Jul 20, 2021

Weekly roundup of MLOps and DataOps - Issue #5

By Subbu Banerjee • Issue #5 • View online

Here are the top links as always from MLOPS world that you can´t miss

Detailed analysis of MLPlatform tools in 2021

How many machine learning platforms run on Kubernetes? Which machine learning platforms can run in air-gapped environments? How common are feature stores in current machine learning platforms? Should you build or buy your MLPlatform, Ian hellstrom takes a detailed look

Feature Stores and why you need to know them

Feature stores have emerged as a pivotal component in the modern machine learning stack. They solve some of the toughest challenges in data for machine learning, namely feature computation, storage, validation, serving, and reuse. Ultimately, feature stores act as the bridge between models in production and an organization’s data. In this talk In this talk Willem Plenaar describes the key problems that feature stores solve, deployment patterns for feature stores that we see in the wild, and finally how feature stores are evolving with the rise of modern data platforms.

Bridging Models and Data feat. Willem Pienaar | Stanford MLSys Seminar Episode 32

Continuing with Feature stores

if you want to dig further on features stores and how organizations like airbnb, comcast, netflix, google etc have implemented their feature store here is a dump of all the presentation curated and maintained at featurestore.org

Automated Data Cleaning

A recently opensourced library to get clean data … well we are almost reaching the holy grail

HoloClean is a statistical inference engine to impute, clean, and enrich data. As a weakly supervised machine learning system, HoloClean leverages available quality rules, value correlations, reference data, and multiple other signals to build a probabilistic model that accurately captures the data generation process, and uses the model in a variety of data curation tasks. HoloClean allows data practitioners and scientists to save the enormous time they spend in building piecemeal cleaning solutions, and instead, effectively communicate their domain knowledge in a declarative way to enable accurate analytics, predictions, and insights form noisy, incomplete, and erroneous data.

GitHub - HoloClean/holoclean: A Machine Learning System for Data Enrichment.

A Machine Learning System for Data Enrichment. Contribute to HoloClean/holoclean development by creating an account on GitHub.

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Modern MLOPS platform

Towards MLOps: Technical capabilities of a Machine Learning platform | by Theofilos Papapanagiotou | Prosus AI Tech Blog | Medium

Large organisations rely increasingly on continuous ML pipelines to keep their ever-increasing number of machine-learned models up-to-date. Reliability and scalability of these ML pipelines are…

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A book on operationalizing MLOPS

Giving the growing importance of MLOPS and the increased need to educate, Noah Gift and Alfredo Deza have come up with their latest publication.

MLOPS Antipatterns

Finally we end this week with an interesting paper on MLOPS antipatterns based on real world experience in deploying ML models in production (in financial domain) Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and methodologies. Here is a catalog and docu-ment numerous antipatterns in financial ML operations (MLOps).

[2107.00079] Using AntiPatterns to avoid MLOps Mistakes

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and methodologies. Here we catalog and document numerous antipatterns in financial ML operations (MLOps). Some antipatterns are due to technical errors, while others are due to not having sufficient knowledge of the surrounding context in which ML results are used. By providing a common vocabulary to discuss these situations, our intent is that antipatterns will support better documentation of issues, rapid communication between stakeholders, and faster resolution of problems. In addition to cataloging antipatterns, we describe solutions, best practices, and future directions toward MLOps maturity.

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By Subbu Banerjee

Weekly roundup of MLOps and DataOps

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