Is Model Deployment a Piece of Cake?
Most firms that engage in machine learning development never make it to the production bulwarks. This demonstrates how complex ML model deployment may be. The training/development scene is a dramatic contrast to the production environment. Discrepancies are sure to impact model performance. Here's a rundown of some of the most typical production-related issues.
Complexity of code
The code becomes more complicated as you create your model. Using unoptimized, poorly constructed code for ML teams operating in silos is particularly dangerous. As a result, the ML models deployed to production would not be able to run in the real world.
Models with a lack of transparency
The siloed business suffers from inconsistency due to diversity in the toolkits utilized during model creation. Model quality suffers due to the opacity produced by various toolkits and platforms employed, and overall visibility suffers. Consequently, keeping track of model performance measures becomes more difficult.
When there is a lack of computational infrastructure, deploying models at scale becomes increasingly difficult. Models take up large volumes of data, massive codes, configurations, and intensive compute resources.
MLOps to the Rescue
MLOps is a more recent domain that focuses on amalgamating machine learning and DevOps to solve real-world business problems. MLOps makes the process of model deployment and model maintenance in production go smoothly. Using MLOps enhances the quality of ML models while automating ML pipelines for flexible upscaling. MLOps uses an agile approach to operationalize production pipelines.
Benefits of MLOps in Production
Multiple teams from various departments, such as data, DevOps, and software, are involved in the ML lifecycle. Effective communication between teams was difficult to imagine because they worked on each project simultaneously. With the introduction of MLOps, a backbone for the holistic platform was created, allowing teams to focus on only the lifecycle elements in which they excel and then integrate their inputs into a collaborative output before deployment.
The technology makes it easier to eliminate bottlenecks in production by allowing each team to monitor their own workflows rather than review the entire lifecycle. These dynamic communication pipelines aid in the adaptation of dynamic models to variations. This is especially useful for sensitive models that are siloed in production.
Reproducible and repeatable workflows and models
Keeping up with the variations in each new iteration of a model released into production may be a tremendous problem. MLOps aids in the streamlining of production pipeline variations and the development of resilient infrastructure to deal with recurring processes. These offer an extensive administration of replicable model procedures for consistent supply by assisting in automated updates in the variances with each passing iteration.
By acquiring insights on traces, logs, and metrics, the workflows may be used to conduct a thorough, brick-by-brick evaluation of the model's performance. These logs and performance metrics can be tracked using advanced model registries and data registries.
Ease in deployment
All operations boil down to comfort at the end of the day. Traditional machine learning deployment methods were far from comfortable in the last decade. Model deployment is further complicated by data inconsistencies, constantly changing parameters, and a dynamic input structure.
MLOps creates tools that make deploying high-precision models quickly and efficiently. Within distributed clusters of CPUs and GPUs, MLOps delivers both cloud-based and on-premise computing capabilities. This allows for quick and effective model auto-scaling while also considering the organization's budget and high-volume expenses.
MLOps helps with meticulous model shipping and migration to production, as well as quality assurance and preservation across the pipelines.
Effective resource management and control
The dynamic status of the production ecosystem is one of the main issues faced by ML teams when it comes to deployment. Furthermore, inherent bias in the data, as well as in the model's architecture, will inevitably favor certain attributes over others. MLOps leverages CI/CD pipelines to automate model training and deployment, resulting in significant outcomes that can be integrated with other previously published apps.
Apart from that, MLOps is quite beneficial for auditing because it keeps track of model version history and overall performance. By employing uniform distribution metrics, MLOps also aid in the reduction of bias. ML teams can set computer resource restrictions to stay within their project budgets while still following legal and privacy regulations. This ensures that model performance is improved by automatic experiment tracing.
How to Operationalize Your Models?
As is evident now, MLOps have the potential to transmogrify the challenges often induced in the ML lifecycle. Model Operationalization, from a higher perspective, comprises four steps.
Data collection and curation
Data acquisition that will help solve the problem from a business standpoint is the first phase in the MLOps lifecycle. IoT devices, mainframes, and relational database management systems are highlighted as reliable data sources with the potential to reduce business complexity.
On the other hand, unstructured and unprocessed data would not add to the model's resilience and robustness. As a result, the data pool must be thoroughly cleansed for data outliers and processed into conventional formats. This ensures that the model's quality is maintained and improved.
An efficient AI observability platform like Censius could be introduced to track data integrity and quality.
Building your models
The next step in the machine learning lifecycle is to use the data to develop models.
The ML models can be created using a variety of programming languages, including Python and R. There are also a plethora of toolkits available for data scientists to utilize in the development and training of their models. A model is exhaustively engineered only after identifying a good algorithm for building predictive models.
It's also worth noting that builds should be developed in such a way that they'll be helpful even in the production environment. Because the dynamic undulations in production are difficult to reproduce in the training environment, ML teams must exercise caution while engineering features so that they can be migrated to production in fractions of a second.
Deployment and management
After you've trained your model to make predictions, it's time to dispatch it to production. This is where the model would get its first taste of real-world problems' dynamics.
Models are moved from the development landscape to the production backdrop via data pipelines to link them with business solutions. MLOps automates the model training/retraining and archiving artifacts processes using CI/CD (continuous integration/continuous delivery) pipelines. Version control and the ability to track data changes are critical in MLOps.
In addition to model deployment, management systems are offered to manage version control, metrics, traces, and logs from a centralized repository. MLOps is a one-stop solution for logging and tabulating model performance.
It's vital to keep track of production undulations if you want to keep the quality of your models high. Adding observability to ML pipelines allows holistic model health monitoring for real-world inconsistencies and initiates a troubleshooting cycle to resolve them.
Model Monitoring is a subset of MLOps, which aims to automate pipelines for quick troubleshooting and outlier/drift identification. When ML teams suffer from automated model monitoring, introducing an AI Observability Platform like Censius could help.
Censius provides robust data, model prediction, and bias drift monitoring within a single flexible platform. Clients can use Censius to proactively monitor the performance of numerous models and correct errors from a business standpoint. MLOps become much more accessible as a result of this.
Best Practices of Operationalizing Models
Organizations' investment strategies should be in line with their budgets. Organizations also create business strategies that are in line with their financial deliverables. The successful use of MLOps necessitates the identification of the fundamental issue as well as a planned calculation of ROIs per investment made.
Security is not optional
MLOps enables quick model execution and deployment in a rapidly changing production environment. Integrating model pipelines with current apps that have been made available for universal access is sure to cause security issues. As a result, security must be prioritized when implementing any ML model.
Model development is not the same as software development
Despite inherent similarities in the product lifecycle of both ML models are software applications, it is worth noting that model development is not the same as software development. Several businesses fail to draw the line between these practices and end up with intensive computing and overhead costs, lengthening deadlines, and gaps in the analytics workflow. ML teams deploying the models must have on-the-fly knowledge about MLOps tools to yield better outcomes.
MLOps entails practices that seek to deploy machine learning models in production and ensure their successful operation through exhaustive monitoring and troubleshooting. MLOps seeks to foster enhanced collaboration, power reproducible and repetitive workflows, simplify deployment and exercise complete control and resource management for the smoother functioning of the model.
ML Models in production could be operationalized by data curation, strategic building, deployment, monitoring, and troubleshooting. Strategic investments, secure policies, and model-specific development and deployment paradigms help operationalize model workflows successfully.