MLOps

Buildpacks - The Ultimate Machine Learning Container

Published

Jul 14, 2022

Author

Enrico Rotundo

Associate Data Scientist

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

Published

Jun 24, 2022

Author

Enrico Rotundo

Associate Data Scientist

Winder.AI worked with Protocol.AI to evaluate general-purpose computation frameworks as part of our MLOps consulting services. 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

Published

Jun 6, 2022

Author

Dr. Phil Winder

CEO

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. They are notorious for requiring expensive GPUs and large numbers of machines to train complex models on large datasets in a reasonable amount of time.

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

Published

May 11, 2022

Author

Dr. Phil Winder

CEO

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.AI, Author of O’Reilly’s Reinforcement Learning and one of the founders of the MLOps Community, covering a range of topics about the use of machine learning operations (MLOps), reinforcement learning (RL), and machine learning (ML) in industry today.

MLOps Presentation: Databricks vs. Pachyderm

Published

Apr 6, 2022

Author

Dr. Phil Winder

CEO

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. There’s also some discussion of future trends and some ideas for aspiring open-source engineers.

Machine Learning Presentation: Packaging Your Models

Published

Mar 16, 2022

Author

Dr. Phil Winder

CEO

Dr. Phil Winder shares experiences of Winder.AI’s machine learning consulting experience at a variety of large and small organizations.

Abstract

In this talk he focuses on packaging ML models for production serving. Learn about how the cloud vendors compare, what orchestration abstractions prefer, and how packaging tools seek to find the right abstractions.

At the end of the talk Phil distils this information and presets best practices. There’s also some discussion of future trends and some ideas for aspiring open-source engineers.

GitOps for Machine Learning Projects

Published

Mar 11, 2022

Author

Dr. Phil Winder

CEO

Not so long ago, developers used clunky consoles to provision infrastructure and applications. It wasn’t long before someone realized it was better to automate such a process via scripts and APIs. But it wasn’t until Hashicorp showed that APIs were not enough. Their insight was to declare a canonical representation of the infrastructure. You can then reconcile this declaration against the live view of the infrastructure.

In 2015-16 we helped WeaveWorks develop their cloud monitoring platform. We targeted Kubernetes but lamented the hackiness of our script-based deployment pipeline. The common pattern at that time was to run a script that ran kubectl apply over and over again.

Databricks vs Pachyderm - A Data Engineering Comparison

Published

Feb 7, 2022

Author

Enrico Rotundo

Associate Data Scientist

Winder.AI has conducted a study comparing the differences between Pachyderm and Databricks. Both vendors are prominent in the data and machine learning (ML) industries. But they offer different products targeting different use cases. Modern, production-ready requirements present major challenges where data is evolving, unstructured, and big. This white paper investigates the strengths and weaknesses in their respective propositions and how they deal with these challenges.

MLOps Presentation: How to Build Resilient AI With GitOps

Published

Jan 12, 2022

Author

Dr. Phil Winder

CEO

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 focuses on how GitOps is a key ingredient in any ML platform to enhance resiliency and observability. Learn why it is important, what it involves, and how to implement it in this short 30 minute video.

About This Series

Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.AI, Author of O’Reilly’s Reinforcement Learning and one of the founders of the MLOps Community, covering a range of topics about the use of machine learning operations (MLOps), reinforcement learning (RL), and machine learning (ML) in industry today.

The Value of a Machine Learning Pipeline: Past, Present, and the Future of MLOps With Kubeflow

Published

Nov 1, 2021

Author

Dr. Phil Winder

CEO

Industrial machine learning consulting projects come in a variety of forms.

Sometimes clients ask for exploratory data analysis, to evaluate whether their data can be used to help solve a problem using artificial intelligence. Other times we use machine learning (ML) algorithms to automate decisions and improve efficiencies within a business or product. More recently we’ve refocused on reinforcement learning and customers ask us to help control some complex multi-step process.

In each case we start in a data science notebook.