In my business, I have observed that MLOps are not just a gimmick, but provide true value to the regular business operations. With both AI and ML, I and my team have made various fruitful decisions for the company that has direct relations with these MLOps.
While MLOps consulting services help you at each step to optimize your AI infrastructure in the business premises, the framework should be efficient in model development, deployment, monitoring, and continuous improvement throughout the lifecycle.
Not only MLOps serve as a great option to pursue a career, but it might get tricky sometimes. An average MLOps engineer in the USA earns around $165,000 p.a. (Talent.com). Apart from that, it also brings some challenges along the way when integrating with AI.
Read the article to know more about them.
4 Major Challenges of AI Model Maintenance
In order to make complex AI programs sustain in their lifecycle, ML plays a critical role from their development to deployment. However, the journey of making everything work to its fullest takes effort.
Below are a few challenges that you, as a professional, will face during the process:
1 Data Drift
As the market and various external factors like user behavior and other trends are highly volatile, it may become a nightmare for the AI program. For instance, as the real-time price of currencies or the price of a share in the market keeps on changing, their concerned outputs will be different each time the user requests.
Likewise, AI programs need to have access to real-world data so that they can provide more precise or accurate outputs.
2 Model Decay
If you have any experience with an AI program, you must know that AI programs tend to degrade over time. Their operations become slow, less accurate, and less functional. New patterns and behaviors tend to make the program work harder with each output.
To reduce this decay as much as possible, organizations keep on introducing various retraining and monitoring programs.
3 Scalability and Performance Issues
The AI programs may sound all heaven, but that’s not the complete truth. While it’s true they make operations super smooth and easy, it is still challenging to scale them and keep their performance consistent over time.
Along with these issues, user experience is managed simultaneously so that the user does not face much difficulty.
4 Model Interpretability and Explainability
For an AI model, it is equally necessary to explain how it ended up to the conclusion, just like how it is expected from human employees. If you’re not able to understand how the decision-making process of an AI program works, it cannot be trusted yet.
This trust issue comes from both ethical and technical aspects. Hence, ML brings the ability for AI to explain its workings and how it concludes things.
INTERESTING STATISTICS This graph shows different use cases in which help is sought from AI and ML.
Address AI Model Maintenance Challenges with MLOps
Let’s take a recap of what we discussed earlier. Machine learning operations, or MLOps, are basically a set of practices done in order to manage, develop, deploy, and monitor the AI and ML models.
According to industry experts and professionals, they go with the path that is also followed by DevOps to integrate software development with IT operations. Keep reading the below points if you want to address the challenges:
Automated Monitoring and Alerts
Since data drift is one of the most common issues in the whole infra, MLOps keeps proper track of how things are going. While a slight drift can ruin the whole performance in the long term, automated monitoring and alerts take the responsibility to notify as soon as things seem to go wrong.
Continuous Integration and Continuous Deployment (CI/CD)
The most exciting thing about the CI/CD pipelines is that they can be used with complete automation. This way, not only operations are quick to finish, but they also become more accurate than if done manually.
If you are looking for some tools to seek help from, Kubeflow, GitLab CI, and Jenkins can be of help.
Scalability Solutions
As automating CI/CD deployment is one solution that can help in making the model scale to a bigger span, you can also leverage cloud resources and containerization to do the job.
Along with that, implementing A/B testing is another way you can go for. This way, any issue can also be addressed quickly to control the damage.
Version Control and Experiment Tracking
The data keeps on evolving even if it’s in use and can cause issues over time. Issues like different or inconsistent results each time may occur. As a solution, you can either create a new data set to get the best performance.
If there is not enough space to create new data each time, saving metadata of the given version and retrieving it according to the need can do the drill as well.
Seamless Integration and Collaboration
To solve this issue, try implementing a unified data pipeline, automated data integration, and using standardized data formats. For more details, you can anytime run research on the web.
Conclusion
Lastly, MLOps is not an easy task to perform, yet it can create a massive change to the technical infrastructure of the company. While integrating data insights with the IT infra is not as easy as it may sound, I shared some efficient and working solutions that are worth implementing.
At the end, if you find this writing helpful, share it with your team and colleagues as well.
