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Why we need MLOps

The drawback of conventional AI model and deployment:
  • The quality of AI training and development is easily affected by different developers
  • Inability to effectively track the development process and compare process details, lack of transparency
  • The development process is slow and cannot effectively speed up the iterative optimization process
  • It is not easy to introduce experts in different fields to cooperate and it is difficult to achieve model optimization
The benefit of AI model with MLOps:
  • Improve the quality of model development service delivery through a DevOps approach
  • Systematize the process, reduce human error, and save model building time
  • Scalable, create repeatable workflows, improve iterative issues
  • Effectively manage the entire machine learning life cycle and develop together with experts in different fields
  • Easily deploy high-precision models anywhere

What is MLOps


Data Preparation

Model Training

Model Evaluation

Model Analysis

Model Deployment

1. Data Preparation

Manual data prep and wrangling can eat up to 80% of your team’s time. While MLOps can’t make your data better, it can help you get better at handling it.

The MLOps way: Build an automated data preparation and management pipeline.


  • Provide AI expert and help to collect data for AI training
  • Review and clean the data to ensure no garbage in garbage out
  • Analyze data for feature store
  • Create auto labeling tool to assist and speed up data processing
  • Manage and store datasets for training preparation
2. Model Training

Model training can get messy when you can’t properly attribute and manage the generated artifacts, along with crowing code branches. When none of these get logged, stored, and version-controlled, team productivity plunges.

The MLOps way:Automate version control and automate metadata management.


  • Select a line up of storage agnostic version control systems, adapted for ML workflows.
  • Integrate them into the platform and configure them.
  • Check that metadata from new training runs gets auto-committed to version control.
  • Build a metadata store to capture t relevant information for further analysis.
3. Model Evaluation

Manual model testing is menial work and a sure-fire way to miss some important performance metrics, especially as you test across different data segments. But it’s a crucial step for ensuring that you are pushing a working thing into production.

The MLOps way:Automate model evaluation and subsequent re-training.


  • Set up a framework for model monitoring and validation, using the selected toolkit
  • Ensure auto-capture of all the essential performance data from each model run
  • Record and store all the tidbits for easy reproducibility.
  • Create specific triggers for launching pre-training when the model didn’t perform well.
4. Model Analysis

Less than 10% of ML models make it past through to successful deployment, oftentimes because your research team cannot properly pass the model to production folks. You can put down that fire with MLOps.

The MLOps way:Set up “Model as a Service” cloud deployment.


  • Decide on the optimal framework for wrapping the model as an API service.
  • Or select and configure a container service for deployment.
  • Create a production-ready repository of models
  • And set up a model registry where all the relevant model metadata is stored.
5. Model Deployment

Model monitoring, so that’s a thing too? If you don’t keep tabs on your model performance in a real-life setting, you are going to miss a huge concept drift heading your way sometime soon.

The MLOps way:Automated model monitoring and auto-triggers for retraining.


  • Pick the optimal agent for real-time model monitoring.
  • Configure it to capture anomalies, detect concept drift, and monitor model accuracy.
  • Add extra measures for estimating model resource consumption.
  • Specify re-training triggers and configure alerts.