Machine learning has become a very popular technology in the last few years, and that is normal when you see all the improvements that have been made and all the benefits we have from having it. There is a lot to learn about how machine learning operations work and we are going to give you a quick guide so that you can keep up with all that is happening.
What are the components?
Model development is the basis for proper machine learning operations. Data is being collected from different sources and then used with the model. That data first needs to be free of any errors, so a data cleaning process is being initiated. Then it is analyzed so that we can understand its characteristics, patterns, and other aspects.
Not all the data features will be used for remodeling, but different techniques are used to get the most informative features out of the data. These features may need to be transformed so that they can be used for the modeling process. Techniques such as one-hot encoding, dimensionality reduction, and others are used.
The next step is to choose the right algorithm and that depends on the problem at hand, the characteristics of the data, and some other things. These models need to be trained, and that is done in two ways. One way is through controlled data and the other is through uncontrolled data. The controlled data is supervised learning, while the other one is the opposite. This training is done so that the model can be optimized. Trained models need to be serialized and that can be done with Python or some other model. These models will need to be tested before deployment.
The models will now have to go through a monitoring period and then the experts have to look at the feedback to see if it is functioning as it is supposed to. In the end, these models need to be documented with the whole process being described so that it is easier to use and share.
Tools and technologies
The tools and technologies used with MLOps are crucial for its lifecycle. They are used in almost every part of the making of the model and play a vital role in its production. If someone wants to have a successful deployment of ML models, then the proper MLOps tools & platforms need to be used. Python Libraries is one of the most used tools because of its extensive ecosystem of libraries. It has good tools for building platforms of machine learning models. Jupiter Notebooks is another famous tool that is usually used for experimenting, exploring, and building prototypes.
Jenkins is a software widely used for building, testing, and deploying software. What is great about it is that it can be used to create CI/CD pipelines for machine learning projects, it helps by automating model training, testing, and deployment. GitLab is a tool that provides CI/CD pipelines for YAML configuration files, which makes it easier to automate ML workflow.
Docker is a tool used for packaging applications into lightweight portable containers and it is a platform that provides a place where machine learning models can constantly be developed, tested, and produced. Kubernetes is an open-source container that provides the deployment, scaling, and management of applications that are put in these containers. This platform makes it easier to manage and deploy machine learning models at scale.
Different cloud software offers a range of services when it comes to the infrastructure of these models. Some of the services include storage, compute instances, and some tools that are specially designed for machine learning models.
For monitoring, one of the platforms that is most used is Prometheus. It collects all the necessary data, stores it, and enables it to be analyzed.
Key principles
Data validation, model training, and deployment are just some examples of processes that MLOps automates for the sake of accuracy and efficiency. To simplify the ML lifecycle, MLOps promotes teamwork across many positions, including data scientists, ML engineers, operations personnel, and more.
Constantly checking on live models to make sure they’re doing what they should and establishing procedures to retrain or replace them are both parts of the monitoring and continuous improvement framework.
To make sure models are fair, understandable, and in line with applicable regulations and laws, MLOps also takes care of the regulatory and governance parts of ML.
Data management
Something that presents the most challenges for machine learning is data management and there are many reasons why. The first thing that needs to be ensured is that the data collected is quality data. Raw data has many errors, and it is by no means so perfect. So something needs to be done to separate what is useful from what is not. Also, since the data comes from a variety of different sources, there may be a problem with how to integrate the data into one cohesive dataset. The volume of the data can be overwhelming so adequate infrastructure needs to be put in place so that everything can be done properly.
Regulatory compliance
Many industries are subject to regulatory compliance when it comes to handling data. Of course, this is normal because no one would want machine learning operations to go out of control of humans. Many times these models use private data and how that data is handled can be really problematic. Ensuring that the private data is secure is essential to the protection that the government gives to people. If anyone goes against these rules, they can be held accountable in court and can face fines as well as harsher sentences in some cases. Of course, many of these regulations stem from ethical concerns of the government and its people, and most people do not feel comfortable with their data being misused in any way or form so many laws in place safeguard their data.
MLOps is a field that strives to keep up with the dynamic data landscape. The features and quality of the training data greatly influence their performance. They need to be monitored continuously and updated frequently to make sure they function well all the time. These are all challenges that MLOps successfully handle, reflecting their importance and role in the digital world.