Without MLOps the process of deploying models can often be labour intensive, frustrating, slow and prone to error. MLOps makes the whole process more transparent; reducing inconsistencies, improving troubleshooting, and speeding up the entire process of AI deployment.
MLOps is about empowering data scientists to productionise AI models. It puts the correct tools in the hands of the data scientists, so that the work they do can be run in production.
MLOps also makes it easy for data scientists to collaborate with one another - helping them work more efficiently together, with less time wasted and fewer errors.
MLOps systems are also easy to replicate and transfer, creating further efficiencies on future deployments too. By minimising manual steps for data scientists, errors are reduced and progress is faster, for rapid deployment and iteration.
Another huge benefit of MLOps is clear model provenance. It gives much clearer tracking of every stage in model development. This in turn allows for more efficient debugging, response to compliance requests and development of new features.
There are two main options when it comes to MLOps solutions. Either, you buy a ready-made ‘off-the-peg’ MLOps platform, or, use open source tools to build a solution that’s right for you. Both approaches have benefits, but the choice can be bewildering - especially when it comes to proprietary platforms, all of which claim to be ‘the best’.
We believe open source MLOps solutions offer greater freedom and flexibility in the long run, and are the fastest way to realise the benefits of MLOps without the risk of being tied in to any one provider or platform.