At Algorithmia, we have learned how enterprise machine learning is developing across industries, and we are most interested in the challenges that hinder organizations from extracting ML value.
In late 2019, we published data on the main challenges, which we consolidated in a report called the “2020...
The machine learning lifecycle begins with data warehousing, ETL pipelining, and model training. At Algorithmia, we focus on the next stages in the lifecycle: deployment, management, and operations. Machine learning deployment plays a critical part in ensuring a model performs well, both now and in the...
The difference between machine learning and statistics has been the subject of long-running debate. A debate so contentious at times, that it has even become the subject of memes.
Some say that machine learning is just glorified statistics, rebranded for the age of big data and faster computing. Others...
In the last 12 months, there have been myriad developments in machine learning (ML) tools and applications, yet many companies are still struggling to see tangible business results from AI models in production. Deploying AI/ML solutions isn’t easy, but Algorithmia and Appen make it simple to tackle...
With the prevalence of computer science constantly rising, knowing at least the basics of machine learning systems is extremely valuable in business. Today we will be discussing 8 of the best machine learning books, from beginner to expert level, along with the topics covered in each, where you can get...
We encounter the results of prediction models everyday. We get offered the right incentive to buy a product. We get a text from the bank when something strange is happening with our accounts. And we rarely have to see spam in our inboxes. This is all thanks to the models built on the data that we generate...
Data insights are knowledge that a company gains from analyzing sets of information pertaining to a given topic or situation. Analysis of this information provides insights that help businesses make informed decisions and reduces the risk that comes with trial-and-error testing methods.
In the digital...
In February we announced our support of GitHub as a repository storage system for any algorithm on Algorithmia. This is big news and it unlocks a lot of potential for developers. In this post, we’ll show you how integrating with GitHub can enable new workflows and automation systems that can magnify...
You’ve probably heard it said in machine learning that when it comes to getting great results, the data is even more important than the model you use. This is true, and it’s not just the native data that’s so important but also how we choose to transform it. This is where feature...
Python is one of the most commonly used programming languages by data scientists and machine learning engineers. Although there has been no universal study on the prevalence of machine learning algorithms in Python in machine learning, a 2019 GitHub analysis of public repositories tagged as “machine-learning”...
