taha7ussein007 / deployed_ml_models

Here i'm practicing on not just building the ML Models but also how to successfully deploy them and automate continuous-integration.

ML Deployed Projects

The benefits for business are in the interference phase when ML algorithms provide information before it is known. There is a technological challenge on how to provide ML algorithms for inference into production systems. There are many requirements which need to be fulfilled:

• ML algorithms deployment automation,
• continuous-integration,
• reproducibility of algorithms and predictions,
• diagnostic and monitoring of algorithms in production,
• governance and regulatory compliance,
• scalability,
• users collaboration.

There are many ways of how ML algorithms can be used:

• The simplest approach is to run the ML algorithm locally to compute predictions on prepared test data and share predictions with others. This approach is easy and fast in implementation. However, it has many drawbacks. It is hard to govern, monitor, scale and collaborate.
• The second, similar approach, is to hard-code the ML algorithm in the system's code. This solution is rather for simple ML algorithms, like Decision Trees or Linear Regression (which are easy to implement independently of the programming language). This solution behaves similar to the first approach - it is easy to implement with many drawbacks.
• The third solution, it to make the ML algorithm available by REST API, RPC or WebSockets. This method requires the implementation of the server which handles requests and forwards them to ML algorithms. In this approach, all requirements for the ML production system can be fulfilled.
• The last solution is to use a commercial vendor for deploying ML algorithms - it can be in the cloud or on-premise. Sometimes, this can be a good solution. When you have a standard ML algorithm so the vendor can handle it and you have money to pay to the vendor (it can be pricy).

This repo covers the basics which should be enough to build your ML system which:

• can handle many API endpoints,
• each API endpoint can have several ML algorithms with different versions,
• ML code and artifacts (files with ML parameters) are stored in the code repository (git),
• supports fast deployments and continuous integration (tests for both: server and ML code),
• supports monitoring and algorithm diagnostic (support A/B tests),
• is scalable (deployed with containers),
• has a user interface.

There are many ways in which this repo can be extended, for example:

• running long jobs for batch predictions or algorithm training with Celery,
• running scheduled jobs with Celery,
• WebSocket interface for Internet-of-Things applications (with Django Channels),
• authentication and user management.