Smart gadgets and smart homes are collecting our everyday move, predicting our likes and dislikes and proposing action based on our behavioral prediction. Enterprises are increasing their foothold in Artificial Intelligence by employing more and more machine learning models to improve their businesses. Even with all the promises AI is offering, enterprises are finding it hard to adopt. With the advent of AutoML and a wide range of Machine Learning frameworks and libraries, it is straightforward to build but deploying and managing Machine Learning models are still laborious and painful.

Evolution of Machine Learning Deployments:

Step Towards Kubernetes

Machine Learning deployments began as a single server on-premise deployment which suited well for enterprises with fewer Machine Learning models. The dynamic nature of data requiring frequent re-training proved to be a nightmare to manage in a single-server environment. 

The performance, flexibility, and ease of deployments in cloud took a shift towards cloud-based Machine Learning. At the same time, enterprises soon learnt the hard lesson of cost management when they started paying more to cloud vendors as the scale of their ML application grew with the increase in data. 

On-premise deployment might be a solution for large enterprises that have a dedicated team to manage their internal cloud but not so optimal for smaller enterprises. With the advent of container frameworks like Docker and container orchestration like Kubernetes, enterprises are able to scale dynamically based on the needs of their ML application either on cloud or on-prem.

Cloud Adoption of Kubernetes

Kubernetes with its strong foothold as a container orchestration framework has paved way for involuntary adoption of Kubernetes by all cloud vendors.

Microsoft Azure offers the flexibility to train and deploy ML models on Kubernetes using CLI, SDK or by using a Visual Studio Code Extension. With Google’s Tensorflow docker image, the model can be deployed with single or multiple replica sets just by changing the yaml file along with an external IP address for accessing your ML model.

AWS provides the flexibility to choose any container orchestration framework with its Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate with ECS or EKS. During the recent re:Invent event, Amazon introduced the release of Amazon SageMaker Operator for Kubernetes optimizing the model performance with reduced cost by effectively managing the delivery pipeline challenges using Kubernetes. 

Kubernetes taking over ML Deployments

Accelerate Deliverables

Containerized deployments with its ease of setting up uniformed pipeline across different environments speed up the process of building, training and deploying ML models.  With Kubernetes, Data Scientists and ML engineers work on environments with similar pipelines thereby reducing the time to debug and deliver production-ready models.

Improve Availability

When a new version of the model is deployed, Kubernetes orchestrates model serving in such a way it gracefully handles the current request with the old model and any new upcoming requests are served by the newly deployed model. The auto-scaling feature along with load-balancing incoming requests raises the availability score of ML models deployed in Kubernetes.


There is no longer a need to manually spin off servers for ML deployments during peak load. Kubernetes reduces both DevOps and hardware cost by auto-scaling up and down based on the need.

ML Model As A Service

Container-based deployments not only amplifies the ease of use and flexibility to auto-scale, but also powers Machine Learning models to be more manageable by breaking down monolithic AI applications into reusable microservices. 

CI / CD Pipeline

Regulatory industries are frequently challenged to recreate the model predictions by managing the same set of pipelines across different environments. Container-based deployment has given rise to a more effective integration and deployment pipelines wherein ML Engineers deploy the same set of pipelines laid out during the development phase gaining visibility to any changes in the pipeline.

Resilient to Changes

The auto-scaling capability of Kubernetes spins up additional resources whenever there is a peak in demand thus effectively managing any changes to the data load.

Maintain uniform code version

Enterprises spend their DevOps resources on monitoring code uniformity across different servers as running different versions not only cause monetary injuries but also lowers the trust among customers. Containerized deployment with its master and worker strategy makes sure that all worker nodes are running the right code version and therefore does not yield any surprising predictions.

Streamline Roles & Responsibilities 

Kubernetes with its role-based access demarcates the responsibilities with regards to model performance and resource consumption. Clear boundaries assist engineers to deal with their respective problem areas where ML engineers can work on resource utilization and Data Scientists can concentrate on the model performance.

Empower Edge Computing

Edge Computing is targeted to grow 10-fold by 2024. Multiple companies like Rancher labs, Edgeworx, CDNetworks, IoTium and Mirantis are extending edge computing with Kubernetes. Edge Computing with Kubernetes is set to revolutionize the consumer world.

Rigidness Adopting Kubernetes

Know More Than Kubernetes 

Knowing Kubernetes alone will not solve the end-to-end needs of Machine Learning projects. For instance, you need to know how to expose your deployed models either as an API or gRPC. There is no integrated monitoring solution bound to a continuous deployment environment. The lack of end-to-end solution makes it harder to operationalize ML models.

Minimal Documentation

Managing End-to-End ML solution necessitates knowing more than kubernetes. The large codebase of Kubernetes with minimal documentation along with the need for external dependencies flounder the success of ML models.

Going Live Vs Managing Live

It is not so easy to manage multiple kubernetes clusters. Debugging in a cluster environment is both time consuming and resource intensive. 

Security Surprise 

There are vulnerabilities in Kubernetes exposed via its API and Kubectl command-line tool. Hackers were able to create or replace files using kubectl command. The security flaw in API exposed the ML models for DOS attack. Even though the kubectl vulnerability has been fixed, hackers are finding different ways to hijack the kubernetes clusters. 

Lack of Monitoring 

There is no unified monitoring solution for monitoring the performance of kubernetes clusters based on the ML workload. One has to evaluate a wide range of monitoring tools available in-market to find the best fit for their specific needs. Even after selecting a monitoring solution, one has to customize it to fit with the other monitoring and alerting tools for end-to-end monitoring solution. 


Even with all the advancements, companies are still struggling to adopt Kubernetes. The rigidness in adopting Kubernetes along with the need for end-to-end solution for managing Machine Learning projects adds on to its struggle.  With struggle comes opportunity paving way for solutions.

Cloud Providers have put their foot forward in finding flexible solutions for adopting Kubernetes. Challenges in adopting Kubernetes is rooting its way towards a solution with plug-and-play deployment and monitoring solution. Enterprises with a growth mindset are eagerly building end to end solutions blending with different ML framework and libraries to build and manage ML models efficiently. 

Check out Predera ( ) to build ML models agnostic to any environment either cloud, on-prem or hybrid with plug and play deployment & monitoring solution.