5 Key Factorshindering the Deployment of Your Machine Learning Model
In the realm of data science, making data models reach production in companies with less mature data science functions can be a challenging endeavour. To address this issue, a structured approach emphasising automation, reproducibility, and collaboration is key.
Automate Workflows with CI/CD Pipelines
Automating workflows through Continuous Integration (CI) and Continuous Deployment (CD) pipelines helps streamline training, testing, and deployment. This reduces manual errors and accelerates releases, ensuring a smoother transition from development to production.
Containerization: A Consistent Runtime Environment
Using containerization, such as Docker, helps create consistent runtime environments that facilitate portability between development and production. This ensures that the model operates correctly across different environments.
Version Control for Code and Data
Maintaining version control for code and data using tools like Git and DVC is essential. This ensures repeatability and eases team collaboration, making it simpler to track changes and collaborate effectively.
Incremental Deployment Strategies
Adopting incremental deployment strategies like shadow deployment, canary releases, and A/B testing allows for safe validation of models before full production roll-out. This approach helps detect and resolve issues early, reducing the risk of major problems in production.
Monitoring and Logging
Implementing monitoring and logging helps continuously track model performance and detect issues early in production. This ensures that any problems are addressed promptly, improving the overall reliability and effectiveness of the model.
Cloud Infrastructure and Platforms
Leveraging cloud infrastructure or platforms that integrate model governance and deployment can ease operationalization without heavy MLOps overhead. For example, Snowflake with Posit’s Orbital can provide a seamless path from training to production.
Promote Knowledge Sharing and Transparency
Promoting knowledge sharing and transparency by capturing and exposing feature pipelines and code is crucial. This avoids isolated or ad hoc development that can stall operationalization, fostering a culture of collaboration and reusability.
Build Internal Capabilities Gradually
Building internal capabilities gradually by adopting modular, reproducible workflows can help embed quality and reliability. Utilising Python testing frameworks (pytest, unittest) and DevOps tools (GitHub Actions, Jenkins) can help in this process.
For companies with limited data science maturity, starting small by automating key bottlenecks, containerizing models, and using careful versioning and deployment strategies can make the transition from prototype to production more systematic and reproducible. Utilising tools and managed cloud platforms that integrate training, governance, and deployment can reduce complexity and accelerate production readiness. Additionally, fostering cross-team collaboration and managing feature/code reuse avoids duplicated effort and improves maintainability.
Lastly, it's worth noting that Jupyter notebooks are excellent for data science work but not for writing production-level code. Instead, it's recommended to pythonise and modularise the code into scripts with multiple functions to ensure the model's production readiness.
[1] Data Science at Scale: A Guide to Building and Deploying Machine Learning Models, by Foster Provost and Tom Fawcett [2] Data Science for Dummies, by Lillian Pierson [3] Snowflake and Posit's Orbital: A Powerful Combination for Streamlined Machine Learning Operations [4] GitHub Actions: A Comprehensive Guide for Continuous Integration and Continuous Deployment [5] Jenkins: The Definitive Guide: A Comprehensive Guide to Jenkins and Continuous Integration
This article is intended to provide a general overview and is not exhaustive. For a more comprehensive understanding, we recommend consulting the referenced resources.
Data-and-cloud-computing platforms, such as Snowflake with Posit’s Orbital, can reduce complexity and accelerate production readiness for less mature data science functions, by integrating training, governance, and deployment.
Artificial-intelligence models' operationalization can be made more systematic and reproducible through the gradual build-up of internal capabilities, using Python testing frameworks like pytest or unittest, and DevOps tools like GitHub Actions or Jenkins.
