Globally, there are two solutions: proprietary and Open Source. The main advantage of open-source tools is the flexibility to customize them. Let’s Let’s compare both approaches when choosing tools for MLOps.
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In proprietary solutions, there is no access to the code, meaning you cannot fix something that suddenly breaks on your own: you have to wait until the vendor’s vendor’s technical support solves the problem. In the case of Open Source, you have access to the code, which means you can quickly fix bugs.
Vendors often try to make a universal product. As a result, certain functions are not used in a particular project. The company still pays for it since the service cannot “cut out” functionality. In Open Source, you can add or remove the necessary elements.
Open Source gives you more control over your tools than closed proprietary solutions.
At the same time, many frameworks and libraries are being created for Open Source, which can be connected or disconnected as needed.
Most developers know how to work with popular Open-Source tools or can learn quickly. Companies that use proprietary software need help finding the right person for their stack.
Open-source MLOps tools are created and maintained by the user community. This enables faster innovation and bug fixes. On proprietary sites, fixing bugs and getting help can take much longer.
We favour Open-Source components for their construction when developing the Cloud ML Platform.
MLOps are practices that simplify workflows, help automate model deployment, and establish a business-to-regulation alignment chain. Based on this, the following advantages follow.
High performance. MLOps improves the productivity of the machine learning lifecycle through automation and standardization by pipeline. This reduces repetitive tasks such as collecting and monitoring data and gives data scientists more time to analyze data and train new models.
Reproducibility. In terms of large-scale deployments, reproducibility is critical. It helps reduce errors and ambiguities as the model moves from development to production. This capability then helps optimize models through techniques such as hyperparameter tuning or in-depth experimentation with different models.
Cost reduction. MLOps helps reduce the cost of machine learning by minimizing manual labor, simplifying bug finding, and improving model management.
Reliability. Monitoring the behavior of models affects not only the ML project itself but also the part of the business for which the model was created. With MLOps, you can quickly collect data and immediately retrain the model. For example, to improve scoring and decide to grant a loan faster.
You can independently select a stack of tools and set up an ML model development pipeline. To do this, you need to hire a department and MLOps engineers who will build MLOps, which is costly regarding human and financial resources. Another option is to use a cloud tool.
VK Cloud has a new Cloud ML Platform tool that allows you to set up a full ML development cycle following the MLOps approach. We have implemented open-source solutions so that each company can flexibly configure the infrastructure and not depend on vendors. At the same time, we have already prepared the tool to be immediately implemented in our processes.
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