The purpose of developing a machine learning model is to address a problem and a machine learning model can only manage a problem if it is in operation and frequently used.
Unfortunately, most models rarely make it to process, and even if they do, the implementation takes far longer than it should.
Even models that have been successfully implemented will need domain-specific maintenance, which might pose new technical and operational issues. The truth is that machine learning models are software. Any software deployment and maintenance effort are complex but machine learning adds to the complexity.
MLOps is a field that arose as a result of these demands. MLOps simplifies the process of creating and deploying machine learning models. However, when choosing your ML stack and tools, there are undoubtedly acceptable and frequent technical concerns to bear in mind.
We’ll go through some of the strategies and methodologies that will help you bring machine learning models into operation
Putting Model in Production
Many teams begin machine learning initiatives without a business plan, which often results in significant issues when it comes time to deploy. Model creation is both costly and time-consuming. ML model deployment is the process of taking trained models into operations and making predictions available to other users. It is quite different from regular machine learning tasks such as model selection and model monitoring.
So, unless you’re performing pure research, you shouldn’t invest in the ML project until you intend to use it in production.
It would be best to consider a few critical areas for putting the machine learning model into production.
- Data
- Tool and framework
- Model Registry
- Automation
- Monitoring
1. Data
No one can utilize a machine learning model if it has no data to work with. You’ll have data sets for training, assessment, validation, and perhaps prediction. Data can be kept on-premises, on the cloud, or in a combination of the two. It makes it logical to keep your data in the exact location where the model will be trained and the outcomes are given.
An on-premise learning algorithm will be optimal for on-premise data, mainly if the data is vast. Data distributed across multiple storage systems such as GCS, AWS S3, or Azure storage should be paired with a cloud.
The volume of your data is also essential. If your dataset is vast, you’ll require extra computational resources for preprocessing and model refinement phases. If you’re running locally, this means you’ll need to plan for additional computing or set up auto-scaling in a virtual environment from the outset.
It’s crucial to collect more information about data retrieval, as this will help you focus on the best way to deploy your machine learning project.
2. Tools and Framework
Your model will not train, operate, or deploy by itself. You’ll require tools and resources, as well as hardware and software, to enable you to deploy ML models efficiently. You should explore and evaluate results for different tools that accomplish the same work to assist you in picking the best tool for the job.
A framework or tool is practical if it best uses available resources such as storage, processing, and time. Since framework and tools directly impact project performance, dependability, and durability, it’s critical to consider how efficient they are.
Examples are frameworks for training models like Scikit-Learn, programming languages such as C, Python, Java, and C#, and even cloud infrastructures such as AWS and Azure.
Read: How Your Organization Can Benefit From Using DevOps Models
3. Model Registry
A model registry allows data scientists to save models as they are trained, making the testing process more accessible. Models that are fully trained as part of deployment should be retained in the same registry compared to previous versions.
Models should be tracked by name/project and a version number in a suitable model registry. When a model is registered, the information from the training job should also be included.
When a model in operation delivers incorrect results, registries simplify identifying which model generates the problem and, if required, scale back to the last variant. Without a model registry, you potentially lose track of the prior model, rendering backup difficult. Model registries also assist in auditing model predictions.
Model registry exists in various forms and sizes to suit the demands of multiple businesses. There are a few types of common considerations:
- Sagemaker Model Registry or Azure Model Registry
- Open-source registries, such as MLflow, allow customization across a wide range of contexts and technology stacks.
4. Automation
Building successful services requires automation, and this notion applies to machine learning applications as well. Any manual procedure has the potential to introduce mistakes and waste resources. Errors like this may be avoided with automation. Developers will devote less time to deployments. As a result, it will allow focusing on more intriguing challenges.
Continuous integration and delivery are used to describe the automation of builds and deploys. CI/CD pipelines are designed to give incremental, safe software development increments. The CI/CD pattern decreases the time and effort required for deployments.
It also eliminates the chance of deployments causing problems because each published increment is tiny and easy to understand. If a problem emerges, it’s simple to undo the update and fix the problem, then redesign the problem feature to correct the problem.
5. Monitoring of the Results
Because machine learning systems rely on data, monitoring them necessitates watching the input characteristics and the output predictions. It also entails ensuring that you have a firm grasp on which model version converted the input to output.
Whether predictions are made one at a time or in batches, each one should be given its own unique ID. If the prediction request comes from a third-party system, this permits the forecast to be connected to the request.
The prediction ID should also be connected to the model’s current version, regardless of how that model is designated in the model registry. The model, query, and prediction IDs should all be written down in a log entry and, if required, placed into a database. Frequently monitoring the results helps to identify errors and evaluate its performance.
Read: Understanding The Fields And Types Of Robots
Final Thoughts
Developing and deploying machine learning models may be difficult. However, in today’s data-driven world, avoiding these difficulties is possible. If all of the considerations are checked before starting a project, effectively bringing an ML model into production should not be difficult. This is critical in every machine learning project you do, and it should be prioritized!