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A few years ago, getting a model into production meant a data scientist, a DevOps engineer, and a narrow set of tools: train it, test it, ship it, watch the dashboards. Large langu...
From notebooks to monitored, reproducible pipelines — the essentialsContinue reading on Medium »
The Agentic Data Operations Platform (ADOP) is a reference architecture on Amazon Bedrock that uses specialized AI agents to automate the full Bronze-to-Silver-to-Gold data pipelin...
TL;DR: MLOps tools help teams track experiments, automate pipelines, deploy models, and monitor performance. Tools such as MLflow, Kubeflow, BentoML, and Evidently AI support diffe...
MLOps Serisi — Yazı 1/4Continue reading on Medium В»
Where the Data Actually Lives Every enterprise I have worked with hits the same wall. Mountains of data. Warehouses, ticketing systems, PDFs, old email archives. Most of that data...
Learn how data engineers use Snowflake's AI coding agent CoCo to build reproducible pipelines — covering setup, Skills, Plugins, and best practices.
For the past two decades, most enterprise data engineering systems have been built on one default assumption: People understand the system. The system executes the pipeline. Engine...
What Skills Do You Need for MLOps and AIOps Careers in 2026?Continue reading on Medium В»
🚀 𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗟𝗢𝗽𝘀 & 𝗔𝗜𝗢𝗽𝘀 𝗮𝗻𝗱 𝗕𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗥𝗲𝗮𝗱𝘆 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀! 🎯 #Visualpath’s #MLOps…Continue reading on Medium В»
Data pipelines are the backbone of the modern enterprise, yet a barrier to entry exists for orchestrating them, making this critical capability unavailable to many data professiona...
The Moment It Gets Real At some point in the last year, every data engineer had the same experience. You opened a copilot tool, typed a rough description of what you needed, and wa...
a conversation with Hugo Lu
350+ CTOs at Snowflake Summit shared lessons on building AI-native engineering orgs — from deploying AI in production to redesigning engineering teams.
Traditional CI/CD pipelines are optimized around a familiar assumption: source code changes, automated tests validate the change, a build artifact is produced, and the application...
Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) often struggle to turn concepts into working code. This post walks through two reference implementations on...
Exploring EDA, data preparation, machine learning, model evaluation, and scalable data workflows through my Data Science Bootcamp…Continue reading on Medium В»
Are you tired of being stuck in the dark ages of model management? You know what it's like: the endless cycle of paper pushing and manual tracking. You have to get approvals from m...
Use Grok Build to create a production-ready data science workflow with EDA, scikit-learn, model training, FastAPI, API testing, and cloud deployment.
The setupAt cellcentric, a joint venture of Daimler Truck and Volvo Group, we develop...
A few months back, I wrote about Loop Engineering: The Layer After Prompt, Context, and Harness Engineering, arguing that once your prompts are tuned, your context is assembled, an...
There is an apparent contradiction at the heart of using AI agents for data work, and resolving it properly is worth an entire article, because the teams that resolve it are quietl...
If you spend your days tuning queries, managing pipelines, or keeping a production database alive, you already carry most of what an AI engineering role needs. What is missing... T...
How to apply the latest context engineering guidelines to your day-to-day data science work The post Context Engineering Is Changing. Here’s What It Means for Data Scientist...
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