Insights from the deep learning industry.
This article is primarily written for business decision-makers looking to understand why they should invest in MLOps. For a more technical reader, this article might help identify some arguments if you are trying to convince your managers. Investing in machine learning will enable you to solve business cases that were previously impossible to solve, for example, automatically categorizing images. Contrary to ML, MLOps in itself doesn’t come with a promise to directly solve any business problems. Rather it comes with the promise to accelerate how your investments in ML return value. 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.
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.
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.
Valohai, the MLOps platform company, is collaborating with Twitter and Facebook to launch a competition for the annual The Neural Information Processing Systems (NeurIPS) conference to advance the optimization of machine learning models towards more accurate AI solutions. The goal is to find better optimization algorithms for machine learning.
Colabel decided to adopt Valohai to manage their machine learning infrastructure & model serving through Kubernetes instead of hiring an MLOps engineer. TL;DR colabel enables companies to automate workflows specific to their business, from recognizing objects in microscopic images to automatically categorizing incoming documents for different internal workflows. They needed a solution that automatically manages their machine learning infrastructure and model serving through their Kubernetes cluster on Google Cloud Platform. colabel started by building their own MLOps solution with Kubeflow but quickly realized that the costs associated with building, maintaining, and hiring the right talent to build your own MLOps solution were not financially sustainable. Today colabel can focus on building their platform and its integrations, leaving the hassle of maintaining the machine learning infrastructure and Kubernetes to Valohai. colabel helps businesses build custom models for automating image and document processing Every business has workflows that can be automated and every business is different. Using out of the box solutions, such as APIs to classify dogs and hot-dogs will take you only so far.
Most software development teams have adopted continuous integration and delivery (CI/CD) to iterate faster. However, a machine learning model depends not only on the code but also the data and hyperparameters. Releasing a new machine learning model in production is more complex than traditional software development.
Grid search and random search are the most well-known in hyperparameter tuning. They are also both first-class citizens inside the Valohai platform. You define your search space, hit go, and Valohai will start all your machines. It does a search over the designated area of parameters you’ve defined. It is all automatic and doesn’t make you launch or shut down machines by hand. Also, you don't accidentally leave machines running costing you money. But we’ve been missing one central way for hyperparameter tuning, Bayesian optimization. Not anymore!
Finding the right subreddit to submit your post can be tricky, especially for people new to Reddit. There are thousands of active subreddits with overlapping content. If it is no easy task for a human, I didn’t expect it to be easier for a machine. Currently, redditors can ask for suitable subreddits in a special subreddit: r/findareddit.
A lot of companies and teams are going fully remote for the first time due to the Coronavirus. We at Valohai are big believers in remote work. Having practiced with a distributed team for a good 4 years we would like to share some of our thoughts on remote work in Machine Learning. A lot of major pain points we have seen revolve around tooling.