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Design effective & reliable machine learning systems!

KDnuggets

Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale.

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A Tour Around Buck2, Meta's New Build System

Tweag

Buck2 is a from-scratch rewrite of Buck , a polyglot, monorepo build system that was developed and used at Meta (Facebook), and shares a few similarities with Bazel. As you may know, the Scalable Builds Group at Tweag has a strong interest in such scalable build systems. Bazel recording steps: 1.

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How Systems Thinking Can Be Applied To Agile Transformations

Knowledge Hut

Applying systems thinking views a system as a set of interconnected and interdependent components defined by its limits and more than the sum of their parts (subsystems). When one component of a system is altered, the effects frequently spread across the entire system. are the main objectives of systems thinking.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood. Register today to save your seat! December 6th, 2023 at 11:00am PST, 2:00pm EST, 7:pm GMT

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Designing a "low-effort" ELT system, using stitch and dbt

Start Data Engineering

Intro A very common use case in data engineering is to build a ETL system for a data warehouse, to have data loaded in from multiple separate databases to enable data analysts/scientists to be able to run queries on this data, since the source databases are used by your applications and we do not want these analytic queries to affect our application (..)

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Fail Safe vs Fail Secure: Top Differences in Locking Systems

Knowledge Hut

I have comprehensively analyzed the area of physical security, particularly the ongoing discussion surrounding fail safe vs fail-safe secure electric strike locking systems. These two distinct approaches offer unique strategies for securing buildings and valuables. It has a robust design and reliable performance.

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