WHAT IS MLOps And WHAT DOES IT DO?
What Is MLOps?
MLOps is a set of practices for collaboration and communication between data scientists and operations professionals. Applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments. It’s easier to align models with business needs, as well as regulatory requirements.
MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle — data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.
What Problems Does MLOps Solve?
Like DevOps, MLOps helps to improve the quality of production models, while incorporating business and regulatory requirements and model governance.
Some problems that Mlops solves;
- Inefficient workflows — MLOps provides a framework for managing the machine learning lifecycle effectively and efficiently. By matching business expertise with technical prowess, MLOps creates a more structured, iterative workflow.
- Failing to comply with regulations — Machine learning is a fairly new field, and regulatory bodies continue to change their requirements and update their guidelines. MLOps takes ownership of staying in compliance and up-to-date with shifting regulations, such as those found in banking.
- Bottlenecks — With complicated, non-intuitive algorithms, bottlenecks can often happen. MLOps facilitates collaboration between operations and data teams, helping to reduce the frequency and severity of these types of issues. The collaboration that MLOps promotes leverages the expertise of previously siloed teams, helping to build, test, monitor, and deploy machine learning models more efficiently.
How It Works?
- A data pipeline providing up-to-date validated, and preprocessed data (Feature Store)
- A development environment to experiment with building models
- A training pipeline that allows you to create ML models from the data
- A continuous training pipeline that automates constructed training pipelines
- A model deployment process that will enable you to take your (chosen) model to production
- A model performance monitoring process that (automatically) checks if model performance is still excellent or decaying.
MLOps Lifecycle
The MLOps lifecycle relies on continuous improvement to function properly .Let’s examine how each step of the 9-stage lifecycle contributes to its overall goals.
- Problem Definition: Identify which problems you want your AI to solve.
- Data Collection: Gather relevant, unbiased data for model training.
- Data Processing and Storage: Sort your data into distinct batches for more efficient processing.
- Metrics Definition: Set benchmarks to determine whether or not your AI is solving the problems it is being designed to.
- Data Exploration and Analysis: Choose the modeling techniques to start with that will make the most of your data.
- Feature Extraction and Engineering: Determine relevant data points and ensure they are updated regularly.
- Model Training and Offline Evaluation: Test different models to see which ones yield the best results.
- Model Integration and Deployment: Implement effective models into the product using a cloud system to allow access by the end-user.
- Model Release and Monitoring: Observe the model’s performance and identify opportunities for improvement and retraining.
Benefits of MLOps
- Improved confidence in their model
- Improved compliance with regulatory guidance
- Faster response times to changing environmental conditions
- Lower break-fix cost
- Increased trust and ability to drive valuable insights
The key difference when comparing MLOps vs DevOps is that MLOps focuses on improving machine learning, while DevOps is geared toward software development and performance.
DevOps vs. MLOps
If we examine these differences under a table,
