Bringing Data Science and App Development Cycles Together Generally, we are accustomed to developing and training machine learning models in our preferred Python notebook or an integrated development environment (IDE), such as Visual Studio Code (VSCode). The model is then passed on to an app developer, who integrates it into the larger application and deploys it. Bugs and performance issues are frequently overlooked until the application has already been deployed. The resulting conflict between app developers and data scientists to identify and resolve the root cause can be a time-consuming, frustrating, and costly process.
Data Science and Application Development As AI becomes more prevalent in business-critical applications, it becomes clear that we must work closely with our app developer colleagues to build and deploy AI-powered applications more efficiently. We focus on the data science lifecycle as data scientists, which includes data ingestion and preparation, model development, and deployment. We are also interested in retraining and redeploying the model on a regular basis to account for newly labeled data; data drift user feedback, and changes in model inputs. The app developer is concerned with the application lifecycle, which includes building, maintaining, and constantly updating the larger business application that the model is a part of. Both parties are motivated to ensure that the business application and model work together to meet end-to-end performance, quality, and reliability objectives. What is required is a more effective way of bridging the data science and application life cycles. Azure Machine Learning and Azure DevOps can help with this. These platform features enable data scientists and app developers to collaborate more efficiently while continuing to use tools and languages with which we are already familiar. For detailed information on Azure DevOps and ML, refer to the trending machine learning course in Mumbai. The Azure Machine Learning pipeline can automate the data science lifecycle or "inner loop" for (re)training your model, including data ingestion, preparation, and machine learning experimentation. Similar to this, the Azure DevOps pipeline can automate the "outer loop" or application lifecycle, which includes unit and integration testing of the model and the wider business application. In short, the data science process is now integrated into enterprise applications' Continuous Integration (CI) and Continuous Delivery (CD) pipelines. There will be no more pointing fingers when there are unexpected delays in app deployment or when bugs are discovered after the app has been deployed in production.
Azure DevOps and Azure Machine Learning are two services offered by Microsoft. Let's discuss how this integration of the data science and app development cycles is accomplished. Assume that your enterprise's data scientists and app developers use Git as their code repository. Any changes you make to training code as a data scientist will cause the Azure DevOps CI/CD pipeline to orchestrate and execute multiple steps, including unit tests, training, integration tests, and a code deployment push. Similarly, any changes to the application or inferencing code made by the app developer will trigger integration tests followed by a code deployment push. You can also use your data lake to set specific triggers for model retraining and code deployment. Your model is also registered in the model store, allowing you to look up the exact experiment run that produced the deployed model.
Final Words! As the data scientist, you retain complete control over model training with this approach. You can keep writing and training models in your preferred Python environment. You can choose when to run a new ETL / ELT run to refresh the data and retrain your model. Similarly, you retain ownership of the Azure Machine Learning pipeline definition, including details for each data wrangling, feature extraction, and experimentation step, such as compute target, framework, and algorithm. At the same time, your app developer counterpart can rest assured that any changes you make will go through the necessary unit, integration, and human approval steps for the overall application. With that in mind, if you’re someone looking to improve your data science skills for successful career, join the data science course in Mumbai and become a certified data scientist in top-notch companies.