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Blog / Henrik Skogström
Henrik Skogström

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.

Blog
April 26, 2022 Henrik Skogström
MLOps for IoT and Edge

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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Blog
January 17, 2022 Henrik Skogström
Managing AI Products: Feasibility, Desirability and Viability

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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Blog
December 15, 2021 Henrik Skogström
A Comprehensive Comparison Between Metaflow and MLflow

If you have a large team interested in large in-production use-cases, Metaflow is a great option for you. However, if you would like to standardize the whole ML workflow, MLflow is a viable option. In this article, you will learn about the similarities and significant differences between Metaflow and MLflow.

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December 10, 2021 Henrik Skogström
A Comprehensive Comparison Between Metaflow and Airflow

If you want a scalable platform that allows you to manage ML workflows, Metaflow is a better choice, while if you want to orchestrate a variety of tasks, Airflow is a viable option. In this article, you will learn about the key differences and similarities between Metaflow and Airflow.

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Blog
December 05, 2021 Henrik Skogström
A Comprehensive Comparison Between Metaflow and Amazon SageMaker

Metaflow is a viable option if you need a specialized platform to build pipelines and SageMaker has a robust ecosystem through AWS. In this article, we will compare the major similarities and critical differences between Metaflow and Amazon SageMaker.

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Blog
December 01, 2021 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and Argo

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 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and Metaflow

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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Blog
November 16, 2021 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and SageMaker

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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Blog
November 09, 2021 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and Databricks

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 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and Airflow

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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Blog
October 28, 2021 Henrik Skogström
Machine learning lifecycle doesn’t end with the model

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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Blog
September 03, 2021 Henrik Skogström
DLOps: MLOps for Deep Learning

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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Blog
August 26, 2021 Henrik Skogström
Three ways to install Valohai

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

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 Henrik Skogström
A Comprehensive Comparison Between Kubeflow and MLflow

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 Henrik Skogström
Data Augmentation Helps Improve Model Accuracy

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 Henrik Skogström
The Best Machine Learning Podcasts

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

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 Henrik Skogström
MLOps for AI Consultancies

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 Henrik Skogström
If You Missed the MLOps Webinar

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 Henrik Skogström
What Is The Difference Between DevOps And MLOps?

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 Henrik Skogström
The Bus Factor in Machine Learning development

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 Henrik Skogström
When Should a Machine Learning Model Be Retrained?

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 Henrik Skogström
The Easiest Way to Become a Valohai User

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

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 Henrik Skogström
The MLOps Stack

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 Henrik Skogström
Risk Management in Machine Learning

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 Henrik Skogström
5 Signs You Might Be in Need of an MLOps Platform

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 Henrik Skogström
Why MLOps Is Vital To Your Development Team

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 Henrik Skogström
Introducing Minihai – Easiest way to run notebooks remotely.

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 Henrik Skogström
Why Are ML Engineers Becoming So Sought After?

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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