We build model agnostic systems with routing, fallback and human oversight designed in. 18 countries, 35 languages. calendly.com/corpshoresoluti… #AI #AgenticAI #Resilience #MLOps

Have you experimented with self-hosted or alternative inference backends? I'd love to hear what setups the community is running. #AIEngineering #MachineLearning #MLOps #vLLM #LiteLLM #ClaudeCode #WSL #RunPod #OpenSourceAI #LearnInPublic #SoftwareEngineering

LLMOps: Post-Training LLM,s. SFT, DPO, ORPO, PPO, GRPO Parte 1 youtu.be/18eyk6_ZLZg In this video I will do a tour of the five techniques that take a pretrained language model from text completer to aligned assistant, SFT → DPO → ORPO → PPO → GRPO Part 2 Demo youtu.be/JnGS4_h0FgY In this video Part 1: In previous sessions built RL algorithms from scratch: MDPs and Bellman (1), DQN family (2), PPO/A2C/TRPO (3), continuous-control actor-critic — DDPG, TD3, SAC (4). Session 7 pivoted to libraries and an applied trading demo. This session pivots again — from agents acting in environments to agents acting in language. We already covert PPO, but we have never seen what it looks like when "the environment" is a tokeniser, "the reward" is a learned classifier, and "the policy" is a 1.5-billion-parameter transformer. #mlops #machinelearning #datascience #artificialintelligence

🏆 Proud to share that our team ranked 33rd out of 2,674 teams in Kaggle's Deep Past Challenge – Translate Akkadian to English, earning a Silver Medal. This challenge focused on reviving 4,000-year-old Assyrian texts using AI and machine translation. 🔹 Fine-tuned ByT5-base with a two-stage training strategy 🔹 Trained over 21 epochs through iterative experimentation 🔹 Used only public data — no synthetic data, no LLM-generated pairs, no ensembles 🔹 Achieved a private score of 38.1 and secured a Top 1.3% finish What I enjoyed most wasn't the final ranking, but the process: testing ideas, learning from failures, and proving that disciplined experimentation and data quality can compete with far larger resources. Research, NLP, LLMs, and low-resource language problems continue to fascinate me. Excited to apply these lessons to future AI systems and real-world applications. #Kaggle #MachineLearning #DeepLearning #NLP #LLM #AI #DataScience #Research #Python #ByT5 #TranslationAI #MLOps

2001: Learn SQL → get a job 2005: SQL Excel → get a job 2010: SQL Python Stats → get a job 2015: SQL Python Stats ML → get a job 2020: SQL Python Stats ML A/B Testing Dashboards → get a job 2026: SQL Python Stats ML A/B Testing Dashboards * Data Engineering * System Design * LLMs * AI Agents * MLOps * Cloud (AWS/GCP/Azure) * Data Pipelines * Streaming (Kafka/Spark) * Experimentation Platforms * Business Understanding * Communication Skills * Domain Expertise * “Ownership mindset” * “Startup hustle” * 5 YOE → Entry-level role Somewhere along the way... “entry-level” stopped meaning entry. If you’re feeling overwhelmed, you’re not alone. The bar didn’t just rise… it multiplied.

🚀 Hiring: SDE III – MLOps | Remote (India) Looking to work on large-scale Machine Learning infrastructure that powers real-world AI systems at global scale? We're partnering with a fast-growing technology company that's building mission-critical AI and identity platforms used by millions of users worldwide. As an SDE III – MLOps Engineer, you'll help architect and scale the infrastructure behind the entire ML lifecycle—from model training and deployment to serving, monitoring, and optimization. 🔹 Build and scale ML serving infrastructure 🔹 Design enterprise-grade ML lifecycle management systems 🔹 Develop CI/CD frameworks for production ML 🔹 Architect distributed and event-driven systems on AWS 🔹 Optimize model inference using ONNX, TensorRT, AWS Neuron, and modern acceleration techniques 🔹 Work closely with ML Engineers, Platform Engineers, and Cloud teams What we're looking for: ✔ 5 years of software engineering experience ✔ Strong Python expertise ✔ MLOps experience (MLFlow, Weights & Biases, or similar) ✔ AWS cloud and serverless architecture experience ✔ Docker, containerization, and orchestration experience ✔ Experience with model deployment, serving, and monitoring ✔ Exposure to PyTorch and production ML workflows 📍 Location: Remote (India) If you're passionate about building scalable ML platforms and enabling AI teams to move faster, I'd love to connect. 📩 Feel free to reach out or share your profile at nachiketh.n@grizmolabs.com

Ran my first vLLM benchmark loop on RTX 5070 Ti with Qwen2.5-0.5B. 16 requests per run, 128/64 configured token lengths, concurrency 1/2/4. Output throughput: - c1: 438 tok/s - c2: 783 tok/s - c4: 1526 tok/s E2E p95 stayed under 171 ms in this tiny test. #vLLM #LLMInference #MLOps #AIInfrastructure

🚀 Looking for AI, Machine Learning, or Data Science Expertise? I'm passionate about building intelligent systems that transform data into actionable insights and automate complex workflows. 💼 My core expertise includes: 🧠 Artificial Intelligence (AI) 🤖 Machine Learning (ML) ⚡ Deep Learning 🔗 Large Language Models (LLMs) 📚 Retrieval-Augmented Generation (RAG) 💬 Natural Language Processing (NLP) 📊 Data Analytics & Business Intelligence ⚙️ AI Agents & Workflow Automation 🔥 Services I provide: ✅ Custom AI Solutions ✅ LLM & RAG Applications ✅ AI Chatbots and Assistants ✅ Predictive Machine Learning Models ✅ NLP and Text Analytics ✅ Data Analysis and Visualization ✅ AI Agents and Automation Workflows ✅ End-to-End AI Product Development I believe the most exciting opportunities are created when data, intelligence, and automation come together. Whether you're a startup, business owner, researcher, or tech enthusiast, I'm always interested in collaborating on meaningful projects and solving challenging problems. 📩 Available for: • Freelance Projects • Remote Opportunities • Consulting Engagements • Long-Term Collaborations Let's build AI solutions that create real impact. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #LLM #LLMEngineer #RAG #NLP #DataScience #DataAnalytics #AIAgents #Automation #MLOps #Python #AIEngineer #DataScientist #BusinessIntelligence #BigData #Analytics #TechInnovation #Startup #DigitalTransformation #FutureOfAI #RemoteWork #Freelancer #OpenToWork #BuildInPublic #AICommunity #Innovation

Evaluation & MLOps Deployment Failures: 12 terms.

Microsoft shipped 7 in-house models. The models aren't the story — the "hill-climbing" loop is. A model is a snapshot; it decays the moment a rival ships. A compounding data→eval→train→feedback loop is a slope. The model isn't the moat. The loop that makes the next one is. 1. This is also a hyperscaler vertically integrating away from its biggest model partner. Own the loop, stop renting capability. 2. Builder takeaway: stop optimizing for this quarter's model. Optimize the speed of your data → eval → post-train → product-feedback cycle. That's what compounds. #ai #mlops #microsoft #strategy

🚀 DevOps MasterClass - MLOps, Platform / SRE & AIOps | Join our 2‑Day Free Live MasterClass led by industry experts 📅 Event Dates: 27th & 28th June 2026 @10 AM IST 🎓 Duration: 2 Days of intensive learning 📍 Seats: Registration Must! #Trending #Training

Looks solid, maybe put MLOps before cloud

Most people quit because they don't know what to learn next. Here's the complete Data Science Roadmap 👇 📍 Python Basics 📍 Statistics & Probability 📍 SQL & Databases 📍 Pandas & NumPy 📍 Data Visualization 📍 Machine Learning 📍 Deep Learning 📍 MLOps & Deployment Follow this path and stay consistent. 💯 #DataScientist #Roadmap #Programming

I want to run ML workloads on k8s. Distribute the compute across nodes, while managing performance. Run RAG pipelines, MLOps, etc. etc.

🆕【DX Next検定攻略 Vol.5】AI・データサイエンスキーワード15選を解説！ ✅因果推論・特徴量エンジニアリング・クラスタリング ✅ MLOps・AutoML・AIエージェント・マルチモーダルAI ✅ファインチューニング・AIOps・オントロジー etc. 📚実話エピソード3本付き！ 📚Amazonレコメンドシステムの誕生と因果推論 📚JR東日本新幹線の予知保全AI 📚Google Med-PaLM 2とファインチューニング 🎬youtu.be/8hzJtOkr5yE #DXNext検定 #AIデータサイエンス #MLOps #AIエージェント #機械学習 #AI #生成AI #ChatGPT #Claude #Gemini #DX #DX検定 #DXビジネス検定 #G検定 #E検定 #資格試験

Even if u ignore water crises, grid strains, & env degradation— 1. Data residency ≠ Data sovereignty 2. -ve economic ripples, negligible jobs created 3. Data centres r only a part, critical AI infra incl. Compute, Networking Fabric, Software and MLOps: where India.. LOL

🛠 note 新着記事｜Tak@ | IT Explore 第29回：強固なMLOpsを築くAmazon SageMaker Pipelines構築術と実務における設計制約 🔗 記事を読む note.com/itexplore/n/ne50ec3…

Tested the new enterprise edition for companies and businesses to use as a data engineer and this is the results *the business version is a extra lean edition of repryntt that makes it even cheaper to run ai agents for business purposes. 64 work cycles · 61 delivered · 1 partial · 2 failed. Starting from "build small data tools," it self-prompted its own escalating task stream and built a complete production MLOps stack: 55 working Python scripts, 38 PNG charts (verified real images), 32 data files, a Dockerfile for a total of 147 files, 5.3 MB. Grok 4.3 API cost $0.84 on a 30 min continuous agent run ML pipeline → train/test → multi-model comparison → 5-fold CV → hyperparameter tuning → ROC/PR curves → feature importance → model serialization → SHAP explainability → FastAPI inference service (/explain, /batch_predict, /metrics) → multi-stage Dockerfile → pytest suite → Grafana dashboard → drift monitoring → automated retraining → versioned model registry → canary releases → automated rollback → load testing.