Every company across every industry wants to find more efficient ways of operating. Advances in technology over recent years have innovated and improved business processes and outcomes exponentially, but there is still more that can be done.
There is a careful balance between adopting new solutions and not overspending on proprietary options.
Open-source solutions are a low cost and agnostic way of accelerating businesses, and bringing them together can be a practical option for many.
Artificial Intelligence (AI) has boomed over the last few years, and businesses of all shapes and sizes are now utilising this technology to reach their goals. AI is commonly used for banking services, product recommendations, and digital assistants, and when created properly, these solutions can be very successful.
One of the biggest challenges with deploying AI in a business is ensuring it stays supportable and operational over time. Combining AI with data science and DevOps can produce more practical outcomes and lead to improved success. However, implementing these aspects together requires careful planning, skills, and effort.
Aligning Development Approaches
Almost all cloud services now use DevOps as their standard for development. It puts an emphasis on automated processes and focuses on creating a culture that encourages collaboration across all teams. Applications using DevOps are well supported through instrumentation, platform, and processes. It forces teams to look at the infrastructure required for supporting the application and if any tools can help automate this. Generally speaking, AI projects use their own development methodologies. Similarly to DevOps methodologies, these use practices and principles from real-world projects to lead the development to success. This approach is individual to data science projects and unique because small iterations are made frequently to refine the data. The intention behind this kind of methodology is to align the AI development alongside the business needs. This process usually has little interaction with operational teams and does not focus on the product release. DevOps teams today are usually unfamiliar with the way data science projects are developed. Both AI development and DevOps are separate methodologies with one goal in common: to get the application into development. By bridging the gaps and aligning these differing approaches, businesses can produce more practical and focused outcomes to meet their goals. AI projects must incorporate some of the deployment and operational methods used in DevOps, and DevOps projects can benefit from AI developments automation and release processes.Integrating AI, Data Science, And DevOps
Bringing these methodologies together could potentially streamline and stabilise a business’s release process. Bridging the gaps between AI and DevOps is not always easy, and there are a few things to consider when looking to integrate these methods.- The AI development process relies heavily on experimenting with different iterations of models. This can take up a lot of time for each model to be tested and trained properly. Create a separate workflow that accommodates the timelines for model builds and the testing cycle.
- When teams are developing for AI and data science projects, put a focus on adopting practices and processes which allow evolution and a model lifecycle. The key aspect here should be delivering value over time as opposed to a one-time creation of a model.
- DevOps is known for integrating development, release, business, and operational knowledge into a single solution. It is critical that AI is represented and included throughout this process.
- Appropriate metrics must be used to inform the models that are being updated and deployed. AI offers many benefits to metrics in application solutions and should be integrated appropriately. This technology can be used to define accuracy metrics and then track these throughout the process. Business metrics should be tracked to capture the impact the model has on operations. Furthermore, data metrics must be monitored to keep an eye on model performance.
