MLOps

Online Safety Bill: How Global Platforms Use MLOps to Keep People Safe

Wed Jun 28, 2023, by Phil Winder, in MLOps

When: Wed Jun 28, 2023 at 10:20 +0200 Where: GOTO Amsterdam 2023 The UK government’s communications regulator, Ofcom, commissioned Winder.AI to produce a report to improve their understanding of the end-to-end AI governance processes that support the creation and deployment of automated content classifiers used in moderating online content. Together we interviewed social media platforms and moderation technology vendors to ask them about the tools, technologies and processes that are often referred to as machine learning operations (MLOps).

How Social Media Platforms use MLOps and AI Governance to Help to Moderate Content

Mon Jan 30, 2023, by Winder.AI, in MLOps, Case Study

The UK’s communications regulator, Ofcom, commissioned Winder.AI to produce a report to improve their understanding of the end-to-end processes that support the creation and deployment of automated content classifiers used in moderating online content.

Do you like DAGs? Implementing a Graph Executor for Bacalhau

Tue Jan 24, 2023, by Enrico Rotundo, in MLOps, Software Engineering, Talk, Case Study

Winder.AI helped Protocol Labs, a technology company in the crypto space, to help develop Bacalhau, a novel decentralised computational platform that focuses on the AI lifecycle. This case study describes some of our work to develop this project but for more information view the Bacalhau website.

How Social Media Companies Use MLOps to Protect Users

Tue Mar 21, 2023, by Phil Winder, in MLOps, Case Study

When: Tue Mar 21, 2023 at 16:30 UTC Where: Linkedin Live Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations. The UK government’s communications regulator, Ofcom, commissioned Winder.AI to produce a report to improve their understanding of the end-to-end AI governance processes that support the creation and deployment of automated content classifiers used in moderating online content. Together we interviewed social media platforms and moderation technology vendors to ask them about the tools, technologies and processes that are often referred to as machine learning operations (MLOps).

Pachyderm ❤️ Spark ❤️ MLFlow - Scalable Machine Learning Provenance and Tracking

Tue Aug 23, 2022, by Enrico Rotundo, in Case Study, Mlops

This article shows how you can employ three frameworks to orchestrate a machine learning pipeline composed of an Extract, Transform, and Load step (ETL), and an ML training stage with comprehensive tracking of parameters, results and artifacts such as trained models. Furthermore, it shows how Pachyderm’s lineage integrates with an MLflow’s tracking server to provide artifact provenance.

Buildpacks - The Ultimate Machine Learning Container

Thu Jul 14, 2022, by Enrico Rotundo, Phil Winder, in Case Study, Mlops, Cloud-Native, Talk

Winder.AI worked with Grid.AI (now Lightning.ai) to investigate how Buildpacks can minimize the number of base containers required to run a modern platform. A summary of this work includes: Researching Buildpack best practices and adapting to modern machine learning workloads Reduce user burden and reduce maintenance costs by developing Buildpacks ready for production use Reporting and training on how Buildpacks can be leveraged in the future The video below presents this work.

A Comparison of Computational Frameworks: Spark, Dask, Snowflake, more

Fri Jun 24, 2022, by Enrico Rotundo, in MLOps, Case Study

Winder.AI worked with Protocol.AI to evaluate general-purpose computation frameworks. A summary of this work includes:

Comprehensive presentation evaluating the workflows and performance of each tool
A GitHub repository with benchmarks and sample applications
Documentation and summary video for Bacalhau documentation website

Save 80% of Your Machine Learning Training Bill on Kubernetes

Mon Jun 6, 2022, by Phil Winder, in Cloud Native, MLOps, Case Study

Winder.AI worked with Grid.AI to stress test managed Kubernetes services with the aim of reducing training time and cost. A summary of this work includes: Stress testing the scaling performance of the big three managed Kubernetes services Reducing the cost of training a 1000-node model by 80% The finding that some cloud vendors are better (cheaper) than others The Problem: How to Minimize the Time and Cost of Training Machine Learning Models Artificial intelligence (AI) workloads are resource hogs.

MLOps Presentation: When do You Need an MLOps Platform Team?

Wed May 11, 2022, by Phil Winder, in MLOps, Talk

Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations. Abstract In this talk he presents industry observations of MLOps team size and structure for a range of business sizes and domains. Learn more about how others structure their MLOps teams. Discover which problems you need to solve first. About This Series Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.

MLOps Presentation: Databricks vs. Pachyderm

Wed Apr 6, 2022, by Phil Winder, in MLOps, Talk

Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations. Abstract In this talk he presents a white paper that discusses the differences between two leaders in the data engineering space – Databricks and Pachyderm. Learn how these two products differ, when to use each, and the pros and cons. At the end of the talk Phil distils this information and presets best practices.