What is MLOps (Machine Learning Operations)? Why Do You Need MLOps for Machine Learning and Deep Learning Projects?

What is MLOps (Machine Learning Operations)?

According to techjury, people created 2.5 quintillion bytes of data every day in 2021, presenting an opportunity for data scientists to explore and experiment with numerous theories and develop different ML(Machine Learning) models.

With this opportunity, however, there lies the challenge of acquiring and cleaning the data, setting up versioning for model training, setting up monitoring pipelines, and scaling ML operations to the needs of businesses. To tackle these issues, MLOps can be very helpful.

MLOps was born at the intersection of Machine Learning, DevOps, and Data Engineering. The concept is similar to DevOps; however, its execution is different. ML systems are more experimental, having significantly complex to-build components.

MLOps is a set of practices for better communication and collaboration between data scientists and operations professionals. Applications of these practices enhance quality, simplify the management process, and automate the deployment of ML models in large-scale production environments.

What are the challenges associated with MLOps?

As per a report by deeplearning.ai, only 22% of companies using ML have been able to deploy a model successfully. Following are some of the root causes of this problem.

In software development, DevOps has made it possible to ship software to production very quickly and keep it running reliably. DevOps uses tools, automation, and workflows to let developers focus on the problem to be solved. Machine Learning, however, is quite different from software development. It is not just a code but code with data.

While coding can be done in a developer-friendly environment, the data from the real world is constantly changing. This disconnect between the code and the data causes several challenges like slow deployment, performance reduction, lack of reproducibility, etc., that need to be solved before trying to put a Machine Learning model into production.

What is the need for MLOps?

Deployment

If models are not deployed or not deployed at the speed or scale to fulfill the needs of businesses, then the company cannot reap the full benefits of AI. MLOps deployment helps to resolve the following issues:

• Multiple languages or multiple teams are being used to build the model.
• Rewriting of models in different languages for deployment.
• Backlog of models waiting to be deployed.
• A lot of time being lost troubleshooting the model during the deployment process.
• No standardized process for elevating models from development to production.

Monitoring

Evaluating ML models manually is very cumbersome and diverts resources from model development. MLOps monitoring helps to resolve the following issues:

• No monitoring has been performed on models already in production.
• No consistent way to monitor the models.
• No centralized way to view model performance across the organization.

Lifecycle Management

Organizations can only update models occasionally, even if model decay is identified because the process is resource intensive. MLOps can help resolve the following issues:

• Models are not updated in production.
• Data Scientists are not aware of model decay after the initial deployment.
• Data Scientists are involved in production model updates.

Model Governance

Businesses need audit processes which are time-consuming and costly, to ensure compliance. MLOps model governance can help with the following:

• Model Audit trails.
• Model upgrade approval workflow.
• Traceable model results.

What are the best practices for MLOps teams?

Dataset Validation

To generate value and provide better results, validating the dataset along with training the model is necessary. Verifying the labels of the dataset can be a tedious and time-consuming task; however, it will ensure that the model’s performance stays within the original predictions. In the long run, it is crucial to detect errors and is directly responsible for the performance of ML models.

Collaborative Culture

Creative ideas generally arise from spontaneous discussions and non-linear workflows. Lack of collaboration among the team members leads to inefficiencies, duplication of efforts, and low ramp-up times. Therefore, it is necessary to practice cooperation and communication at every step to ensure that the project is completed at the earliest.

Application Monitoring

ML models will fail to make accurate predictions if the input dataset is error-prone. Monitoring pipelines is, therefore, an important step to incorporate while adopting MLOps. Automating continuous monitoring can ensure that degradation in the model’s performance is caught quickly and remediated. Apart from keeping track of the model’s performance, operational metrics like latency, response time, and downtime should also be monitored.

Reproducibility

Reproducibility in ML requires tracking the code, data algorithm, and environment configuration. For ease of reproducibility, there needs to be a central repository that keeps track of all the artifacts and their versions. It is necessary to develop a replica of the model that will deliver the same results. The inability to reproduce models makes it difficult for data scientists to show how the model provided output and for validation teams to recreate the results. It also makes compliance with regulatory requirements difficult.

Experiment Tracking

Tracking and versioning different combinations of scripts, datasets, model architectures, hyperparameter values, and various experiments, including their results, is an essential requirement to keep track of what is happening in the model. This will ensure that the best model is used in production and help build reproducible ML models.

Conclusion

MLOps makes it possible to efficiently get models into production and ensure they continue functioning reliably. It helps organizations streamline and automate their processes throughout the data science lifecycle and solve scalability issues regarding large and complex datasets.

Improved productivity, reliability, and faster deployment of ML models are just some of the many benefits of adopting MLOps. Implementing the above best practices will help organizations unlock and reap the benefits of increased ROI from ML models.

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References:

• https://neptune.ai/blog/mlops
• https://builtin.com/machine-learning/mlops
• https://www.datarobot.com/lp/why-you-need-mlops/#:~:text=Machine%20Learning%20Operations%20(MLOps)%20allows,lifecycle%20through%20automation%20and%20scalability
• https://www.dominodatalab.com/blog/mlops-best-practices-for-large-organizations
• https://medium.com/coriers/mlops-best-practices-59c0348e7125
• https://towardsdatascience.com/mlops-practices-for-data-scientists-dbb01be45dd8
• https://blog.dataiku.com/mlops-in-manufacturing