Conf42 MLOps 2025
2025-09-18
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| title | author | length |
|---|---|---|
| Low-Code Meets Public Code: Automating | Anoosha Cherukuri | 09m |
Claim: combining low-code platforms with public code cuts delivery time and raises transparency. A global bank shrinks a compliance project that used 20… | ||
| Harnessing Cascading Timeouts for System Resilience | Madhavi Bhairavabhatla | 14m |
Cascading timeouts require upstream calls to have longer timeouts than downstream ones, with a disciplined decrement such as a 500ms buffer per layer to avoid… | ||
| Designing Governed AI Pipelines for Healthcare Impact | Nikitha Edulakanti | 11m |
A governable MLOps pipeline standardizes data formats, enforces PHI masking and access controls, and runs bias checks and explainability before deployment so a… | ||
| Transform Residential Communities Through AI | Sheela Kakanur Shivayogi | 13m |
Claims a unified AI-powered single pane of glass that consolidates communications, bookings, service providers and real‑time property valuations for the 77… | ||
| Operationalizing Large Language Model Training Pipelines | Anjan Dash | 23m |
Operationalizes training for trillion-parameter LLMs using Megatron and DeepSpeed on A100/H100 clusters, claiming runs that span weeks and consume thousands of… | ||
| Self-Healing Infrastructure and AI-Driven Code Evolution | Mohan Singh | 08m |
Describes a working stack: observability-driven AI for anomaly detection, cross-service dependency graphs for contextual diagnosis, and automated patching… | ||
| Deploying AI Literature Agents at Scale | Nishanth Joseph Paulraj | 12m |
Deploys a production RAG stack with domain-adapted LLMs, synchronous embedding pipelines, hierarchical indexing and read replicas to serve 30+ million… | ||
| Production MLOps for Spam Detection: Real-Time Multi-Modal AI | Prabhakar Singh | 16m |
System operates at billion-user scale making millions of evaluations per second and targets ~80 millisecond end-to-end latency while keeping a 0.1% false… | ||
| Scaling Educational AI at the Edge | Lalitha Potharalanka | 56m |
Claims that low‑code platforms plus privacy‑first, edge‑ready MLOps make AI practical in schools: pilots report 70% of teachers configuring models within two… | ||
| Scaling Conversational AI in Production | Manoj Kumar Vunnava | 11m |
Contact center systems handle millions of conversations per day and this explains a concrete MLOps stack: a feature store for context, synthetic data… | ||
| From Paperwork to Pipeline: Autonomous Mortgage AI | Naganarendar Chitturi | 38m |
Claims concrete outcomes: a $8 million operations saving and a generative AI chatbot handling 60% of customer inquiries while end-to-end pipelines cut cycle… | ||
| Sky Computing: Charting a New Course in AI Cloud Infrastructure | Brian Irish | 16m |
Sky Computing proposes an intercloud broker model (SkyPilot) that already has over 1 million downloads and a v0.10 release, exposing a unified interface to 17… | ||
| Scaling MLOps with Self-Service Platforms | Rajeev Chevuri | 11m |
Claims measurable wins: 71% reduction in data preparation, 40% lower training cost, and 35–38% better deployment success from standardized pipelines and CI/CD… | ||
| AI-Driven Enterprise Intelligence | Terance Joseph Paulraj | 23m |
Describes building a cloud native, event‑streaming platform with Spark Streaming and managed cloud services, Kubernetes containers, feature stores and CI/CD… | ||
| MLOps in Healthcare: Scaling AI Model Deployment | Aishwarya Pai | 08m |
A four-phase MLOps framework—model assessment, infrastructure readiness, automated pipelines, monitoring—claims concrete gains: 73% more successful projects… | ||
| MLOps-Native Data Governance for Generative AI Pipelines | Bhanu Maryala | 09m |
Claim: embedding a Training Data Declarations (TDD) framework into CI/CD cuts compliance review time by 87% while keeping 100% regulatory adherence. TDD uses a… | ||
| Production-Ready MLOps for Telecommunications | Krishna Munnaluru | 09m |
Neural network pipeline predicts VoIP call quality at 88% prediction accuracy — a 23‑point gain over prior statistical models — and sustains 96% anomaly… | ||
| Real-World MLOps Lessons from Warehouse Deployment Disasters | Shubham Beldar | 10m |
Lab vision models fall from 95% to ~60% accuracy on transparent shrinkwrap, irregular shapes and dust; forecasting models miss volatile demand leading to… | ||
| How ML-Driven Slack Integration Cut MTTR by 88.5% | Sreelatha Pasuparthi | 10m |
ML-driven Slack integration routes prioritized alerts from a lightweight middleware (Kafka backbone, containerized inference) that captures 99.7% of exceptions… | ||
| Shipping Enterprise AI Agents with Amazon Bedrock AgentCore | Samuel Baruffi | 33m |
Amazon Bedrock AgentCore offers production primitives: agent core runtime runs serverless agent sessions (scale-from-zero, sessions up to 8 hours, multimodal… | ||
| Generative AI in Fintech | Pushpalika Chatterjee | 18m |
Generative AI converts raw transactions into contextual narratives and actionable offers, combining structured and unstructured data to generate personalized… | ||
| Scaling AI in Finance: Strategies Behind $78.6 Billion Market | Parul Purwar | 12m |
Claims a $78.6 billion AI in finance market by 2030 and reports 85% of financial firms deploying AI now, with millions of daily transactions and thousands of… | ||
| MLOps at Scale: Financial Risk and Fraud Detection | Dileep Kumar Kanimetta | 13m |
Practical blueprint for financial MLOps: Azure-based deployments using Terraform for infrastructure as code, CI/CD pipelines that cut model rollout from days… | ||
| Operationalizing AI in Finance: Real-Time Analytics | Mallikarjun Ramasani | 16m |
Claims that 83% of models never reach production and that fixing post‑development plumbing unlocks value drive the session, with concrete stacks: Airflow Spark… | ||
| MLOps in FinTech: Architecting Responsible AI | Narasimha Rao Vanaparthi | 13m |
Claims that AI in fintech hits $61 billion by 2029 and that 60% of model teams exhibit bias frame a push for reproducible pipelines, secure isolated training… | ||
| Scaling AI Systems for High-Volume Transaction Processing | Naresh Karri | 13m |
Claims production systems hit sub 10 millisecond latency while handling peaks of 100,000 transactions per second and maintaining 99.99% uptime. Architecture… | ||
| Transforming Manufacturing: AI-Driven Predictive Maintenance | Muruga Angamuthu | 14m |
Manufacturing moves from reactive fixes to AI systems that cut downtime 50% reduction in downtime and claim 385% ROI by combining IoT sensors (1.5–2.3 TB/day)… | ||
| AI-Driven Insurance Operations | Sameer Joshi | 09m |
Claims concrete outcomes: 65% efficiency gains, policy issuance drops from days to ~15% of prior time (quoted as a 99% reduction) and underwriting accuracy… | ||
| MLOps at Scale: AI Systems for HR Transformation | Ramprasad Reddy Mittana | 14m |
Production MLOps blueprint for HR systems that handles 10 million monthly interactions and serves 50,000 employees using containerized deployments on… | ||
| Human-in-the-Loop MLOps: Boost Model Performance by 40% | Tejasvi Nuthalapati | 13m |
Real production patterns: a FinTech fraud pipeline embeds tiered autonomy with service meshes and Kubernetes operators to auto-approve low-risk, route… | ||
| MLOps: From AI Research to Production-Ready Systems | Bharath Reddy Baddam | 13m |
Only about 20% of ML prototypes reach production; mature MLOps pipelines cut deployment time by ~80% and hold ~95% live accuracy. Recommendations include using… | ||
| From Model to Production: Building Scalable MLOps Pipelines | Jimmy Katiyar | 10m |
Enterprise MLOps roadmap that moves a team from ad hoc to Level 3 automation in 18 months with concrete results: reduced deployment times 45 days to 4 hours… | ||
| Building Scalable MLOps Pipelines That Actually Work | Srikanth Vissarapu | 25m |
Claims that 87% of ML efforts never reach production and that mature MLOps teams deploy models 5x faster and realize 60% higher production performance… | ||
| Scaling AI: Fault-Tolerant GenAI Solutions for Millions | Dennis Nerush | 32m |
Elementor builds the AI site planner that serves ~1 million daily AI requests and exchanges ~10 billion tokens monthly, using OpenAI plus a multiorg proxy to… | ||
| MLOps at Scale | Naveen Edapurath Vijayan | 13m |
Claims that operationalizing models is the hard part and that MLOps prevents mission‑critical failures; cites a real credit‑risk model that misclassifies after… | ||
| Intelligent Threat Detection in the Post-Quantum Era | Arfi Siddik Mollashaik | 12m |
Claims that signature systems miss 38% detection failure and that the assembled ML stack hits a 93% detection rate using random forests, gradient boosting… | ||
| Securing AI Pipelines from Development to Production | Sudheer Obbu | 19m |
Claims a 73% reduction in breaches after adopting zero trust across ML pipelines, quantifies three top threats (data poisoning 95, interface interference 90… | ||
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