It is getting more popular than ever for AI systems to be deployed on the hybrid cloud. The major reason for this is the rise in regulations concerning privacy and user data. The location of where that data is stored can have a major impact on the steps companies need to take to ensure that they remain compliant. For example, the state of California has some of the most strict data security policies for companies keeping user data. These policies are similar to data security policies that were passed in the European Union. Companies now have to deal with the challenge of streamlining the deployment of their machine learning systems into these hybrid cloud environments.
Why Companies Use the Hybrid Model
We have previously covered the legal reasons for using the hybrid model for the deployment of ML models in the first paragraph. MLOps is a little bit more complicated than DevOps because you can sometimes work with private user information. Data security laws govern this information, and these laws vary by country and even the states in America. There is also the added complexity of where the data is located versus where it is processed. Ideally, processing data as close to its source as possible would be the fastest and most affordable option. This is because the data wouldn’t travel as far, and you would have to pay for the bandwidth needed to cover those costs. It would also simplify many deployments, as managing an architecture where you process data far from its source is a lot more difficult than simply ingesting and processing the data at the same location.
Benefits of Hybrid Model
The main benefit of the hybrid model for machine learning is its flexibility. Companies can keep a portion of their data on their on-premise servers. The company can then move all the data, depending on local regulations, to cloud servers worldwide. With the almost infinite availability of cloud resources, companies have access to much more computer power, such as Amazon Web Services and Microsoft Azure.
Downsides of This Model
Despite the benefits, this hybrid model does have a few downsides. These downsides lead to companies needing ways of speeding up their machine learning deployments. MLOps gets a lot more complicated when you have to deal with a variety of computing resources from different companies. There are currently no abstractions that can help us deal with these resources, which is ultimately the major downside. It is also quite impractical to have to move your data from on-premise to the cloud or even from your edge computing devices. It can take a lot of time, and it might be a lot more expensive to do things this way. While many of these problems cannot be avoided, you can solve the complexity issue with orchestration tools.
Using Orchestration to Build a Working MLOps Stack in the Hybrid Cloud
Orchestration tools like Kubernetes have done a wonderful job of making DevOps easier for enterprises. The main method of speeding up your machine learning software stack and deployment in the hybrid cloud will be the use of orchestration tools like those found in the DevOps field. Since machine learning projects usually involve researching, production, and then monitoring the data, we can create tools that automate many of these problems. It would do it in the same way that tools like Jenkins can help automate running unit tests on your code in DevOps.