Data & AI

MLOps services

Get seamless model integration, flawless performance and maximum ROI with ELEKS' end-to-end MLOps services

What is MLOps?

What you get with ELEKS’ MLOps services

Shorter model development lifecycles

ELEKS’ MLOps services accelerate machine learning development and model integration by implementing CI/CD pipelines, reducing technical debt and enabling rapid experimentation. Furthermore, by applying automation, we’ll help you reduce the burden of manual intervention and enable faster iterations. So, your team gains agility and flexibility when it comes to machine learning model testing and deployment, significantly reducing your ML system’s development lifecycle.

Improved scalability and ML model performance

Utilising MLOps, continuous integration and deployment practices, we’ll establish the consistently high performance of your ML solution through automated model validation, monitoring, retraining and re-evaluation. Our MLOps engineers will also optimise your infrastructure and set up your workflows to proactively identify and resolve bottlenecks. So, your ML model will perform better and scale seamlessly, even when demand surges or workloads become complex.

Increased ROI of your machine learning projects

With ELEKS’ MLOps services, you can maximise the business impact of your machine learning initiatives and be confident that any ML project investments translate into increased business value and profit. By optimising your resources, automating model management processes and machine learning workflows, improving ML model accuracy and reducing your solution’s time-to-market we’ll help you get the biggest possible return on your investment.

Optimized infrastructure costs

Businesses seeking to optimize their machine learning processes can streamline operations and reduce costs by leveraging MLOps services. One of the key advantages of adopting MLOps infrastructure is its rapid deployment capability, allowing businesses to quickly initiate ML-related operations without the need for additional development or configuration. With MLOps, you can achieve faster results while efficiently managing costs and resources, allowing you to focus more on your core business and customer needs.

How we deliver MLOps services

Infrastructure analysis and preparation

To kick off, our MLOps experts collaborate closely with your team to holistically analyse your existing infrastructure. We deep-dive into your business requirements and define the specific problems you aim to address with a machine learning model. This approach allows us to build a best-fit roadmap with clear timelines, objectives and KPIs, ensuring your model integrates seamlessly, scales effectively and delivers the desired business value. And, if you need custom ML model development, our data science team will be on hand to support you.

ML solution architecture design

Our DevOps experts will set up the infrastructure for further ML model implementation. Then, our team will design a robust ML solution architecture that optimises model performance, scalability, and maintainability. If you need a custom model, our data scientists and machine learning engineers will perform data preprocessing, cleaning, and feature engineering. Then, we’ll develop and train the ML model to handle predictive decision-making in the latter stages of the project.

Deploying machine learning models in production

Post-architecture, our MLOps specialists will develop pipelines based on your ML models and deploy them into the production environment, ensuring flawless interoperability within your ML systems. We'll oversee the entire deployment process, ensuring no disruption to your operations. Ultimately, we aim for you to leverage machine learning to achieve maximum efficiency and scalability.

Model monitoring, optimisation and maintenance

Beyond deployment, our MLOps team will provide continuous ML lifecycle management, including model monitoring and optimisation. We’ll track model performance metrics and anomalies and trigger alerts if we find any deviations. Additionally, we can offer continual enhancement of your ML pipelines based on new data, changing trends or evolving business needs. The scope of work may include retraining models and updating them to maintain accuracy and performance over time.

Have a question? Speak to an expert

MLOps levels

Level 0

Model deployment

This initial stage involves manually executing each step in the pipeline, covering everything from data analysis to model deployment.

Level 1

Model retraining

The next stage introduces automated retraining of the model within the production environment. Here, an entire training pipeline, rather than just the model itself, is deployed along with its corresponding service. This level is suitable for scenarios where data alterations are rare while the ML approach remains stable.

Level 2

CI/CD of the pipeline

Advancing to level 2 introduces enhancements in continuous integration and continuous pipeline delivery. This stage is crucial for achieving a fully functional Machine Learning application at the production level, particularly when data and ML models undergo frequent changes.

The MLOps process

Design

Requirement engineering
ML use cases prioritization
Data availability check

Model development

Data engineering
ML model engineering
Model testing and validation

Operations

ML model deployment
CI/CD pipelines
Monitoring and triggering

Why choose ELEKS
as your MLOps partner?

Robust DevOps and Data
Science expertise

With a solid team of DevOps experts, software engineers and data scientists, you can be confident your machine learning project is in safe hands. Decades of experience in developing sophisticated machine learning models and seamlessly integrating customers’ existing ML models into large-scale production environments have made us the partner of choice for some of the world’s biggest businesses, delivering complex ML projects and MLOps services to an industry-leading standard.

End-to-end MLOps
services

We provide expert end-to-end support and guidance throughout the machine learning lifecycle. If you need a custom ML solution, our data science team will handle data preparation, development and model training, plus any further implementation and support. And where your existing machine learning models are concerned, we offer expert MLOp services including infrastructure setup, model integration, continuous monitoring and maintenance. We’ll support you at every stage of your ML project, guaranteeing that it aligns with your technological requirements and business goals.

Awards

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