali Naik | 15m |
Claims concrete: 90% of enterprise data is unstructured, ChatGPT hits 1M users in 5 days. Explains actual stack: RAG with embeddings and a vector DB plus LLMs… | ||
| Transforming Financial Decision Intelligence Through Advanced NNs | Swapna Anugu | 18m |
Claims live or fail: a deployed anomaly detection framework uses behavioral biometrics and cross‑channel network analysis to cut false alerts while keeping… | ||
| Revolutionizing Healthcare Population Management | Roja Perumandla | 12m |
Claims an SMS platform with HL7 FHIR EHR integration and event-driven architecture that routes intelligent, sequenced messages to close care gaps. Provides… | ||
| Innovations in Hardware Verification for High-Speed Protocol Testing | Jena Abraham | 25m |
A hybrid verification stack combines formal methods, cycle‑accurate simulation and an AI-driven core that prioritizes and generates tests using custom neural… | ||
| The Limits of Imagination : An Open Source journey | Aroma Rodrigues | 44m |
A readable inventory of claims tools results and failures: a terms‑and‑conditions pipeline trains on 2,000 hand-annotated sentences to extract obligations… | ||
| Leveraging Global HCM Systems for HR Digital Transformation | Swetha Priya Sathiyam | 17m |
Claims that 93% of companies plan an HR overhaul in three years and that 82% move to cloud while 42% target efficiency gains drive the decision. The talk lists… | ||
| Transition from Manual Management to Self-Optimizing Systems | Madhuri Koripalli | 34m |
Claims concrete outcomes: four implemented pillars—metadata intelligence, self-healing pipelines, predictive resource optimization and embedded… | ||
| AI’s Hidden History: Bias, Power, and the Builders Who Shaped It | Lianne Potter | 28m |
An embedded anthropologist spends eight years in four US labs in the 1980s–90s watching medical AI be built from interviews with single experts and textbooks… | ||
| AI Coding Agents and how to code them | Alex Shershebnev | 48m |
Demo shows concrete claims and working code: an agent loop using GPT4.1 plus a REACT thought‑action loop calls tools exposed via MCP servers (bash, file… | ||
| The Rise of Agentic AI and Its Transformative Potential | Naman Goyal | 19m |
Claims concrete performance gains: hierarchical planning yields about 45% higher success rates than flat planning on complex tasks; adding explicit semantic… | ||
| Beyond Chatbots: How Amazon Bedrock Agents Drive Enterprise AI | Samuel Baruffi | 43m |
Claim: Amazon Bedrock Agents run managed, production-grade multi-agent workflows that orchestrate tools, knowledge bases and models with built-in… | ||
| Revolutionizing Operations with Intelligent Automation | Balakrishna Sudabathula | 18m |
Concrete wins and tools: a delivery routing system cuts late deliveries and boosts satisfaction; an online retailer uses review analysis to reduce returns… | ||
| AI-powered Productivity using Open-Source LLMs | Ayodele Samuel Adebayo | 26m |
Claims that running open‑source LLMs locally cuts cost and latency, supports fine‑tuning, and keeps code private, demonstrated by integrating models with VS… | ||
| Beyond Barriers: AI Reshaping Digital Accessibility | Vignesh Kuppa Amarnath | 13m |
Claims concrete outcomes: a healthcare portal reaches 89% engagement after integrating voice interaction, context‑aware adaptive layout and personalized color… | ||
| Artificial Intelligence for Store Assistance | Hari Prasad Bomma | 23m |
Claims concrete ROI: a machine-learning pipeline using regression and clustering yields 12% reduction in inventory waste, customer segmentation that raises… | ||
| Implementing Robust AI Security for Enterprise Innovation | Chintan Udeshi | 24m |
Claims 86% of executives expect AI mainstream by 2025 and 60% plan AI in mission‑critical systems, and that drives specific risks: training‑data poisoning… | ||
| Transforming Financial Services with Real-Time Analytics | Sudhakar Kandhikonda | 13m |
A cloud-based real-time fraud system is described with concrete results: 167.3 ms average processing for the prediction engine, 237 behavioral indicators, and… | ||
| Democratizing AI: AutoML Transforms Predictive Analytics | Suresh Kumar Maddala | 21m |
Claims concrete numbers: 60 to 80% faster development, 30% reduction in downtime, and ROI within 12 to 18 months for enterprise AutoML adoption. Describes… | ||
| Demand Forecasting in Supply Chain Using AI Basis Machine Learning | Jubin Thomas | 09m |
Claims that XGB regressor wins on the Walmart dataset using Python out‑of‑the‑box models and basic parameters, reporting 97.2% test accuracy versus lower test… | ||
| Contract Rate Optimization: Reducing Payment Discrepancies | Uday Bag | 17m |
Predictive models and workflow integration cut negotiation time and payment errors, with a cited case showing 42% faster contract lock-in and fewer provider… | ||
| How Generative AI is Reshaping the Future of Smart Manufacturing | Kumar Nayan | 18m |
Generative AI claims concrete wins: almost 40% reduction in product development timelines at an aerospace supplier, 35% OEE improvement and packaging-line… | ||
| Revolutionizing Predictive Analytics | Raghavender Puchhakayala | 12m |
Claims concrete wins: a deployed AI-driven scheduling framework using LSTM and isolation forest that tests on AWS Azure and Google Cloud and yields up to 40%… | ||
| AI-Powered Predictive Analytics: Optimize Performance | Solomon Chigurupati | 26m |
Claims a layered AI system with data collection layer, analysis engine, optimization engine and controlled implementation layer that uses reinforcement… | ||
| Challenges & Solutions for AI-Based Message Summarization | Swapnil Hemant Thorat | 30m |
Claims a three-layer pipeline (text normalization/tokenization → semantic analysis → summary generation) implemented with multi-threaded message queues and… | ||
| AI and ML for Centralized Patient Data Repositories | Vishal Kumar Jaiswal | 22m |
Architecture description focuses on concrete claims not promises: a 38% operational cost reduction and 52% improved data accessibility from cloud‑native… | ||
| How Modern Architecture Slashed Cost & x2 Deployment Speed | Tarun Kumar Chatterjee | 15m |
Claims specific outcomes and techniques: 65% reduction in infrastructure management, 40% cost savings, and deployments move from weeks to deployments in 2–4… | ||
| Engineering AI-Powered Predictive Healthcare Systems | Darshan Patel | 23m |
Claims concrete numbers: 70% improvement in early cardiovascular risk detection, about a week of advanced warning, and one-third reduction in ER visits… | ||
| API-Driven Architectures Deliver 60% Faster Time-to-Market | Rishi Nareshbhai Lad | 14m |
Claims specific numbers and concrete tools: an API integration fabric of 300+ integrations tying 110+ systems yields 60% faster time-to-market and cuts… | ||
| ML-Powered Search & Recommendation 101 | Sergey Polyashov | 23m |
Claims concrete numbers: recommendations drive 30% of YouTube views, 35 of Amazon purchases, and 80% of Netflix watches; production pipelines use a multi-stage… | ||
| Real-Time Fraud Detection at Scale | Satyanandam Kotha | 25m |
Architecture uses Apache Kafka for ingest and Apache Flink for stream processing, deploying models horizontally and a custom graph DB plus GNNs for… | ||
| AI-Powered Cognitive Retail Transformation | Rajkumar Chindanuru | 23m |
A four‑layer cognitive framework embeds APIs and event streams, machine learning for predictive analytics, a hyperpersonalization engine and intelligent… | ||
| Test Automation: Managing Complexity and Enhancing ROI | Sujeeth Manchikanti | 17m |
Claims a utility-based framework that decouples heavy flows into small tools (Python pandas / PySpark utilities) with standard logging and clear entry/exit… | ||
| Optimizing Data Pipelines with Azure Data Factory | Lokeshkumar Madabathula | 07m |
Claims concrete outcomes: Azure Data Factory orchestrates linked services, datasets, activities and pipelines with event triggers, retries and checkpoints and… | ||
| Microservices vs. Monolith for Scalability and Efficiency | Asif Mehboob | 19m |
Argues concrete tradeoffs: internal function calls run around 1 ms internal calls while service-to-service adds 50–100 ms API latency, microservices increase… | ||
| The Role of AI in SAP and ERP Systems | Venkata Kalyan Chakravarthy Mandavilli | 19m |
Slides claim 97.2 billion global ERP market by 2024 and concrete outcomes like 32% operational cost reduction in year one and automation eliminates up to 40%… | ||
| Revolutionizing Customer Experience Through AI & Data | Mohan Krishna Mannava | 25m |
Claims that siloed systems block a 360° customer view and that consolidating behavioral, profile, transaction and sentiment data into a single store enables… | ||
| High Availability & Disaster Recovery for Cloud SAP Systems | Naveen Karuturi | 21m |
SAP uptime is quantified: $8,750 per minute and potential losses over $2.2 million per incident, with 84% of Fortune 500 on SAP and 92 million users affected… | ||
| Architecting Intelligent Platforms | Baba Prasad Pendyala | 18m |
Cloud-based ML platforms use TensorFlow on cloud and Kubeflow on Kubernetes plus MLOps and cloud ML services to deploy models that analyze vitals labs and EHR… | ||
| Resource Management Innovation for AI Workloads in Cloud Computing | Shreya Gupta | 19m |
Claims concrete savings and measured behaviors: up to 40% utilization improvement and 25–35% cost reduction from ML-driven allocation, profiling accuracy… | ||
| AI-Powered Cloud Security: Lessons from Enterprise CRM | Nagasruthi Kattula | 16m |
Concrete claims and numbers decide: Starbucks embeds Einstein analytics integration in Salesforce, processing 87 million customer records with anomaly… | ||
| Harness Digital Innovation for Environmental Protection | Sneha Deepika Kalagarla | 12m |
Claims of concrete gains appear throughout: ISTA solutions report 23% better data accuracy, 35% lower admin overhead and 38% improved reporting, while… | ||
| Building Resilient ML Cloud Integrations | Naga Swetha Kattula | 19m |
Claims that 78% of ML deployments run across multiple clouds and cloud spend will exceed $1 trillion by 2028, then lists concrete patterns and tools: REST… | ||
| Strategic Framework for Successful Oracle Migrations to AWS | Krishna Anumula | 15m |
Claims 62% of enterprises cite data complexity issues and 47% suffer failures from missing dependency maps, recommends 3–5 pre‑migration iterations, and… | ||
| Harnessing AI for Autonomous Threat Defense in Multi-Cloud Security | Pradeep Kurra | 17m |
Claims concrete operational gains: AI reduces false positives by 30%, correlates Azure/AWS/GCP events across SIEMs and SOARs, and response times drop from 6… | ||
| Maximizing ROI Through Distributed Computing and Multi-Cloud | Ravi Kumar Amaresam | 08m |
Claims measurable results: 50% maintenance cost reductions and latency cut from 300+ ms to ~40 ms from edge inference; manufacturing anomaly detectors run on… | ||
| Mastering Multi-Cloud ERP Integration | Srinivasan Pakkirisamy | 29m |
Claims concrete outcomes: 67% of multicloud projects overrun, 43% of dev effort goes to semantic mapping, and 6–15% of bidirectional sync records hit… | ||
| Blueprint for the Cloud: Building a Future-Ready Data Strategy | Raman Kapoor | 14m |
Claims that cloud migration needs a concrete data strategy not platitudes and shows actionable steps: inventory legacy ETLs and integrations, enforce… | ||
| Machine Learning: Catalyst for Cloud-Native Digital Transformation | Tejaswi Katta | 09m |
Claims include 10–20× faster training on parallelized pipelines and handling petabyte‑scale data, with MLOps platforms cut deployment time ~80% and lifecycle… | ||
| Data Management in Multi-Cloud Enterprise Environments | Prakash Vanga | 33m |
Claims concrete results: an AI integration platform that connects AWS S3, Azure Monitor and Google BigQuery, delivers 85% reduction in manual data mapping… | ||
| Enhancing Cloud Scalability and Fault Tolerance | Rahul Singh Thakur | 13m |
A concrete architecture proposes sharding the service itself with a metadata service that maps IP or customer credentials to a consistent shard, plus a… | ||
| AI-Powered Zero-Touch Integration | Narendra Chennupati | 15m |
Claims a 70% cost reduction 60% time saving and 90% risk reduction from a built zero-touch AI-driven middleware that uses natural language processing… | ||
| Predictive Analytics for 99.98% Oracle Cloud Availability | Sunil Yadav | 20m |
Claim of 99.98% Oracle Cloud availability backed by deployments across hundreds of enterprises, a monitoring→learn→optimize→validate pipeline, and concrete… | ||
| AI-Driven API Resilience in Cloud Ecosystems | Vamsi Krishna Munnangi | 14m |
Claims include concrete numbers and deployed patterns: AI models tune an API gateway to 3500 requests per second and report a 67% increase in throughput while… | ||
| AI-Powered Anomaly Detection in Cloud Data Pipelines | Santosh Kumar Sana | 29m |
AI system detects pipeline drift and subtle distribution shifts using variational autoencoders and LSTMs, runs as containerized microservices on AWS/Azure/GCP… | ||
| Low-Latency Solutions for Resource-Constrained Environments | Sai KR Pentaparthi | 31m |
Claims concrete optimizations and outcomes: hardware-focused tips (NPUs, Jetson Orin, TensorRT, TF Lite, ONNX), quantization workflows (PTQ and QAT to int8 or… | ||
| Designing a Scalable Data Governance Framework | Kushal Shah | 08m |
Scalable multi-tenant governance delivers concrete artifacts: role-level security in a shared environment, dedicated encryption keys per tenant plus ownership… | ||
| AI-Powered Cloud Automation – transforming cloud operations | Rahul Amte | 09m |
Claims that AI creates predictive scaling for flash sales by forecasting demand and pre-provisioning capacity, that it achieves self-healing… | ||
| Responsible AI with Amazon Bedrock Guardrails | Mohamed Najaaf | 20m |
Lists concrete defenses: a configurable prompt injection slider (none→high) that detects and blocks instruction‑overrides, a profanity filter, tunable… | ||
| Building Trust in AI-Enhanced Public Sector Systems | Sanjiv Kumar Bhagat | 11m |
Claims include 60% reduction in incidents, 75% administrative overhead elimination, and 82% enhanced service accuracy supported by case studies reporting… | ||
| Machine Learning for Advanced AI Search | Sowjanya Pandruju | 27m |
Claims that embeddings come from Amazon Bedrock and get stored in either Amazon OpenSearch or PostgreSQL with PGVector and demonstrated a small product‑catalog… | ||
| Front-End Analytics for Smarter Products and Engineering Growth | Raju Dandigam | 11m |
Shows an actual Amplitude setup in React and a concrete signup funnel where 70% start and only 20% finish, so the claim is measurable not vague. Walks through… | ||
| How Tool Proficiency Drives Higher Career Outcomes | Sudhakar Guduri | 07m |
Claims concrete outcomes: multi-tool practitioners see 42% higher salaries and grow 3–3.5x faster; a fintech case reports $2.8 million saved and 64% faster… | ||
| Harnessing Microservices for Scalable, Fault-Tolerant ML Systems | Abhishek Walia | 21m |
Claims microservices let inference, training and APIs scale and fail independently and give language freedom while costing data consistency, network overhead… | ||
| Addressing Privacy, Bias, and Fairness in AI Systems | Arun Vivek Supramanian | 11m |
Concrete claims and outcomes guide decisions: systems use differential privacy (ε≈2–4 at Apple, ≈0.3 at Google) and federated learning plus GAN synthetic data… | ||
| Machine Learning for Smarter Expense Management | Vishal Gangarapu | 20m |
Claims include a baseline 27% forecast inaccuracy and 42% classification error for traditional methods and a case study that cuts misclassification from 38% to… | ||
| How AI Transforms Enterprise Data Pipelines with 85% Gains | Gopinath Govindarajan | 25m |
Claims concrete 85% efficiency gains claimed with measured case studies: finance productivity up 37%, healthcare classification automation 73%, retail… | ||
| Beyond ETL: Revolutionizing Data Engineering Workflows | Janardhan Reddy Kasireddy | 16m |
Claims concrete gains: built self-healing pipelines that detect anomalies, run root-cause analysis, pull fixes from a library and learn, cutting debugging from… | ||
| Intelligent Notification Systems at Scale | Ankita Kamat | 25m |
System designs for billions of notifications daily with target 1ms response latency using microservices, event sourcing/CQRS, gRPC and Kafka + Redis (plus… | ||
| The Synergistic Impact of Artificial Intelligence on DevOps | Kowshik Sakinala | 12m |
Claims concrete outcomes: GitHub Copilot generates whole functions, tests and docstrings and adapts to a codebase; a reported 30–50% faster development and… | ||
| AI-Driven Security as Code in Jenkins | Sarathe Krisshnan Jutoo Vijayaraghavan | 20m |
Claims that Jenkins pipelines run SAST SCA DAST and container scans with policy as code and secret management, catching vulnerabilities 17× faster and… | ||
| Dynamic Rebranding in DITA: Scale Content for Co-Branded Experience | Rakesh Konda | 18m |
Claims a single-source DITA setup on AEM Guides that uses modular topics, key maps, and data vals to swap names, logos, screenshots and feature lists per… | ||
| Optimal Video Compression Using Pixel Shift Tracking | Hitesh Saai Mananchery | 25m |
Claims an AI pipeline that tracks individual pixels with a PIPs persistent independent particles model and a deep contextual network, nullifying redundant… | ||
| How Salesforce’s Ecosystem Drives Collaborative Success | Sekhar Midathana | 37m |
Lists concrete metrics and concrete builds so worth skimming: AppExchange claims 8,000 apps, ~90 million installs and 6.2 billion revenue for partners… | ||
| Data Quality and Validation in ML Pipelines | Sunil Kumar Mudusu | 05m |
Compares three concrete tools and when they actually work: Great Expectations for pandas/SQL/Spark with human-written rules and HTML reports, practical for… | ||
| Revolutionizing Data Management | Venkatachala Nivas Chainuru | 12m |
Claims concrete savings and methods: predictive analytics raise utilization from 48% to 72% and cut procurement spend by up to 35%, intelligent tiering yields… | ||
| AI: Empowering Students, Not Replacing Universities | Vahid Mohammadi | 24m |
Survey of 3,800 students across 16 countries finds 86% of students use AI and reports show only 7% of ChatGPT references verifiable. Common detectors—Turnitin… | ||
| AI-Powered Customer Insights for Financial Services | Neha Thakur | 20m |
Claims a near‑real‑time pipeline that captures every call and chat and turns them into dashboards within minutes, implemented on AWS with event‑driven… | ||
| Advancing Financial Fraud Detection with Graph Dbs | Venkateswarlu Boggavarapu | 26m |
Graph databases plus graph neural networks and embeddings produce concrete gains: 35% improvement in detection when graph features augment models, GNNs deliver… | ||
| Leveraging AI in Financial Marketing Compliance | Vijayakumar Jayaseelan | 13m |
Slides claim concrete returns: EFL case study reports 34% more compliance activity, 96.7% accuracy, 392 violations prevented and $83.4M in avoided penalties… | ||
| How AI Implementation Transformed Financial Services in 2025 | Sampath Varanasi | 22m |
Claims measurable ROI: global investment of 142.3 billion in 2025, operational efficiency up 35% and data processing down 20%. Concrete deployments: JP Morgan… | ||
| AI & Digital Twins for Financial Risk & Asset Optimization | Nikhil Kassetty | 19m |
An end-to-end AI-powered digital twin framework is built for real-time financial risk and asset optimization, ingesting transaction and market streams, using… | ||
| Transforming Legacy Banking Systems through Machine Learning | Srinivas Allam | 21m |
Concrete claims, metrics and artifacts: 12.5 million accounts migrated with no data loss, a microservices cloud‑native platform plus API‑first and zero‑trust… | ||
| Building High-Performance Financial Data Pipelines | Anandan Dhanaraj | 10m |
Claims a full pipeline that achieves 300k transactions per minute normal and 450k in burst at 8–10 ms response, uses Oracle database with partitioning and… | ||
| Revolutionizing Financial Systems | Srinivasa Rao Akyana | 11m |
Consolidation into a metadata-driven financial data platform on Azure replaces stitched legacy apps and yields concrete results: report prep drops from 24… | ||
| ML & GenAI for Vibe Marketing: Future of Audience Connection | Udipta Basumatari | 28m |
Claims: generative tools (ChatGPT, DALL·E, Canva, Zapier, Salesforce/Adobe integrations) cut campaign build time and team size dramatically — a demo site is… | ||
| How GenAI is Revolutionizing Business Intelligence | Pavan Kumar Bollineni | 20m |
Claims include a projected ERP AI market CAGR of about 29.7% and ~65% enterprise adoption, backed by concrete case results: Walmart cuts stockouts ~30%, Tesla… | ||
| Securing Generative AI Workloads | Kalyan Madicharla | 09m |
A Five-Pillar framework names Infrastructure security, Data protection, Application security, Responsible AI, Regulatory compliance and gives concrete… | ||
| High-Definition Flexible Endoscopy | Santosh Suresh | 11m |
Claims concrete upgrades: tip diameter shrinks from 10–15 mm to about 5 mm, imaging moves to 1020p HD at 60fps with 170° FOV, on‑tip CMOS cameras replace fiber… | ||
| Multimodal AI: Transforming Patient-Centered Healthcare | Hari Suresh Babu Gummadi | 16m |
A multimodal AI framework integrates electronic health records, patient‑reported outcomes, genomic profiles and real‑time sensor feeds and generates dynamic… | ||
| Revolutionizing Healthcare with Enterprise Architecture | Suresh Kumar Gundala | 23m |
Claims concrete outcomes: 37% workflow improvement, 54% uptime boost, 89% data consistency and 42% cost reduction from enterprise architecture with FHIR… | ||
| Overcoming Key Challenges in Value-Based Care | Bandanawaz Mulla | 18m |
Claims a Value Based Care Data Enablement Engine built on AWS cloud solutions, aggregating siloed EHR, lab and claims data via HL7 and FHIR/ APIs to create a… | ||
| Transforming Healthcare Through Data-Driven Intelligence | Peeyush Khandelwal | 21m |
Concrete claims and measurable results: AI-assisted documentation reports 93% accuracy and saves about 4.2 hours per clinician per week while medication-error… | ||
| Transforming Population Health with Predictive BI | Satya Manesh Veerapaneni | 10m |
Predictive stack ingests EHRs, claims and SDOH into 8.5 million patient records and 360° profiles, running ETL pipelines, secure API gateways and HL7 FHI… | ||
| Cloud-Native AI: Architectural Patterns for Enterprise Success | Bhaskar Goyal | 20m |
Claims concrete gains and concrete tools: container images and Docker reduce inference time ~35% and cut failures 85% reduction in deployment failure, IaC with… | ||
| Harnessing Kubernetes for Scalable AI/ML Workloads | Praneel Madabushini | 27m |
Kubernetes drives concrete results: Tesla processes ~1.5 TB per car and >100,000 video clips/day, cutting deployment time from two weeks to under four hours by… | ||
| Transforming Supply Chain: Smart Logistics | Venkata Anil Kumar Nilisetty | 08m |
Concrete claims and metrics drive the decision: an event‑driven microservices stack built with Kafka, Kubernetes, and Springboard processes up to 25,000… | ||
| Leveraging Azure ML for Advanced Document Management | Harsha Vardhan Reddy Yeddula | 26m |
Claims a working pipeline that uses Azure AI Document Intelligence (Form Recognizer) plus Power Automate, Logic Apps and Azure Functions to auto-extract… | ||
| Fast Confidence Estimation: Regression models | Andrei Stroganov, Andrei Visochan | 21m |
Claims a p-value based confidence metric that judges counts or ratios without ground truth by mapping them to a fitted CDF (normal or spline) and returning… | ||
| Aggressive LLMs Optimization: Making Them Work on Tiny Devices | Max Navrotsky | 25m |
Uses GPT-2 117M / 500MB as a baseline, fine-tunes it to generate ISO software requirements, then applies 12→6 layers and 768→384 hidden architecture trimming… | ||
| Beyond BLEU & ROUGE — Evaluating LLMs and AI Systems | Alok Ranjan, Saurabh Suman | 46m |
Claims that BLEU/ROUGE break on real dialogue (one-word correct answer “34” gets BLEU 0.016) and proposes concrete replacements: an fact score pipeline that… | ||
| Hallucination by Design: How Embedding Models Misunderstand Language | Ritesh Modi | 29m |
Claims that off‑the‑shelf embeddings misread negation and numerics and that experimentation fixes it; shows 38 concrete failure cases and examples where MPNet… | ||
| Improving Large Language Model Performance | Gaurav Bansal | 14m |
Argues that LLMs require evaluation not binary tests and gives concrete steps: define metrics (factual accuracy, safety, coherence), build scalable evaluation… | ||
| Vector Embeddings and RAG Demystified | Veliswa Boya | 14m |
Shows how text is converted into vectors with Amazon Titan embeddings and a live Boto3 Bedrock client code sample that initializes a session, calls… | ||
| Architecting Sub-Second Analytics Pipelines | Sai Kaushik Ponnekanti | 27m |
Claims a sub‑second pipeline that hits under 10 ms latency using concrete components: Kafka ingestion (≈2M events/s, ~10 ms) feeding Flink submillisecond… | ||
| ML-based Log Analysis for Faster Debugging | Vijaybhasker Pagidoju | 10m |
Claims a reproducible ML pipeline — log collection, heavy pre‑processing, embeddings and LSTMs, anomaly detectors like isolation forest and autoencoders, plus… | ||
| Data-Driven NLP: From Statistics to GenAI | Shahzeb Akhtar | 21m |
Patent intelligence product Vantage for IT processes hundreds of thousands of patents using NLP and is presented as a working system. Model-size progression is… | ||
| Converging Networking and Security | Sharanya Prasad | 12m |
Claims concrete ROI and implemented tools: SDN, SASE, Zero Trust and AI security drive unified policies and automated response; projected markets rise from… | ||
| Zero Trust: Continuous Verification in the Modern Enterprise | Vaibhav Anil Vora | 26m |
Claims that every access is continuously verified and identity becomes the primary control point with concrete controls: multifactorauthentication, behavioral… | ||
| Beyond Perimeters: Implementing Zero Trust at Enterprise Scale | Naveen Kumar Birru | 19m |
Claims, metrics and concrete controls decide: adoption rises from 21% to 49% reporting progress, only 8% fully deployed, and mature programs save about $1.4M… | ||
| Harnessing Machine Learning to Combat E-commerce Fraud | Surendra Lakkaraju | 13m |
Claims concrete numbers and engineering choices: rule systems show ~28% false positives and $3.6B lost to false declines, while ML reports 96% detection… | ||
| Quantum Computing Encryption | Vishnuvardhana Reddy Veeraballi | 13m |
Claims that Shor's algorithm breaks RSA and ECC lead to concrete countermeasures: lattice, code and hash-based schemes plus NIST choices such as CRYSTALS‑Kyber… | ||
| AI-Powered Cybersecurity in the Age of Advanced Threats | Dinesh Rajasekharan | 11m |
Claims of concrete wins sit next to practical limits: MIT's AI2 detects 85% of attacks, a financial firm reports a 60% drop in false positives, a bank sees a… | ||
| Edge Intelligence Revolution | Sai Prasad Mukala | 29m |
Claims measurable wins: GPU acceleration delivers up to 15x performance and inference below 10 ms, paired with examples that actually run on site — a wind… | ||
| Using Low-Code/No-Code Platforms to Speed Up Development | Balaji Bodicherla | 13m |
Claims apps deploy up to 10x faster with Salesforce low‑code, citing 70% developer time reduction and 80% lower maintenance costs and a claimed 40%… | ||
| Secure Distributed Healthcare Systems with 99.97% Availability | Sachin Telalwar | 19m |
Claims concrete outcomes: system design targets 99.97% availability and data-layer validation rates near 99.997%, with encrypted distributed storage handling… | ||
| Transforming Digital Identity with AI | Venkata Krishna Ramesh Kumar Koppireddy | 14m |
Claims concrete numbers and implemented features not promises: a bank onboarding example completes in two minutes using facial and fingerprint verification… | ||
| Architectural Patterns for Oracle SOA Suite Integration | Vasipally Nagireddy | 13m |
Claims a concrete integration framework that wires Oracle SOA Suite to Spring Boot microservices using Oracle Service Bus, API gateway, CI/CD for SOA… | ||
| Reimagining Risk Management for Modern FinTech with AI | Deepu Komati | 22m |
A deployed real-time monitoring system claims 95% faster response time, uses advanced neural networks and anomaly-detection algorithms to compare each… | ||
| Convergent Quality Engineering | Jyotheeswara Reddy Gottam | 12m |
Claims a measurable payoff: 78% defect reduction, 63% faster detection and a move from 12‑week to 2‑week release cycles after unifying shift‑left… | ||
| AI-Powered CRM in Policing: Innovation and Surveillance | Chiranjeevi Dubaguntla | 08m |
Claims measurable effects: a reported 24% reduction in emergency response time and 37% increase in community satisfaction from pilot deployments in Boston… | ||
| AI-Powered Integration Testing | Siva Yachamaneni | 17m |
Claims concrete numbers and artifacts: self-healing testing frameworks that auto-update scripts and cut maintenance by up to 60%, 99.9% visual AI accuracy for… | ||
| Machine Learning in the DevSecOps Pipeline | Srikanth Potla | 12m |
Claims include 71% fewer security incidents, three times faster secure deployments and 89% reduction in container vulnerabilities with mean time to remediation… | ||
| Using AI to Fuel Digital Transformation & Growth | Lakshmi Narayana Gupta Koralla | 35m |
Salesforce Customer 360 runs 80+ billion daily predictions and the presentation claims concrete lifts: 20% higher conversion, 27% bigger AOV, 35% higher… | ||
| The AI-Human Partnership: Transform Professional Messaging Platforms | Gagan Shah | 09m |
Claims a platform rebuild that migrated 17 years of messages with zero downtime and produced a clear uptake in conversations and reconnections while changing… | ||
| Revolutionizing Industrial Maintenance | Karthikeyan Rajamani | 15m |
Claims a production pipeline that combines microphones and vibration sensors, signal processing into frequency/time features, edge computing to trim bandwidth… | ||
| AI-Powered Startup Toolkit for a Product Designer | Nikita Poloznikov | 19m |
Starts with Stable Diffusion XL via Dream Studio to prototype a limited‑edition payment card, moves to MidJourney 6.1 for higher fidelity and personalization… | ||
| Environmental Benefits of Efficient Power Moding in Vehicles | Ravinder Katla | 20m |
A software-first power‑model is deployed as 500-vehicle OTA test fleet with ECU firmware upgrades and 12 months of telemetry-driven algorithm tuning… | ||
| Achieving Innovation and Stability for Enterprises | Sasidhar Reddy Somula | 18m |
Claims include 15–30% cost reduction, 40–60% faster response and 25–35% better market adaptability, with goals like 99.9% availability; concrete patterns named… | ||
| Driving Digital Transformation in Insurance with SAP and ML | Satish Puram | 14m |
Concrete results and methods are front and center: 61% reduction in order processing time, 30% operational efficiency gain, 40% cost reduction and 3.5x ROI… | ||
| Reinventing Event Photography: Transform Community Moments | Dmytro Hlotenko | 37m |
Builds a serverless pipeline that streams Sony ARW files into S3 via Transfer Family, triggers S3 + EventBridge + SQS + Step Functions + Lambda, extracts raw… | ||
| How ML is Powering the Future of IoT | Gayathri Jegan Mohan | 20m |
Claims that 75% of data is processed at the edge and that edge inferencing cuts latency and bandwidth while improving privacy. Lists concrete problems: limited… | ||
| AI on the Fast Lane: Smart Traffic Systems to Unclog our Cities | Avraam Tolmidis | 20m |
Claims an AMORE adaptive multiobjective optimized routing system where vehicle and intersection agents bid for road segments and vehicles select routes by… | ||
| 5G Automation: Building the Foundation for Smart Cities | Anil Thimmapuram | 11m |
Claims concrete automation: Terraform plans and Helm charts deploy 5G clusters in AWS with CI/CD pipelines and automated latency/bandwidth tests; rollout… | ||
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