Today, mass amounts of data come from a myriad of applications and microservices. DevOps engineers are often tasked with ensuring that data is collected, retained, and secured in a way that follows strict regulations. Focusing on data security, many companies rely on VMware for various internal cloud-computing applications.
And rather than utilizing a public cloud, VMware allows companies to leverage much of that technology behind their own firewall.
In an ongoing effort to provide the fastest route from inception to market, Algorithmia offers an Enterprise platform on VMware. The same technology we developed that keeps ML models stable, scalable, and secure in the public cloud now has a place on-prem.
“If you look today, still the overwhelming majority of spend is on-premises.” Andy Jassy, CEO, AWS
This simplifies and provides built-in efficiencies for infrastructure work required by DevOps engineers. They can truly rest easy knowing critical resources can be provided seamlessly and with less overhead. Additionally, required handling of protected data is lessened because it resides safely behind your organization’s network firewall.
Native scalability with seamless integration
One of the main reasons a company’s IT chooses VMware products is due to its ability to manage how virtual machines and applications scale. Through this method, savvy firms are reclaiming equipment and adding it to pools of computing and storage resources. By combining these resources and distributing them as needed, VMware makes internal cloud infrastructure much easier to manage.
The same manageability that exists in VMware is extended into the Algorithmia Enterprise platform. Using the native scalability within VMware allows for a seamless integration of critical data science applications without the need for constant infrastructure modifications.
Your DevOps and IT teams will appreciate the ease of use while data analysts will revel in their new ability to utilize company resources seamlessly.
Faster results on critical data sets
Having the necessary storage and compute power on-premise allows for efficient control of data for analysis. This control is not only critical for security, it provides the least amount of latency possible. Since the data reside on your own equipment, it takes advantage of security policies already put in place by IT. Existing security is extended into the Algorithmia platform intrinsically. New security requirements are easily managed through the integration into the VMware product.
When hosting data for use with ML models, every aspect of the environment affects the rate of data transfer. Information hosted in cloud providers can be affected by service interruptions or additional traffic that affects latency. Security may be another issue that isn’t as easily managed as if the data were retained in-house. Algorithmia’s on-premise integration into the VMware product allows for the highest possible IOPS to critical data.
Shortening time-to-compliance for data standards
Since most of what companies do involve international markets, they aren’t only required to protect private data based on US standards. Companies must also adhere to the regulations found in the GDPR. By housing data on-premise, the requirements to scrub data for PII or financial data is lessened.
As any engineer or project manager can attest, ensuring compliance for multiple privacy guidelines can be a complicated and long-spanning venture. By using Algorithmia within the policy constraints of the VMware platform, companies are already a step ahead.
The protected data never leaves the confines of the network which is often-times already behind multiple firewalls and virtual networks.
Drop-in existing infrastructure ensures data security
DevOps engineering is expanding to include critical security and data standards compliance. Many companies are now looking towards the practice of DevSecOps. By building security into every aspect of what they do, this extension of traditional DevOps ensures compliance from the start. Using VMware allows many corporations flexibility while still maintaining high levels of automation and security as the foundation of their infrastructure.
Introducing the Algorithmia platform into an existing VMware implementation is a “drop-in” extension of the high-level security enjoyed by companies that depend on it. As security concerns are recognized by top technologists, they are immediately addressed by minor or major version upgrades to the VMware host software.
In this way, the Algorithmia Enterprise platform also benefits from these critical updates. Existing ML models and critical data are continually secured at the infrastructure level.
When searching for technology that supports the latest in AI, using this drop-in model to an already secured VMware implementation should put Algorithmia at the top of your list.
Algorithmia on VMware makes any-prem ML a reality – a blog post
Algorithmia on VMware – product release notes