DevOps Engineers are delighted when they find a product or service that fits in with their already refined CI/CD processes. When we choose a new pipeline tool, engineers depend on a number of factors. Ease of use, repeatable processes, and a solid support model are key. Choosing a continuous integration tool for machine learning model deployment is no different.
Algorithmia puts DevOps up front
When a company starts with a DevOps perspective from the onset, the finished product can more easily complement new deployment challenges every day. Algorithmia recognizes model deployment obstacles and takes those key aspects of DevOps to heart in its ML platform, which offers instant, repeatable deployments, thereby removing the burden that typically falls on data scientists.
By using existing DevOps concepts centered on deployment, security, and scalability, Algorithmia overcomes many of the hurdles necessary to decrease the time it takes for ML models to make it to production.
Algorithmia also adheres to DevOps principles in providing a fully scalable production environment for hosting completed machine learning models. Since much of the underlying technology is run on tried and true products and processes, important functions like auditing and traceability are in place up front.
Future-proofing model management
From the start you will notice that models are maintained in a familiar repository structure (Git). These models can be consumed through most major programming languages data scientists use, such as Python, R, Java, and Algorithmia’s own CLI client.
Most big data spans vast amounts of storage, and giving your models access to that data should not force you to move from one cloud to another. In addition to providing your own, secure area for Hosted Data Collections, the data you already host in Amazon S3, Google Cloud Storage, Azure, and even Dropbox is easily accessible by your models from within your Algorithmia account.
Model collaboration made easy
To complement its deployment capabilities, Algorithmia has included a number of other features meant to enable collaboration inside (or even outside) your organization. Publishing your models and sharing them with other users can help build more advanced applications and is paramount to preventing tech silos in decentralized organizations. When you manage your model portfolio on Algorithmia, you control how and where your data is shared.
DevOps engineers strive to remove barriers that block innovation in all aspects of software engineering. Now faced with an additional task of deploying a myriad of AI models, that same attitude will ensure data science products will open up even more opportunities for data exploration and use.
Remove deployment barriers
Luckily, deploying machine learning models with Algorithmia can be just another process intrinsic to the tenets of DevOps engineers. Algorithmia recognizes that there would be significant challenges in the field of machine learning model deployment otherwise. Data scientists would take on unnecessary aspects of infrastructure (that could be easily handled by the platform) to ensure their models would complement existing DevOps procedures. On the DevOps side, Algorithmia recognized that without a deployment platform, DevOps engineers might be deploying something foreign to them. For that reason, the Algorithmia platform is the natural approach to the new and ever-evolving field of machine learning for DevOps engineers and data scientists alike.
The Algorithmia Learning and Training Center
Machine learning infrastructure best practices
Permissioning your algorithms on Algorithmia