Importance of MicroServices based Machine Learning development projects
Importance of MicroServices based Machine Learning development projects:
Projects involving machine learning algorithms or data science algorithms, generally use various libraries and other dependencies in order for the model or algorithm to run. Now, if there were multiple parts to your project, and you happen to work on a monolithic architecture, you will very soon encounter a problem of conflicting deployment dependencies, meaning that the requirements of some components of your project are overlapping with variations in the versions of the dependencies. Not only that, you might as well want to use a completely different technology stack for a particular component development.
Microservices are the best choice in such scenarios. Basically you break down your monolithic application into independent components called a microservice. Each microservice can implement its own set of ML algorithms, having its unique dependencies stored in a requirements.txt file. These requirements will be completely independent from requirements of a different microservice, hence giving you the flexibility to choose the libraries which suits best for your particular component. Not only that, machine learning algorithms have different levels of complexities, which might require different amounts of computation power. We can easily achieve this using microservices by deploying them on independent machines. Various microservices interact with each other in order to coordinate on a task. Any change made to a specific microservice does not affect other microservices. Also, teams can work independently on each microservice due to the decoupled nature of the architecture.
We can use docker containers as well for microservice deployments, which further eases out the deployment wherein the development teams ensure proper configurations are made in the docker files and are tested locally. All you need to do is deploy the same docker image in your product code base and all the requirements are pre-satisfied. Working at scale, the interaction between microservices might be a bottleneck, hence, we can use message brokers like Kafka. If you are using Django for your machine learning deployment, you can use Kafka logpipe to easily integrate your application with Kafka.
To summarize, monolithic applications might be useful for very small applications not involving varied machine learning requirements, but for projects with multiple components having different requirements, microservices are the way to go. There are ways to further manage scalability of docker containers using Kubernetes, which makes it a good choice for production level machine learning projects.