Why Companies need to think about MLOps and how zerocodeai user gain highest value from it.
For many years, machine learning (ML) researchers have focused on creating better and better models and figuring out how to get even more performance out of them. At some point, however, it had to be acknowledged that creating top-notch models doesn't equate to them delivering direct business value. Even the best models can be complex and costly to deploy in production. In fact, according to VentureBeat, the vast majority of models (87%) never make it into production.
Machine Learning Operations (MLOps) ensures that the models (so far) created by the Data Science team can later be used in production. MLOps is the generic term for the technologies that automate the management, deployment, monitoring, and alerting of ML models. MLOps ensures that a production-ready infrastructure is in place to run the models developed.
zerocode.ai has been built according to MLOps standards that the user always follows exactly the same structure, ensuring successful development.
1. Realise business value
Models that are not deployed are useless. They are even worse than useless. The time and money invested in developing AI-powered solutions really should result in tangible business value, and that is only possible if they are deployed in production. By providing a framework, clear workflows and standards in the tool for deploying your models, MLOps ensures that the business value of a model is realized.
2. Motivated Teams
Teams that spend their time developing models that are never used will quickly become fatigued and demotivated. In contrast, a team that constantly deploys new models whose use constantly impacts the entire organization will be excited and motivated to keep building on the results. Thanks to this positive feedback loop, employees will want to stay with your company longer and will be encouraged to experiment and be creative. This creativity can lead to innovative solutions that other teams wouldn't even dream of.
3. Less wasted time
It is not uncommon for data science teams to spend days building models, only to be told by the ops team that the solution cannot be translated into reality. This situation can be very unsatisfying for both teams and lead to great frustration.
However, a robust MLOps function like the one present in the platform results in more models in production at any one time and results in minutes instead of weeks, giving the development team more reference experience on which models can and cannot be deployed. The team can then use this knowledge to spend more time on projects that have a high chance of being deployed and reduce time spent on models that will never work in production.
In addition, without MLOps, the data science team would have to manually deploy and maintain its models over and over again. This work is routine, time-consuming, and doesn't produce the new models that actually drive the business forward. Fortunately, the platform automates most of this tedious work, saving significant time and ensuring that your data science team can focus on high-value, interesting work.
4. Fast repeatable workflows
ML engineers have not been able to know exactly how well a model will perform or what kind of data they will see in production until the model is actually in production. They used a wide variety of techniques to make estimates and reduce the risk of developing inferior solutions, but the only way to really know was when the model was deployed.
However, an efficient MLOps process knows that you will want to re-train your models in a few days/weeks/months anyway. Therefore, fast, repeatable workflows were implemented that allow you to account for model drift or completely retrain a model based on new data.
Implementing such practices has long been a bottleneck for data science teams.