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Henrik Skogström
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Henrik Skogström

At Valohai I lead the growth team. My mission is to ensure that no company tries to reinvent the wheel and waste their resources building their own MLOps tooling.

April 26, 2022

MLOps for IoT and Edge

Henrik SkogströmHenrik Skogström

There's a new wave of automation being enabled by the combination of machine learning and smart devices. With the complexity of use cases and amount of devices increasing, we'll have to adopt MLOps practices designed for IoT and edge.

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January 17, 2022

Managing AI Products: Feasibility, Desirability and Viability

Henrik SkogströmHenrik Skogström

Product management is as massive a topic as machine learning so let's start with a fundamental question. When is it worthwhile to develop an AI product? A helpful tool most PMs have seen for this is the Sweet Spot for Innovation that IDEO popularized.

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December 01, 2021

A Comprehensive Comparison Between Kubeflow and Argo

Henrik SkogströmHenrik Skogström

Finding the most suitable platform to build ML workflows may be a challenge. Some are looking toward specific tools built for ML/MLOps, such as Kubeflow, while others are looking at more general-purpose orchestrators such as Argo.

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November 25, 2021

A Comprehensive Comparison Between Kubeflow and Metaflow

Henrik SkogströmHenrik Skogström

Creating a pipeline to automate ML workflows is necessary to save time and improve efficiency. There are two popular open-source tools for ML pipeline orchestration: Kubeflow and Metaflow. In this article, we will compare the differences and similarities between these two platforms.

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November 16, 2021

A Comprehensive Comparison Between Kubeflow and SageMaker

Henrik SkogströmHenrik Skogström

Kubeflow and SageMaker have emerged as the two most popular end-to-end MLOps platforms. Kubeflow is the first entrant on the open-source side, and SageMaker has a robust ecosystem through AWS. In this article, we will compare the differences and similarities between these two platforms.

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November 09, 2021

A Comprehensive Comparison Between Kubeflow and Databricks

Henrik SkogströmHenrik Skogström

Databricks is a unified data analytics platform, while Kubeflow is an MLOps platform. In this article, we will look at how they are comparable and how they are very different.

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November 02, 2021

A Comprehensive Comparison Between Kubeflow and Airflow

Henrik SkogströmHenrik Skogström

Kubeflow and Airflow can both be used to orchestrate ML workflows. Airflow is the tool of choice for most engineers but this article will show what else MLOps platforms like Kubeflow can offer on top of DAGs.

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October 28, 2021

Machine learning lifecycle doesn’t end with the model

Henrik SkogströmHenrik Skogström

Let me preface this article by saying there isn’t a single accepted definition of a machine learning lifecycle. Most articles about the machine learning lifecycle tend to focus only on a small portion of the actual lifecycle: the Experimentation loop.

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September 03, 2021

DLOps: MLOps for Deep Learning

Henrik SkogströmHenrik Skogström

DLOps, deep learning operations, is an evolution of MLOps, looking to answer the unique operational challenges that deep learning sets. A skeptic may look at it as unnecessarily muddying the waters with a new buzzword.

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August 26, 2021

Three ways to install Valohai

Henrik SkogströmHenrik Skogström

One of the unique aspects of Valohai is that despite being a proprietary platform it can run in fully private, even airgapped, environments. Why is this important? Machine learning often revolves around data that is sensitive and thus data security is a fundamental requirement.

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August 24, 2021

What is the AIIA blueprint and how does Valohai fit into it?

Henrik SkogströmHenrik Skogström

The AIIA blueprint is an excellent starting resource for teams looking to implement their stack for machine learning development. The initiative draws inspiration from other popularized tech stacks.

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August 11, 2021

A Comprehensive Comparison Between Kubeflow and MLflow

Henrik SkogströmHenrik Skogström

As a data scientist or a machine learning engineer, you have probably heard about Kubeflow and MLflow. They are often compared against each other despite being quite different.

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July 05, 2021

Data Augmentation Helps Improve Model Accuracy

Henrik SkogströmHenrik Skogström

Putting together a suitable dataset for training a model can be one of the biggest challenges. Data augmentation is an approach where you start with an existing dataset and expand it to have more variety.

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June 15, 2021

The Best Machine Learning Podcasts

Henrik SkogströmHenrik Skogström

Summer is here and hopefully, for most of us, it means time to decompress. But if you are like me and learning is relaxing, podcasts are a great way to enjoy the summer weather while learning.

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May 04, 2021

The Three Roles in a Machine Learning Team (and Two Technologies to Connect Them)

Henrik SkogströmHenrik Skogström

It's becoming more important to think about the competencies of a team rather than expecting every individual to be an expert at everything related to machine learning.

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March 25, 2021

MLOps for AI Consultancies

Henrik SkogströmHenrik Skogström

How can MLOps make consultant-client relationships more productive? Starting with machine learning is a massive, strategic undertaking, and many are turning to consultancies and contractors to take the first steps with AI.

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March 18, 2021

If You Missed the MLOps Webinar

Henrik SkogströmHenrik Skogström

March 16th, we held a webinar to follow up on our MLOps eBook. Together with our co-authors, we wanted to tackle the goal we set for MLOps in the eBook: “The goal of MLOps is to reduce technical friction to get the model from an idea into production in the shortest possible time to market with as little risk as possible.”

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December 21, 2020

What Is The Difference Between DevOps And MLOps?

Henrik SkogströmHenrik Skogström

If you are involved with production machine learning in any way, understanding MLOps is essential. For people with software development experience, the easiest way to understand MLOps is to draw a parallel between it and DevOps.

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December 10, 2020

The Bus Factor in Machine Learning development

Henrik SkogströmHenrik Skogström

The bus factor is a common term in software engineering describing the risk of a key contributor disappearing unexpectedly from a project – because they get hit by a bus. In machine learning the bus factor is magnified significantly.

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November 30, 2020

When Should a Machine Learning Model Be Retrained?

Henrik SkogströmHenrik Skogström

Should a machine learning model be retrained each time new observations are available (or otherwise very frequently)? The answer is “it depends”, but this article looks at two components to consider: the use case and the costs.

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November 24, 2020

The Easiest Way to Become a Valohai User

Henrik SkogströmHenrik Skogström

Buying an MLOps platform is tricky and for that reason we’ve introduced a model where teams can sign up for a two-week proof-of-concept project to test out our platform with their environment and projects.

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November 17, 2020

When Is a Machine Learning Model Good Enough for Production, and How to Stress About It Only Once?

Henrik SkogströmHenrik Skogström

As you start incorporating machine learning models into your end-user applications, the question comes up: “When is the model good enough to deploy?” There simply is no single right answer.

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October 26, 2020

The MLOps Stack

Henrik SkogströmHenrik Skogström

To make it easier to consider what tools your organization could use to adopt MLOps, we’ve made a simple template that breaks down a machine learning workflow into components.

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October 21, 2020

Risk Management in Machine Learning

Henrik SkogströmHenrik Skogström

Machine learning and artificial intelligence allow businesses to gain new insights and improve their business processes. However, they expose companies to additional risks because humans do not explicitly program the algorithms. Let's look at some of these risks and how data scientists and compliance officers can help mitigate them.

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October 09, 2020

5 Signs You Might Be in Need of an MLOps Platform

Henrik SkogströmHenrik Skogström

Using the MLOps platform allows you to manage everything about machine learning in production, where each new update doesn’t feel like an entirely new project and easily dovetails to the last.

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September 23, 2020

Why MLOps Is Vital To Your Development Team

Henrik SkogströmHenrik Skogström

To make an analogy to a more traditional industry, machine learning is shipping goods while MLOps is containerization. And much like containerization of global shipping, MLOps is equal parts process and infrastructure.

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September 15, 2020

Introducing Minihai – Easiest way to run notebooks remotely.

Henrik SkogströmHenrik Skogström

Imagine this; you are working on a notebook that takes ages to run, and it bogs down your computer, so it's even hard to multitask. We've seen this countless times, which is why we are introducing Minihai.

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September 07, 2020

Why Are ML Engineers Becoming So Sought After?

Henrik SkogströmHenrik Skogström

For a long time, most machine learning initiatives have been stuck in a persistent state of proofs-of-concept. However, in the past year, we’ve seen a rapid acceleration of machine learning models getting real-world use. Consequently, machine learning engineers are increasingly sought after – nearly catching up to data scientists in posted jobs.

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