Introduction To MLOps

In this article, we’ll get introduced to MLOps. We’ll learn what MLOps is, the Data Science Lifecycle, the Machine Learning Lifecycle, multiple challenges we face with Machine Learning and then get to understand the importance of MLOps. Finally, we’ll make a brief comparison of MLOps to DevOps and learn about various principles of MLOps along with specific benefits and business values of MLOps for businesses and organizations.

MLOps

Machine Learning Operation shortly known as MLOps focuses on empowering data scientists and application developers to help bring ML models to production. The MLOps makes it faster for experimentation and in the development of machine learning models. Moreover, faster deployment of models into production can be made. Furthermore, the quality assurance and end-to-end lineage is possible with MLOps.

To fully understand MLOps and its importance, we must first understand where in fits in the Data Science Lifecycle and Machine Learning lifecycle.

Data Science Lifecycle

The process of Data Science mainly starts with understanding the main business and then moves on to the Data acquisition process. From Data Cleaning, Data preparation like exploration and wrangling is all done here. Thereafter, Modeling is done where feature engineering, model training, and evaluation are done. After this, the models are deployed at web services for an intelligent application. Scoring Performance and Monitoring are done at this stage. And finally, the customer is satisfied with the output that was being looked out for.

Source: Microsoft

Machine Learning Lifecycle

Similar to Data Science Lifecycle, the machine learning lifecycle also consists of data preparation. However, ML mainly focuses on model building, training, validation, and deployment. The models once developed are trained and tested. Then we package these models and validate our output. The data must have been initially divided at around 70 –30% for training and validation. Next, we repeat this process unless we obtain the optimum output, and then once we have it, we deploy our model. Finally, we deploy our model and retrain our model time and again with the new data that we’ll obtain in time.

Source: Microsoft

Challenges with Machine Learning

There are numerous challenges that comes in the process of machine learning and data science works. These range from human issues to technical challenges.

Cross Team Alignment

When multiple teams are working on the same project, for different segments of the project, it is assured that there will be some conflict no matter how well tasks are executed.

Standard and Repeatable Process

A lot of tasks are pretty standard and mundane so much so that the process is repeated throughout. During Machine Learning inclusion, there arises a situation of a loop of process. From overtraining to numerous other issues, a lot needs to be taken care of.

Resources

Machine Learning is a hardware intensive task and resources are depleted in a matter of no time. Thus, it's important to make sure budgeting is proper in case of Cloud Computing usage for ML.

Auditability

Machine Learning works face an issue of auditability. ML itself is functioning on a black box. Auditing the work is a nightmare where at times the process that the system takes within training itself is in abstraction for engineers and data scientists themselves. Moreover, in cases of Deep Learning and Neural Network works, this complexity and challenge is more prevalent.

Explainability

Similar to Auditability, being able to explain Machine Learning process at times before an impossible task as we are mostly working on a black box where the machine is learning itself.

Why do we need MLOps?

In order to solve the numerous challenges, we face in Machine Learning, MLOps comes to the rescue. MLOps helps us enable continuous experimentation and comparison against a baseline model and monitor the incoming data to detect data drift. It also helps us trigger model retraining and set-up a rollback just in case. Lastly, creating reusable data pipelines which can be applied for both training and scoring is possible through MLOps.

Comparison of MLOps and DevOps

To MLOps, we can compare it with DevOps. This will help us get the bigger picture. While comparing with DevOps, we can see how exploration always precedes development and operations. More so in ML, data exploration is vital. Today, when we listen to Andrew Ng, he never steps back to discuss about the importance of Data Centric Model rather than Model-centric models. Data Science lifecycle truly requires an adaptive way of working as data quality requirements and data availability constrain the work environment. Thus, it would not be wrong to say, that Machine Learning requires a greater operational effort. Hence, Machine Learning teams requires specialists and domain experts in order to deal with the unknown at most times.

If you want to learn more about MLOps, Watch this video.

Seven Principles for MLOps

When we focus on MLOps, there has been a wide acclamation of the seven principles in MLOps. Here are these listen down. It is key that these principles are kept in mind while focusing on MLOps.

Version Control code, data, and experimentation outputs
Use multiple environments
Machine infrastructure and configuration-as-code
Track and machine Learning experiments
Test Code, Validate, Data Integrity, Model Quality
Machine Learning continuous integration and delivery
Monitor services, models, and data

Besides, seven general principles, MLOps can also be viewed down narrowly for Business value and specific organizational scale levels and how institutions can benefit from it.

Business Value of MLOps

Model Reproducibility

Model once and reuse it. MLOps makes it beneficial for businesses to reuse Models once it has been developed.

Model Validation

Validating Model has been made easier with MLOps. With the structured process, businesses atlas always benefit from a managed process with lesser risk of failure as the outcome of validation can be tested, retrained, and reworked over the lifecycle again to validate for the required output.

Model Deployment

The Deployment of the models and the Machine Learning lifecycle has been made extremely easy with MLOps.

Model Retraining

When Models do not provide output as required, it is essential to retrain it with probably different set of data of different algorithms. At times, it is also important to roll back to probably prior training levels to save from overfitting by retraining. MLOps makes sure, this all is possible without a hitch.

MLOps at Organization Scale

Organization benefits a lot from MLOps. For scaling ML applications, MLOps have importance like no other. Firstly, MLOps helps us standardize on repeatable architectural patterns and facilitate cross-team collaboration and sharing. This makes sure, the architecture of the system is perfect along with collaboration between teams seamless. Moreover, project templates can be developed to reuse over time. Besides, Centralized Data Management can be setup and utilities can be shared which is only possible due to MLOps.

Conclusion

Thus, in this article, we got a detailed introduction to MLOps. We learned what MLOps is, the Data Science Lifecycle, the Machine Learning Lifecycle, the multiple challenges with Machine Learning, and then understood the importance of MLOps. Furthermore, we compared MLOps to DevOps and learned about various principles of MLOps, specific benefits of MLOps for businesses, and scaling organizational works.