MLOps: A Promising Way to Tackle Top Machine Learning Challenges

Enterprises are banking on machine learning to revolutionize their work processes, exploring the possibilities to overcome the top machine learning challenges with MLOps. 

Machine Learning is becoming an integral part of every modern enterprise application. A recent report by IDC titled “IDC FutureScape: Worldwide Artificial Intelligence and Automation 2022 Predictions” states approximately 85% of the world’s big enterprises will be using Artificial Intelligence (AI) – including Machine Learning (ML), Natural Language Processing (NLP) and pattern recognition by 2026. 

Despite all the funding for ML projects, many enterprises find it challenging to implement and utilize ML tools and their applications in their workflow. The answer could lie with MLOps or Machine Leaning Ops.

The Challenge with Machine Learning

According to the industry experts, process and infrastructure are the crucial factors enterprises struggle with while adopting ML. 

Most organizations do not have repetitive processes to address this issue as yet. Therefore, data scientists utilize their time to accomplish IT operational tasks such as allocating technical resources rather than designing and training data science models. 

The restricted access to hardware in the AI infrastructure stack like GPU processing, CPU processing, data storage, networking, resource sharing, or integrated development environments is a challenge in ML adoption. 

Also Read: Debunking Top Three Myths Holding Back Enterprise Digital Transformation

MLOps is a way to tackle the top machine learning challenges

A partial solution to tackle the challenges in ML is the implementation of MLOps. The same report of IDC also suggests that approximately 60% of organizations will have ML models in their workflow with MLOps/ModelOps capabilities. Off-the-shelf open-source ML pipelines are also available in the market. A few vendors in the market have designs that can execute on any infrastructure. MLOps is an effective solution to minimize the friction between the development and engineering teams to get the ML model into production.

Serverless ML functions

Implementing serverless ML functions is an efficient way to minimize the intricacies of ML pipelines. Such technologies enable developers to design a code and its specifications that automatically translates itself to auto-scaling production workloads. Earlier developers were restricted to stateless and event driver workloads. With these serverless functions, they can execute more significant challenges in real-time by significantly scaling data analytics and machine learning.

The automation of the workflow from packaging, scaling, tuning, instrumentation, and consistent delivery will help to overcome two main challenges of most enterprises:

• Substantially minimize time to market.
• Reduce the number of resources and skills required to accomplish the project.

ML pipelines can be created by seamlessly aligning ML functions. It will help generate data and better features to support in later stages. Shifting to microservices and functional programming models allows for better collaboration and code recycling. The users can eventually expand and align functions without disrupting their pipeline by consuming the required amount of  CPU, GPU, and memory resources. Kubernetes and Kubeflow play a crucial role in this infrastructure by processing pipelines, extending the workloads, and scheduling the right help. 

The CIOs who plan to implement ML and AI in their enterprise applications should know their objectives and initiate by adopting MLOps. Additionally, it is essential to select the right open technologies developed on Kubernetes and its vast ecosystem instead of cloud-specific solutions.  

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