Understanding MLOps challenges and solutions play a key role for successful machine learning projects. Software Engineering is all about building, testing, deploying, and monitoring applications. While Machine Learning seems similar to software engineering, it is much more iterative in nature involving data engineers defining and refining data, data scientists building, and experimenting models, and mlops team training, deploying, and monitoring models with observability.
Machine learning started to gain more traction in the 1990s with the onset of a data-based approach for solving problems. With the availability of GPUs, and various cloud companies offering hardware resources at a reasonable price have fueled the growth of machine learning. MLOps is a relatively new discipline formed after understanding the importance of collaboration among data scientists and the ops team needed to operationalize machine learning models.
In the early stages, there were very few machine learning models with limited datasets making it affordable to infer models from a single on-prem server. Hence Data Scientists used to deploy and monitor models by themselves without any trouble.
But as the data grew exponentially along with the number of models, deploying and monitoring models became cumbersome.
Machine Learning Project Lifecycle
The lifecycle of the Machine Learning project involves different phases starting from the business team defining goals, setting metrics to Data Scientists building models, and the MLOps team deploying and monitoring machine learning models in production.
Business Team: To start off, the business team identifies the machine learning use case, defines the goals for the project and the metrics to measure its success.
Data Engineers: Data collection, analysis, governance, security, and storage are an integral part of any machine learning projects as most of the time is spent by the data engineers processing the data.
Data Scientists: Once the data engineers are ready with the feature store, the experimentation phase kicks off. Model building involves Data Scientists implementing and experimenting their models with different machine learning frameworks and algorithms by fine-tuning the hyperparameters.
MLOps: After the model is ready for operationalization, either Data Scientist or MLOps team starts with the deployment and monitoring of machine learning models in production.
Each phase in the machine learning project lifecycle has its own challenges. The process of building models has eased over the years with the availability of different machine learning libraries, frameworks along with AutoML.
Even though there are mlops solutions from different cloud and machine learning vendors, moving models from POC to production is still a challenge.
Not all data scientists have expertise in Kubernetes
Containers started off as the Linux kernel process for cgroups and namespaces. Docker in 2013 popularized containers simplifying the deployment process. Later Kubernetes which was released in 2015, became the standard for container orchestration.
The iterative nature of machine learning makes it harder to replicate the environments between development and production. Since the advancement of Kubernetes, it is easier to replicate environments as it encapsulates both the hardware and software needed to run the model into deployable modules.
Any enterprise irrespective of its size needs to be an expert in Kubernetes to manage the model efficiently. Either companies need to allocate budget for the mlops team or data scientists need to learn and master Kubernetes to automate the deployment process.
Nightmare managing Deployment Pipelines
Deploying Machine Learning models is not that straightforward as it involves not just the model deployment but also the data needed to train the model. Moreover, the iterative nature of building machine learning models require frequent retraining and validating models before pushing to production. Manually performing all these deployment steps are both time-consuming and labor-intensive.
Model is only as efficient as its data quality
The quality of the model is closely tied to the data used for training the model. Data quality matters not just for model performance but also to eliminate any bias in considering the datasets. Many AI applications are outright obsolete as it was not trained with a broader variety of datasets.
Data growth demands more computing power
As the data increases in size, the machine learning model improves in accuracy as more data is used to train the model. At the same time, if the underlying resources are not flexible to data upticks like adding more storage and processing power, then the model usability declines drastically.
How to overcome challenges in operationalizing Machine Learning models? MLOps Solutions
Understanding the challenges of deploying machine learning models lets enterprises innovate by focusing more on the machine learning models and less on the hurdles to operationalize those models.
Increase Visibility into Machine Learning Projects
Machine Learning challenges varies with perspective and approach. For instance, management would like better visibility into machine learning projects with faster onboarding of Datascience teams and reduced cost.
Flexibility to choose any ML Libraries, Frameworks and AutoML
Data Scientists would require automated deployment pipelines which can integrate with the models implemented using any ML libraries, frameworks or AutoML of their choice. Models should be deployed automatically with minimal effort providing inference endpoints for applications to make use of the model.
Deploy effortlessly with automated CI / CD pipelines
Data Scientists experiment the models repetitively with different algorithms by tuning the hyperparameters for continuous improvement of model’s accuracy. After experimentation phase, the trained models are deployed to staging environment for evaluation before pushing to production.
Ever-changing data along with the iterative nature of machine learning projects mandates for an automated CI / CD pipelines wherein any new environment like staging, production are reproduced automatically with minimal effort.
There are readily available CI / CD pipelines from different cloud and machine learning vendors. But changing from one vendor to another demands revamping your entire CI / CD pipelines.
Enterprise needs the ability to store models in any cloud or in-house registry, deploy models to any cloud-agnostic environment without having to re-engineer their pipelines. Integrated MLOps should be able to deploy to any cloud, on-prem or hybrid based on the infrastructure of your choice by determining the cost for managing the computing resource and monitoring the performance of your machine learning models. Kubernetes based deployment with reproducible CI / CD pipelines makes it easier not only to onboard any new environment but also onboard new team with machine learning models along with the infrastructure needed to train and run the inference for the model.
Automatic scaling & Complex Deployments
Deployment pipelines should be capable of provisioning different resource types (CPU, GPU or TPU) with auto-scaling capabilities. It should also assist in complex model deployment scenarios such as A/B deployments, shadow and phased rollouts which have now become a common pattern in data-driven applications, all while providing real-time insights and recommendations.
End to End MLOps solution necessitates automated monitoring service inspecting model’s health score along with data drift, usage predictions and resource utilization.
The performance of any machine learning model is affected by any change in data, code or model’s behavior. For instance let’s consider a machine learning model approving credit application. Previously the model required only FICO score and income, but later enhanced to use customer’s digital footprint expanding the landscape of potential borrowers. This mandates for code change along with retraining the model with new training datasets with additional features. The CI / CD pipelines should be able to automatically detect these changes, retrain and deploy the trained model with minimal effort.
Monitoring should not only capture data drift but also monitor and auto-scale computing resource for better cost management. Machine learning models without diversified datasets tends to be biased. Enterprises with biased models lose their reputation increasing the customer churn rate.
MLOps solution should be able to go beyond deployment and monitoring with the ability to observe and act on insights with self-explainable capabilities justifying why a model behaved in certain manner.
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