Category: Machine Learning
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From Notebook to Production: Building End-to-End ML Pipelines with Kubeflow, KServe, and Fractional GPU Sharing
Transitioning a machine learning model from an experimental Jupyter Notebook to a highly available, auto-scaling production endpoint is rarely a linear path. In an enterprise environment, this migration introduces severe operational friction—not just in terms of rewriting code, but in handling infrastructure efficiency. With modern workloads demanding massive compute resources, assigning an entire enterprise-grade GPU…
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Specialized Judges: Scaling RAG Evaluation with Prometheus-2 and PydanticAI
Our production benchmarks utilize the Feedback Collection and Preference Collection datasets to establish the performance delta between generalist and specialized evaluators. We observed that Prometheus-2 (8x7B) achieves a Pearson correlation of $0.898$ with human-annotated ground truth, which is on par with GPT-4 ($0.882$) and significantly higher than previous iterations of small generalist models. By enforcing…
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Modernizing Data Warehouses for AI: A 4-Step Roadmap
It’s the same conversation in every boardroom and Slack channel: “How are we using LLMs? Where are our AI agents? When do we get our Copilot?” But for the teams in the trenches, the hype is hitting a wall of legacy infrastructure. The truth is that Modernizing Data Warehouses for AI is the invisible hurdle…
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Designing Production-Grade GenAI Automation
A dbt Ops Agent Case Study A small, well-instrumented workflow can turn dbt failures into reviewable Git changes by combining deterministic parsing, constrained LLM tooling, and VCS-native delivery — while preserving governance through traces, guardrails, and CI. This is a blueprint to build a first Production-Grade GenAI Agent. You can find the complete implementation and…
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Google Cloud Data Engineer Exam Preparation
This is a little text with all the stuff that helped me prepare for the Google Cloud Data Engineer Exam. There are a lot of courses and resources, that help you in preparing for this. The following links helped me in preparation for my Google Data Engineer Exam. On Coursera there is are several courses…
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Plumber: Getting R ready for production environments?
R Project and Production Running R Project in production is a controversially discussed topic, as is everything concerning R vs Python. Lately there have been some additions to the R Project, that made me look into this again. Researching R and its usage in production environments I came across several packages / project, that can…
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Apache Spark 2.0
Apache Spark has release version 2.0, which is a major step forward in usability for Spark users and mostly for people, who refrained from using it, due to the costs of learning a new programming language or tool. This is in the past now, as Spark 2.0 supports improved SQL functionalities with SQL2003 support. It…
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Python vs. R for Data Science
In Data Science there are two languages that compete for users. On one side there is R, on the other Python. Both have a huge userbase, but there is some discussion, which is better to use in a Data Science context. Lets explore both a bit: R R is a language and programming environment especially…
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Apache Spark: The Next Big (Data) Thing?
Since Apache Spark became a Top Level Project at Apache almost a year ago, it has seen some wide coverage and adoption in the industry. Due to its promise of being faster than Hadoop MapReduce, about 100x in memory and 10x on disk, it seems like a real alternative to doing pure MapReduce. Written in…
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SQL on Hadoop: Facebook’s Presto
Earlier this month Facebook open sourced its own product for using SQL on Hadoop. It is called Presto and is something like Facebook’s answer to Cloudera’s Impala or Hortonwork’s Stinger already presented in an earlier post called SQL and Hadoop on this site. Presto is unlike Hive and more like Impala, since it doesn’t rely…