Full-cycle MLOps Services

Efficient, cost-effective, and scalable MLOps solutions for complex AI/ML projects
100+
Projects completed
$20M+
Saved in infrastructure costs
$10B+
Clients' market capitalization
PredictKube Case Study
Originally developed for PancakeSwap to manage 158 billion monthly requests, PredictKube optimized traffic prediction and resource scaling. The AI-driven solution proved so effective that it later evolved into an independent product.
Before
Overprovisioned infrastructure leading to excessive cloud costs
Frequent latency spikes during traffic surges
Inefficient manual scaling, unable to predict load
Challenges in handling unpredictable traffic growth
After
30% reduction in cloud costs through proactive, AI-based autoscaling
Reduced peak response time by 62.5x
Fully automated scaling with up to 6-hour traffic forecasts
Scalable infrastructure that adapts to traffic growth and ensures stability

Benefit from our MLOps services

Full Pipeline Automation
End-to-end automation of data pipelines, training, and deployment reduces human errors and accelerates the entire ML lifecycle
Scalable infrastructure
Our solutions dynamically scale your infrastructure as your data and models grow, ensuring optimal performance without manual intervention.
Accelerated Time-to-Insights
By streamlining data pipelines and automating model deployment, we enable faster delivery of actionable insights, allowing your business to react in real-time.
Guaranteed Model Accuracy
We implement continuous monitoring and validation to ensure model accuracy over time, catching drifts or performance issues before they impact your operations.

What you gain with MLOps as a service by Dysnix

Automated infrastructure management
Automatically adjust GPU resources based on current workload demand, preventing waste and ensuring efficient resource utilization.
Predictive scaling
Anticipates future resource needs and scales accordingly, reducing downtime and over-provisioning costs.
Automated model training
Automatically train or retrain models with updated data, keeping your AI models up to date without manual input.
Automatic rollback
Instantly revert to previous model versions if issues arise, ensuring smooth recovery and minimizing disruptions.
Model registry
A centralized repository to track and manage model versions, ensuring easy access for developers.
Single data source for developers
Provide a unified source of truth for all data, simplifying access and collaboration across teams.
Manual pipeline creation
When custom workflows are required, easily create manual pipelines to fit unique business needs.
Automatic pipelines to train/retrain
Pre-built pipelines streamline the retraining process, making it faster and more reliable.
A/B deployment
Test different model versions simultaneously to select the best-performing one for production.
Progressive learning
Enable models to learn incrementally, improving accuracy with continuous data ingestion.
End-to-end tests
Perform comprehensive end-to-end testing to ensure reliability across the entire model lifecycle.
Monitoring and Alerting
Continuously monitor model performance metrics to ensure accuracy remains at optimal levels.

Our step-by-step MLOps journey

  • 1 Consultation
    We begin by working closely with your team to understand your machine learning workflows, data infrastructure needs, and key project challenges.
  • 2 Assessment
    We evaluate your current machine learning models, providing expert analysis to identify bottlenecks and areas for automation, performance optimization, and scalability.
  • 3 MLOps strategy
    We design a custom MLOps strategy tailored to your needs, outlining key tools, automation processes, and timelines to streamline the model lifecycle, from training to deployment and monitoring.
  • 6 Engineering support
    We provide continuous engineering support to adapt and scale your MLOps processes as your business evolves, ensuring long-term operational success.
  • 5 Scaling & Optimization
    We refine MLOps processes, ensuring scalable solutions that adapt to growth.
  • 4 Implementation
    Our engineers execute the tailored MLOps strategy, integrating advanced automation tools and engineering best practices to ensure optimized performance
Daniel Yavorovych
Co-Founder & CTO
Ready to transform your MLOps? Let Dysnix lead the way!

Leading companies trust our MLOps slotions

We're glad to receive regular signs of approval from our partners and clients on Clutch.
FAQs about MLOps engineering services

What is included in MLOps?

This is the set of processes, procedures, and components that are the parts of MLOps solutions at Dysnix:

  • Data preparation, exploratory data analysis, and feature development. We analyze and plan the future model creation and training, including all the features we want to develop and train.
  • Training, editing, governing, and analyzing the model. This is an ML model development stage. We deploy and serve it in the most suitable scalable environment that supports CI/CD and other efficient maintenance tools.
  • Automation of retraining and optimization. We analyze certain aspects of the model performance and rework it if needed.

What is an MLOps example?

A good MLOps example stands on three pillars: data scientists, data engineers, and DevOps teamwork. Each part owns a unique set of MLOps tools and processes to be done. Thus, MLOps requires the team to be tightly integrated and interconnected to create models that work efficiently. The example of the MLOps services application at Dysnix was a case of building and deploying a model that can recognize the surgical instruments on a table using the computer vision for Explorer Surgical.

What is MLOps in simple terms?

The simplest way to understand MLOps is to imagine it as a kindergarten class of robots that need to be educated on how to do their job. And all those data engineers, scientists, DevOps, AI specialists are teachers that bring all the groups of newborn robots together and raise them until they mature. Does this explain it better?

What is the use of MLOps?

The best application of MLOps is simplifying the process of building ML models by using a mass of DevOps experience and toolkits. Starting from the environment setup and ending with correct work of deployment operations and updates, MLOps combined becomes a much more efficient model of ML model development than any other.

Which is best for MLOps?

The best for the MLOps project is the right selection of the team. With balanced roles and distributed responsibilities, each participant in the process will know what should be done and perform it without worrying about other parts of the project. When you work with a team like Dysnix, your experts get reliable partners deep diving into the context and applying all their expertise for the sake of the project.

How to deploy ML models?

In a few words, to deploy ML models, you need to prepare and train them first. For this purpose, you need to prepare the training environment with all connections the production environment has. After testing, tuning, Q/A checking, and other preparations, you consider your ML model ready to deploy. You prepare a production environment and launch it there.

How to produce ML models?

Training ML models is a complex of manual and automated procedures that must describe, define the architecture, set up and verify the model, and pre-set how it develops and can be updated. To produce ML models using MLOps tools, you have to clarify the goals of their work, the best architecture for them, the characteristics of environments where they might be launched, and the performance of all vital processes of the model itself.