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William Manning

Senior Solution Architect, JFrog ML

William is a JFrog ML Senior Solution Architect. A technical and marketing product strategist with over 20+ years of experience in building and developing successful products and teams, William is also an active speaker, mentor, and advisor to various startups. Prior to joining JFrog, he successfully exited 3 companies and took one public in Australia. In his spare time, he likes to travel with his wife and two boys. He also plays guitar, loves gadgets & IOT, lives for the beach, rides skateboards, and is an avid cyclist.

The Latest From William Manning

  • Get to Know JFrog ML
    | 6 min read

    AI/ML development is getting a lot of attention as organizations rush to bring AI services into their business applications. While emerging MLOps practices are designed to make developing AI applications easier, the complexity and fragmentation of available MLOps tools often complicates the work of Data Scientists and ML Engineers, and lessens trust in what’s being…

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  • MLOps Your Way with the JFrog Platform
    | 5 min read

    Just like in traditional software development, creating AI applications isn't a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads - difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on. Regardless of how you build it, to accelerate production-ready AI,…

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  • An Interview with a Data Scientist
    | 5 min read

    In this interview, we reached out to one of our customer’s data scientists to talk about their experience working with data, as well as their insights into MLOps and the challenges involved in deploying machine learning models in production. We discussed the daily challenges of dealing with incomplete or messy data, as well as communicating…

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  • A Brief Comparison of Kubeflow vs. MLflow
    | 7 min read

    Kubeflow, created by Google in 2018, and MLflow, an open-source platform for managing the end-to-end machine learning lifecycle are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production. As a data scientist or machine learning (ML) engineer, you’ve probably already heard of them. They’re two of the most popular…

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  • A Brief Comparison of Kubeflow vs. SageMaker
    | 7 min read

    Kubeflow, created by Google in 2018, and Amazon SageMaker, a cloud machine learning platform, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production. As a data scientist or machine learning (ML) engineer, you’ve probably already heard of them. They’re two of the most popular open-source tools available today,…

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  • A Brief Comparison of Kubeflow vs. Metaflow
    | 8 min read

    Kubeflow, created by Google in 2018, and Metaflow, created by Netflix in 2019, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production. Indeed, there’s no shortage of similar MLOps tools available on the market right now that all promise to do one thing: make the lives of ML…

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  • A Brief Comparison of Kubeflow vs. Databricks
    | 7 min read

    Kubeflow and Databricks are just two of a wide range of MLOps tools available on the market that are helping ML teams to streamline their workflows and deliver better results. As the number of MLOps tools has exploded, however, it has become more challenging for decision makers to figure out which ones to use and…

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  • A Brief Comparison of Kubeflow vs Airflow
    | 8 min read

    There has been an explosion in new technologies and tools for managing tasks and data pipelines in recent years. There are now so many of them, in fact, that it can be challenging to decide which ones to use and understand how they interact with one another, especially because selecting the right tool for your…

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  • A Brief Comparison of Kubeflow vs Argo
    | 7 min read

    Organizations are rapidly investing in MLOps to enhance their productivity and create cutting-edge machine learning (ML) models. MLOps helps to streamline the ML lifecycle by automating repeatable tasks and providing best practices to help ML teams collaborate more effectively. As a result of the growth of MLOps in recent years, there has been an explosion…

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  • An introduction to Hugging Face transformers for NLP
    | 9 min read

    If you have been paying attention to the latest developments in machine learning (ML) and artificial intelligence (AI) over the last few years, you will already be familiar with Natural Language Processing (NLP), largely in part due to the development of burgeoning transformer models—and we’re not talking about the popular shape-shifting cartoon robots. Rather, we…

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