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“We adopted AI, but the results aren’t meeting expectations.”

This is likely the concern of many corporate managers who have implemented AI. Swept up in the AI boom, many rushed to adopt artificial intelligence, but they find it difficult to realize tangible changes like increased sales or improved work efficiency. Facing an ROI that is slow to materialize, despite the investment, many companies start to wonder, “Are we doing something wrong?”

The problem isn’t where we think it is. It’s not a lack of model development skills, nor a lack of data.

The real pitfall lies in ‘Operations.’

While traditional software, once completed, runs stably, AI is different. Like a living organism, its performance fluctuates according to the ever-changing data and business environment. Much like a demanding houseplant, it is difficult for AI to function properly without continuous management and careful monitoring.

Ultimately, the real key to successful AI adoption is establishing a ‘sustainable operational environment.’

The concept that emerged to solve these operational issues is “MLOps.”

Today, we will discuss what MLOps is and the solution offered by LaonPeople’s MLOps platform, EZ PLANET.

What is MLOps, and Why is it Necessary?

MLOps, short for Machine Learning Operations, is an integrated management methodology that connects the development and operation of machine learning models. It systemizes and automates the entire AI operation process, solving problems inherent in traditional machine learning projects, such as model performance degradation, complex deployment processes, and version control difficulties. This allows ML engineers to train models in an efficient environment and stably serve completed models to the actual business.

The Role of MLOps

MLOps Value

Operational Automation and Standardization

→ Supports the integrated management of complex elements that constitute an ML system (code, data, models, infrastructure).
→ Efficiently controls the entire process from development to operation, simplifying system complexity.
→ Strengthens collaboration across multiple teams through a standardized pipeline.
Business Agility and ROI Improvement

→ Automates model development and deployment, shortening the time it takes for business ideas to reach the market.
→ Maximizes resource utilization efficiency, reducing operating costs → Improves ROI.
Operational Stability and Reliability

→ Ensures service stability through continuous management of model performance and a rapid response system for issues.

In conclusion, MLOps establishes the foundation for systematically managing the entire lifecycle of machine learning models, enabling AI projects to grow into businesses that generate continuous results, not just one-off successes.

Core Components of MLOps

The core components of MLOps consist of the key stages and technologies necessary to efficiently manage the entire lifecycle of a machine learning model, from development to deployment and operation.

1. Data Management

Organizing datasets required for model training, version control, and labeling support.

2. Model Training & Experiment Tracking

Providing an environment to train models using various algorithms and hyperparameters, tracking and comparing the results of each experiment.

3. Model Training & Validation

Preventing overfitting and underfitting and validating model performance with various metrics to secure a highly reliable model for the actual operating environment.

4. Deployment & Serving

Supporting the easy deployment of trained models as APIs and enabling them to be served in various environments (Cloud, On-premise).

5. Monitoring & Retraining

Supporting pipelines that automate model training, testing, and deployment processes.
Includes the function to automatically initiate retraining (CT, Continuous Training) upon detecting model performance degradation.

6. Collaboration & Governance

Standardizing the collaborative environment among data scientists, ML engineers, and operations teams.
Maintaining security and productivity through systematic management of access rights.

Since MLOps deals with many complex intertwined elements, management with a single tool is difficult. To address this operational complexity, an integrated platform like LaonPeople’s EZ PLANET is emerging as an essential solution.

Now, let’s take a look at LaonPeople’s MLOps platform, EZ PLANET.

MLOps Platform, EZ PLANET

EZ PLANET is an integrated solution that provides data scientists and machine learning engineers with a consistent One Process collaboration environment. It helps automate and simplify the entire process, from repetitive data processing to experiment tracking, model management, deployment, and monitoring.

EZ PLANET’s Basic Configuration

Key Features of EZ PLANET

1. Flexible Member Management Function by Organization and Department
AI operation permissions can be freely configured according to the situation.

2. Batch Image Preprocessing Function to Reduce Data Resources
Supports image preprocessing at the tool level, and centralized data management enables history tracking and reuse.

3. Easy Labeling Tool Provided
Supports everything from data labeling to training, offering easy usability with shortcuts, history, and auto-labeling.

4. Support for Various AI Model Integrations
Both internally developed AI models and SOTA models, as well as provided AI models, can be utilized.

5. Support for Continuous AI Learning
Supports the management of algorithms, parameters, versions, and weights.

6. GPU Resource Sharing and Parallel Learning Support
GPU resources can be shared with other users, and parallel learning shortens training time.

7. GPU Resource Monitoring Function
Allows checking the required GPU capacity and the available/unavailable GPU capacity.

8. GUI-based Pipeline Connection Function
Intuitive UI & UX enables quick and easy pipeline connection.

9. Pipeline Schedule Automation Function
Provides automated schedule management for performing repetitive pipeline tasks.

Thanks to these features, EZ PLANET efficiently automates the operation and synchronization of the entire machine learning lifecycle, maximizing productivity and minimizing errors.

The Effect of EZ PLANET

EZ PLANET maximizes a company’s AI capabilities, achieving the following tangible results:

1. Rapid Deployment and Quality Innovation

Significantly shortens the time required to release AI solutions by automating and standardizing the process from model development to deployment. Simultaneously, it guarantees stable quality for AI services by enhancing overall system efficiency.

2. Significant Cost Reduction Effect

Eliminates repetitive manual work, reducing unnecessary operating expenses by up to 30%. Furthermore, it reduces GPU costs by over 25%, securing both financial efficiency and a stable operational structure.

3. Securing Long-Term AI Model Reliability

Maintains a high inspection accuracy of over 95% through continuous learning and optimization, ensuring stable model performance over the long term.

4. Maximizing Business Agility and ROI

Organically connects and simplifies the entire process from product development to operation. Automated operations, cost optimization, performance maintenance, and business agility maximize the company’s Return on Investment (ROI).