Over the past few years, the data science industry grew rapidly with the influx of data science innovations. This event signals the start of an era where machine learning (ML) and artificial intelligence (AI) are becoming commonplace in different industries.
More and more businesses are starting to take their fair share of AI/ML technology, and just recently, around half of the businesses worldwide have participated in this venture. But after numerous enterprises failed to integrate ML/AI into their applications and systems, the rest are starting to wonder, is this venture worth the investment?
As if to give hope to the data science industry, DevOps experts came up with MLOps, and the results have been mostly positives. However, most enterprises are still skeptical and full of questions like ‘What do we stand to gain from MLOps?’ But the real question is: ‘What difference would MLOps make if ML models are already difficult enough as is?’
That’s what this article is for—to show you the uses of MLOps in a business that are otherwise unfeasible when using regular ML models. Let’s start with perhaps the most significant advantage of using MLOps on your business management systems.
Quicker Deployment
The main problem with machine learning models is deployment. Putting ML models into production involves a complex process that requires a whole team. There are typically seven stages of developing machine learning models:
- Data gathering
- Preparation of data
- Model selection
- Training
- Evaluation
- Fine-tuning
- Prediction
Each stage does not happen overnight either, so a lot of time and effort, not to mention money, must be invested before perfecting and integrating an ML model into business applications.
MLOps aims to make the deployment of machine learning models a lot easier by using DevOps practices. With a smoother deployment, businesses can invest smaller and reap the same benefits as what they would from exhausting their funds when using the old approach. This system also gives teams more time to work on the actual science of ML models.
Adapt to Real-Time Data
Sudden changes are expected in the business world. However, these changes mean that machine learning models must adapt as soon as possible before it wreaks havoc on management systems due to its inconsistencies. ML models of the past are incapable of quickly adapting to real-time data, resulting in unsatisfactory results.
Take COVID-19, for example. Due to the sudden changes in the business industry, there were many changes in data, resulting in abnormal activities within ML models, leaving teams of data experts to grasping at straws.
Detecting anomalies is one of the capabilities of MLOps. Depending on the platform the team uses, sudden changes can be monitored with DevOps technology. A great example of a platform with monitoring capabilities is cnvrg.io. With MLOps, teams can have a smoother workflow, allowing them to fix any issue within business management systems.
Effective Communication
Effective Communication Most businesses have different teams working on the development and deployment of machine learning models. Each team has its own responsibilities, but their tasks are somewhat interconnected. Some examples of common teams tasked with ML models include:
- Data science teams (scientists and engineers)
- Operations team
- Machine learning development teams
If one of these teams fail, the whole project will fail. Therefore, communication between these teams is crucial. But due to the large amounts of data, it’s difficult to communicate the progress of each team with one another. And that’s where MLOps comes in.
One of the advantages of MLOps over the regular machine learning approach is its organization. While the old approach results in a messy development, MLOps allows teams to keep their work organized and documented, making it easier to share their progress with other teams.
This will lead to an increase in productivity and a higher chance of a breakthrough. However, implementing MLOps would also require the business to hire another set of experts to form a team that would consist of DevOps engineers.
Accurate Experimentation
Integrating ML/AI into business management systems and applications consists of several stages. One particular stage where most teams fail to excel is the experimental stage, wherein regular testing is done to refine the ML/AI models.
The experimental stage, or what data science experts call the “Training” stage is one of the most arduous stages of developing and deploying ML models. Training machine learning models means exactly how it sounds.
Just like how a father would train his child to play catch through repeated activities, ML teams must also put the model to a test repeatedly in the same scenario. After taking into account the results of previous deployments, the teams must prepare the model for the next test. At its core, the training stage mainly involves the following tasks:
- Creation of the initial algorithm
- Testing
- Updating values
After going through these three tasks repeatedly, no matter how skilled the teams are, mistakes are inevitable, and one mistake can lead to a disaster.
By using MLOps, data from ML/AI models are automatically cleared, and the ideal state is achieved without extra effort from the data science experts. Consequently, teams of data science experts can achieve optimal results with the experimental stage, further reducing the time and money needed to be invested by the business in the venture.
Improved Data Security
As mentioned above, models must be created, validated, and deployed repeatedly. Hence, most teams don’t have enough time for other vital tasks, such as monitoring the security of business data. This lack of security may lead to confidential data leaking to the public.
Since MLOps is capable of managing ML models automatically, using such a system will provide teams extra time to work on security, thus preventing potential risks concerning data security.
Final Words
Developing machine learning models is one of the best innovations within the data science industry. The only problem was its complexity, which results in a higher chance of failure than success. Such uncertainty is not appealing to businesses. Now that there’s a way to utilize ML models easily, many businesses will try to involve themselves in MLOps.
Founder Dinis Guarda
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