Valohai Blog

What if I told you there is a simple, free, lightweight tool for weaponizing any CLI-based ML project. All the commands are nicely wrapped and accessible via shortcut aliases and only a TAB keypress away. This tool is easily installable and super robust for all operating systems! It is called Make.

We look at industries with the highest need for artificial intelligence solutions in 2022, why they need it, and gives example use-cases.

Here are some tips and tricks for Jupyter notebook with step-by-step guides: from running shell commands to changing the notebook theme - easily!

There is absolutely nothing wrong with notebooks, and they are fantastic for many use-cases, but they are not the only option for writing programs. Too many get stuck in the vanilla notebook and do not realize what they are missing out on.

Valohai has been mentioned as a Representative Vendor by Gartner® in the “Market Guide for DSML Engineering Platforms”

We want to talk about what distributed learning is in brief and focus more on why having this feature is a valuable tool for your business.

What started as a fun side project for our developer Magda turned out to be a proud addition to the platform. Valohai can now estimate the carbon emissions of cloud instances. Yay!

Almost any programming language in the world is more powerful than the command line. Why would you even bother doing anything on it? Don't be fooled: the modern command line is rocking like never before!

Suppose you find your projects to be in the gray area between the extremes of delayed and real-time inference where you can go with either one, ask yourself if you can delay. And if you can, you should!

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.

Machine learning comes with new types of risk. We need to minimize the risk by addressing how we develop these algorithms and also how we apply these algorithms in the real world. In this article, we'll look at three ways of mitigating the latter – i.e. output risk.

Mike Del Balso is a familiar name to most in the machine learning community. He's one of the pioneers in the MLOps space and has laid the foundations for operational machine learning at Uber, Google, and most recently, Tecton.

Syngenta is a leading provider of agricultural science and technology focused on seed and crop protection products aiming to improve global food security by enabling millions of farmers to make better use of available resources.

Algorithms have become faster, fancier, and more complex in the past couple of years. Still, they haven't gained as much complexity as the systems around algorithms. In this article, we'll discuss three examples of systems complexity.

Docker isolates the software from all other things on the same system. A program running inside a "spacesuit" generally has no idea it is wearing one and is unaffected by anything happening outside.