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11 November 2022 23h47

If you're a company that uses Keras and you face this use case (large scale adapt() of Keras preprocessing layers), would you consider working with us to implement it? We're a small team and we don't have the resources for this at this time…

Beam-style computation support designed but not yet implemented

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11 November 2022 23h25

Correct, the underlying API / infra is designed to potentially allow Beam-style computation. We have not implemented it and it's currently deprioritized (the current adapt() is serial and single-threaded). But we could if there's demand in the future — the design is there.

TensorFlow Graph Performance Eliminates Python Slowness Issues

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11 November 2022 23h20

The fact that we're able to do everything as part of the TF graph is really nice — Python slowness is never an issue. There's no need for us to rewrite anything in, like, Cython or Rust.

Keras Preprocessing Layers: High-Performance In-Graph Implementation

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11 November 2022 23h16

All the work is done in Keras preprocessing layers, which are implemented in TF ops (everything is 100% in-graph!) so it's highly performant. During training (presumably on GPU/TPU) you'd use async preprocessing in TF data to avoid CPU preprocessing being a bottleneck.

→ View original post on X — @craigbrownphd

Learn the Architecture of a Data Pipeline

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@craigbrownphd

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11 November 2022 17h49

Learn the Architecture of a Data Pipeline: This article was published as a part of the Data Science Blogathon. Introduction Controlling the flow of information from a source to a destination system, such as a data warehouse, is an integral part of any… https://
analyticsvidhya.com/blog/2022/10/l
earn-the-architecture-of-a-data-pipeline/?utm_source=dlvr.it&utm_medium=twitter
…

→ View original post on X — @abacusai

Building Machine Learning Systems: Complete Components Overview

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@abacusai

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11 November 2022 17h00

Building machine learning systems is hard. Some of the components you need: 1. Data sources
2. Data pipelines
3. Feature stores
4. Model training
5. Model evaluation
6. Model deployment
7. Model monitoring
8. Predictions API At @abacusai we help you with this!

→ View original post on X — @langchain

LangChain 0.0.12 Release with AI21Labs and Manifest Integrations

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@langchain

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11 November 2022 16h57

LangChain version 0.0.12 Two super exciting integrations! @AI21Labs integration (from a friend of @YuvalinTheDeep
) Integration with @HazyResearch
's manifest library (with help from @laurel_orr1
)

→ View original post on X — @avikumart_

ML in Production: Beyond Notebooks to Deployment Challenges

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@avikumart_

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11 November 2022 15h30

Machine learning is hard, there's no doubt about that But Machine learning in production is much harder than ML in notebooks. Developing the ML pipeline to deployment and then maintaining consistent quality prediction building a retraining pipeline becomes more complicated…