Here at Algorithmia, we have written and talked extensively about the challenges of deploying machine learning models at scale and the importance of data access in general machine learning development and infrastructure. When it comes down to it, ML is critical for any modern company to remain competitive.
Machine learning has the most impact on a company’s core line of business applications that are often behind the firewall, particularly in regulated industries like financial services, insurance, health care, and laboratory sciences.
For ML infrastructure to serve those industries, an on-premise product is a requirement. And users need low latency, high throughput, data-driven applications in a modern data-driven world. To those ends, we are thrilled to announce that Algorithmia Enterprise is now on VMware!
Go where the data and users are
Data and security go hand in hand, which is why concerns around ML security are the natural product of that relationship.
One concern is the security implications of moving data between systems. Another is that data is expensive and difficult to move, so building and running ML models close to the data source is a preferred practice as it reduces costs, increases iteration speed, and satisfies security, compliance, and privacy requirements that many businesses have.
Announcing Algorithmia Enterprise on VMware
The general availability of Algorithmia Enterprise on VMware, the next version of our enterprise on-premises product, means customers can run Algorithmia on their existing VMware infrastructure in their data center with the lowest latency and highest security for their ML–enabled applications.
By providing a fully integrated solution for connecting, deploying, scaling and managing, we are enabling enterprises to leverage ML in a way they could not before.
Multi-cloud is in our DNA
Customers faced with the challenges of multi-cloud sometimes try to build their own complex systems using native or in-house services across many cloud providers. This creates massive variability and volatility in deployment, upgrades, performance, and customer experience. And don’t forget that the engineering and support matrix grows with each variant.
From the early days at Algorithmia, we knew that multi-cloud was critical to enabling our customer’s success, given the vastly different infrastructure choices one could make. So we focused on getting the foundation right as we knew the speed and quality of the deployment experience is a crucial advantage for customers.
And we also know that the customer experience must be fantastically consistent across any platform. By delivering on a truly multi-cloud platform that has UX, feature, and operational parity, we solve these problems for our customers and ensure a delightfully consistent experience.
The market has spoken. VMware has won the on-premises cloud war and serves the majority of the private cloud/hypervisor market. The rest of the landscape is fractured, the number of variants and incompatibilities too high to navigate. The next largest vendor adoption is low, less than 10 percent.
Services via VMware are the standard offered in nearly every IT environment. By choosing VMware as the preferred on-premises infrastructure platform, again we are enabling the greatest number of companies to achieve their full potential through the use of AI and ML.
Multi-cloud and any-prem
Now with Algorithmia Enterprise on VMware, multi-cloud ML deployment across public and private clouds is not just a wish, it is a reality. Companies that leverage the benefits of having their ML workload close to the data it needs and users that need it will realize that multi-cloud is a true differentiator for their business.