Algorithmia

Seattle, WA, USA
2013
Apr 7, 2021   |  By Diego Oppenheimer
Algorithmia was named today by CB Insights on its fifth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world.
Apr 6, 2021   |  By Aslı Sabancı
We’re excited to share a new integration between YData and Algorithmia. In this blog post, we walk you through the process of combining these two powerful machine learning platforms.
Mar 24, 2021   |  By Diego Oppenheimer
A recent speech by Governor Lael Brainard of the US Federal Reserve emphasized the importance of machine learning governance. Read more in this blog post.
Mar 11, 2021   |  By Diego Oppenheimer
We’re excited to share that we’ve received an honorable mention in Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms.
Mar 9, 2021   |  By Patchen Noelke
Model risk refers to the inherent risks associated with running machine learning models in production. Read this post to learn how to manage model risk.
Mar 2, 2021   |  By Craig Perrin
Learn how cutting-edge financial institutions are applying machine learning and accelerating their results with machine learning operations (MLOps).
Feb 26, 2021   |  By Daniel Schafer
Businesses are dramatically increasing the resources they put into machine learning initiatives for 2021. Here’s what this means for your own ML strategy.
Feb 25, 2021   |  By Diego Oppenheimer
Algorithmia, along with 25+ fellow leaders in the AI and ML space, is excited to announce the founding of the AI Infrastructure Alliance. Learn more in this blog post.
Feb 23, 2021   |  By Diego Oppenheimer
We're excited to announce a partnership between Arthur and Algorithmia. Learn how to get started with the Arthur and Algorithmia integration, so you can complete your AI stack.
Feb 16, 2021   |  By Hernan Alvarez
Machine learning operations (MLOps) is the key to your ML success. This post breaks down the five most common misunderstandings we encounter about MLOps.
Apr 1, 2021   |  By Algorithmia
We’re excited to share our new integration with MLflow, a popular open-source platform for managing various stages of the ML lifecycle. With this new integration, you can build and train your models using MLflow, then deploy them to production with Algorithmia where you can use our advanced features for enterprise ML operations and governance.
Mar 18, 2021   |  By Algorithmia
In this video, you will learn how to insert a CI/CD system into your workflow to complete an automated build and publish whenever you push your code changes to Git.
Mar 18, 2021   |  By Algorithmia
In this video, we will show you how to create a simple workflow for building and publishing a model.
Mar 18, 2021   |  By Algorithmia
In this opening video, we answer the question "Where does Algorithmia fit into the machine learning lifecycle?" Algorithmia is machine learning operations (MLOps) software that manages all stages of the ML lifecycle within existing operational processes and tools. Put models into production quickly, securely, and cost-effectively.
Sep 29, 2020   |  By Algorithmia
Join us for a conversation with Diego Oppenheimer, CEO of Algorithmia, and Kahini Shah, Investor at Gradient Ventures, as they discuss the impacts that the global pandemic is having on AI/ML.
Jan 22, 2021   |  By Algorithmia
As more businesses begin to realize the full potential of AI to deliver business results from their data, they're starting to bump up against their ability to manage it all. As the amount of data and number of models grow, organizations can accrue significant technical debt. Chief risk officers (CROs) and model risk managers can be left asking themselves, "Do I spend more to keep up with model demand, or do I accept more risk?"
Sep 24, 2020   |  By Algorithmia
According to Gartner, 85 percent of all AI projects fail, and the majority of organizations actively developing a machine learning capability are struggling to extract a return on their AI investment. Therefore it is crucial to know up front what to expect in terms of infrastructural requirements, developer workloads, time, and costs associated with building an in-house machine learning management platform so you can prepare to meet your goals.

We help every company develop an optimal path to machine learning operational maturity. Use our framework to assess your ML roadmap location and we'll chart your path to maturity. We are machine learning, managed.

Algorithmia streamlines your ML lifecycle:

  • Automate the software development lifecycle for machine learning: MLOps teams can securely scale a machine learning portfolio across their organization without sacrificing on delivery quality.
  • Securely deliver AI applications on time and on budget: With a secure ML management system that scales for enterprise workloads, business leaders can deliver value faster.
  • The simplest route to deployment for data scientists: With Algorithmia, data scientists can deliver data insights by deploying AI models from any tool, language, or framework at 
DevOps speed.

Machine learning maturity for every business.