In this article, we discussed how machine learning is related with operations management and what kind of problems operation managers should also consider in order to better use machine learning technology.
MLOps is basically a set of practices that allow for collaboration and communication between operations and data science professionals. It is designed in such a way that there is no waste, more automation, and greater insights with machine learning. MLOps allows data scientists to build and deploy models to get the insights that they are looking for.
MLOps and Data scientists:
AI and ML are becoming an integral part of many modern businesses. Many analysts say that organizations fail to deliver successful AI-based applications as they are stuck in the process of turning data-science models. These data sets are only tested on sample data, and do not take into account how different the real-world data is.
The combination of AI and ML with DevOps practices to create continuous deployment and delivery of data and ML intensive applications is what MLOps is.
Most data science teams don’t do data science alone, they spend a lot of time on data wrangling, data preparation, managing packages and frameworks, and so on. All of this can be considered as feature management tasks and MLOps tasks.
MLOps is at the intersection of DevOps, data engineering and machine learning. MLOps is built on top of the existing concept of DevOps, and the MLOps solutions are designed to get powerful insights with machine learning.
MLOps solution provides data scientists with an easier and efficient way to maintain monitor models. By getting models into production and bridging the gap between the stakeholder teams, they can focus on data science.
With the help of MLOps, deployment can be done on any platform. The data scientist can even pick the frameworks or the languages that they want to use. MLOps provides a system for monitoring all the models, no matter where they are deployed or the kind of frameworks that are used to build the models. The production lifecycle management available in the MLOps framework makes it easier for data scientists to manage a large portfolio of production models.
MLOps’ production model governance makes sure that there are robust governance practices, process reviews, and tools that minimize risk while also ensuring regulatory compliance.
Here are seven reasons how MLOps affect the output of data science related projects:
1. Automated retraining:
For keeping a model up to date, frequently retraining it is pivotal. If you are planning to manually check the results, then it leads to a lot of overheads. By using automated techniques, it is possible to discover model drift and data input drifts which reduces the associated costs as well. After implementing the model once, you can train and deploy newer models on a regular basis without worrying about odd model predictions.
2. Shorter release cycles:
MLOps successfully uses the concepts of CI/CD pipelines and end-to-end lifecycle management as it is strongly related to DevOps. With the help of these concepts, it allows teams to test frequently without having to indulge in extensive test documentation, thereby making deployment of new releases possible.
3. Enables versioning code:
Data science is heavily dependent on code and data, therefore replicating experiments and analyses becomes a problem in data science projects. MLOps solves this issue by versioning code and by also using the used data and model parameters.
4. Automates manual labour:
While there are some setup costs associated with MLOps, the best advantage of it is that it increases the productivity of your project by a huge margin. With MLOps, data scientists don’t have to just run analyses manually or do mundane and repeatable tasks. Instead, they can concentrate on building models.
5. Repeatable workflows:
MLOps support and optimize models to allow for automatic and streamlined changes. A model created through MLOps journeys its way towards processes which allows for data drift without six months of lag and email chains. It constantly measures and keeps the behaviors of the model in check while it's running.
6. Regulatory compliance:
MLOps’ greatest advantage is that it helps in following regulatory compliances. The guidelines surrounding machine learning are becoming increasingly strict, the GDPR requirements, for example. MLOps systems can reproduce models in compliance and accordance with the original standards. Even when the models and pipelines keep evolving, the systems continue to act in compliance with all the rules.
7. Reduction in bias:
MLOps helps your organization guard itself against certain development biases, some of which can lead to under-representation of audiences or put you in legal troubles. When you have an effective MLOps system, it ensures that certain features within a data report do not outweigh others. MLOps boosts the credibility and productivity of machine learning development.
To sum up:
For a successful MLOps program, many key roles are to be successfully utilized. If you are developing hundreds of models at scale, you would require software engineers and subject matter experts to make a dent. Apart from building robust ML solutions, communication with stakeholders, having clear expectations, employee upskilling, etc., are also pivotal in delivering value. MLOps-enabled practices help data scientists achieve these goals.
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