Machine learning deployment is the process of deploying a machine learning model in a live environment. The model can be deployed across a range of different environments and will often be integrated with apps through an API. Deployment is a key step in an organisation gaining operational value from machine learning.
Machine learning models will usually be developed in an offline or local environment, so will need to be deployed to be used with live data. A data scientist may create many different models, some of which never make it to the deployment stage. Developing these models can be very resource intensive. Deployment is the final step for an organisation to start generating a return on investment for the organisation.
However, deployment from a local environment to a real-world application can be complex. Models may need specific infrastructure and will need to be closely monitored to ensure ongoing effectiveness. For this reason, machine learning deployment must be properly managed so it’s efficient and streamlined.
This guide explores the basic steps required for machine learning deployment in a containerised environment, the challenges organisations may face, and the tools available to streamline the process.
How to deploy machine learning models
Machine learning deployment can be a complex task and will differ depending on the system environment and type of machine learning model. Each organisation will likely have existing DevOps processes that may need to be adapted for machine learning deployment. However, the general deployment process for machine learning models deployed to a containerised environment will consist of four broad steps.
The four steps to machine learning deployment include:
- Develop and create a model in a training environment.
- Test and clean the code ready for deployment.
- Prepare for container deployment.
- Plan for continuous monitoring and maintenance after machine learning deployment.
Create the machine learning model in a training environment
Data scientists will often create and develop many different machine learning models, of which only a few will make it into the deployment phase. Models will usually be built in a local or offline environment, fed by training data. There are different types of machine learning processes for developing different models. These will differ depending on the task the algorithm is being trained to complete. Examples include supervised machine learning in which a model is trained on labelled datasets or unsupervised machine learning where the algorithm identifies patterns and trends in data.
Organisations may use machine learning models for a range of reasons. Examples include streamlining monotonous administrative tasks, fine-tuning marketing campaigns, driving system efficiency, or completing the initial stages of research and development. A popular use is the categorisation and segmentation of raw data into defined groups. Once the model is trained and performing to a given accuracy on training data, it is ready to be prepared for deployment.
Test and clean code ready for deployment
The next step is to check if the code is of sufficient quality to be deployed. This is to ensure the model functions in a new live environment, but also so other members of the organisation can understand the model’s creation process. The model is likely to have been developed in an offline environment by a data scientist. So, for deployment in a live setting the code will need to be scrutinised and streamline where possible.
Accurately explaining the results of a model is a key part of the machine learning oversight process. Clarity around development is needed for the results and predictions to be accepted in a business setting. For this reason, a clear explanatory document or ‘read me’ file should be produced.
There are three simple steps to prepare for deployment at this stage:
- Create a ‘read me’ file to explain the model in detail ready for deployment by the development team.
- Clean and scrutinise the code and functions and ensure clear naming conventions using a style guide.
- Test the code to check if the model functions as expected.
Prepare the model for container deployment
Containerisation is a powerful tool in machine learning deployment. Containers are the perfect environment for machine learning deployment and can be described as a kind of operating system visualisation. It’s a popular environment for machine learning deployment and development because containers make scaling easy. Containerised code also makes updating or deploying distinct areas of the model straightforward. This lowers the risk of downtime for the whole model and makes maintenance more efficient.
The containers contain all elements needed for the machine learning code to function, ensuring a consistent environment. Numerous containers will often make up machine learning model architecture. Yet, as each container is deployed in isolation from the wider operating system and infrastructure, it can draw resources from a range of settings including local and cloud systems. Container orchestration platforms like Kubernetes help with the automation of container management such as monitoring, scheduling, and scaling.
Beyond machine learning deployment
Successful machine learning deployment is more than just ensuring the model is initially functioning in a live setting. Ongoing governance is needed to ensure the model is on track and working effectively and efficiently. Beyond the development of machine learning models, establishing the processes to monitor and deploy the model can be a challenge. However, it’s a vital part of the ongoing success of machine learning deployment, and models can be kept optimised to avoid data drift or outliers.
Once the processes are planned and in place to monitor the machine learning model, data drift and emerging inefficiencies can be detected and resolved. Some models can also be regularly retrained with new data to avoid the model drifting too far from the live data. Considering the model after deployment means machine learning will be effective in an organisation for the long term.
Challenges for machine learning deployment
The training and development of machine learning models is usually resource-intensive and will often be the focus of an organisation. The process of machine learning deployment is also a complex task and requires a high degree of planning to be effective.
Taking a model developed in an offline environment and deploying it in a live environment will always bring unique risks and challenges. A major challenge is bridging the gap between data scientists who developed the model and the developers that will deploy the model. Skillsets and expertise may not overlap in these distinct areas, so efficient workflow management is vital.
Machine learning deployment can be a challenge for many organisations, especially if infrastructure must be built for deployment. Considerations around scaling the model to meet capacity add another layer of complexity. The effectiveness of the model itself is also a key challenge. Ensuring results are accurate with no bias can be difficult. After machine learning deployment, the model should be continuously tested and monitored to drive improvements and continuous optimisation.
The main challenges for machine learning deployment include:
- A lack of communication between the development team and data scientists causing inefficiencies in the deployment process.
- Ensuring the right infrastructure and environment is in place for machine learning deployment.
- The ongoing monitoring of model accuracy and efficiency in a real-world setting can be difficult but is vital to achieving optimisation.
- Scaling machine learning models from training environment to real-world data, especially when capacity needs to be elastic.
- Explaining predictions and results from a model so that the algorithm is trusted within the organisation.
Products for streamlining machine learning deployment
Planning and executing machine learning deployment can often be a complex task. Models need to be managed and monitored to ensure ongoing functionality, and initial deployment must be expertly planned for peak efficiency. Products like Seldon Deploy provide all the elements for a successful machine learning deployment, as well as the insight tools needed for ongoing maintenance.
The platform is language-agnostic, so it is prepared for any model developed by a development team. It can easily integrate deployed machine learning models with other apps through API connections. It’s a platform for collaboration between data scientists and the development team, helping to simplify the deployment process.
Seldon Deploy features for machine learning deployment include:
- Workflow management tools to test and deploy models and make planning more straightforward.
- Integration with Seldon Core, a platform for containerised machine learning deployment using Kubernetes. It converts machine learning models in a range of languages ready for containerised deployment.
- Accessible analytics dashboards to monitor and visualise the ongoing health of the model including monitoring data drift and detecting anomalies
- Innate scalability to help organisations expand to meet varying levels of capacity, avoiding the risk of downtime.
- The ability to be installed across different local or cloud systems to fit the organisation’s current system architecture.
Deploying machine learning models in your organisation
Seldon moves machine learning from POC to production to scale, reducing time-to-value so models can get to work up to 85% quicker. In this rapidly changing environment, Seldon can give you the edge you need to supercharge your performance.
With Seldon Deploy, your business can efficiently manage and monitor machine learning, minimise risk, and understand how machine learning models impact decisions and business processes. Meaning you know your team has done its due diligence in creating a more equitable system while boosting performance.
Deploy machine learning in your organisations effectively and efficiently. Talk to our team about machine learning solutions today.