AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

An Introduction to Reinforcement Learning: All You Need to Know

Published
Author
Dr. Phil Winder
CEO

When a child wants to ride a bike, they learn by doing. This process of trial and error is also known as learning through reinforcement, because positive and negative experiences promote or discourage certain behaviours, respectively. Children learn to ride by avoiding actions that cause them to crash; for the thrill of feeling wind in their hair.

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

Published
Author
Enrico Rotundo
Associate Data Scientist

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.

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Buildpacks - The Ultimate Machine Learning Container

Published
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.

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

Published
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

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Save 80% of Your Machine Learning Training Bill on Kubernetes

Published
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.

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

Published
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.

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MLOps Presentation: Databricks vs. Pachyderm

Published
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.

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Machine Learning Presentation: Packaging Your Models

Published
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.

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GitOps for Machine Learning Projects

Published
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.

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