Since the early 2000s, the advent of digital transformation has compelled businesses across industries to reimagine their business processes, customer interactions, products and even business models to remain relevant. While some companies have flourished, others have struggled in their efforts to transition into software-driven tech companies. Today, a new wave of transformation is taking place at an unprecedented pace: Enterprise AI transformation. Fueled by technology innovations in accelerated computing and generative AI, genAI has sparked a widespread sense of urgency among businesses across the globe, compelling them to develop AI strategies and once more rethink their offerings to become the disruptors, not the disrupted. This heightened focus on AI is evident from the drastic increase in the number of AI-related press releases and references during enterprise earnings calls over the past six months. In the coming years, it is evident that the most successful business applications will seamlessly blend code with machine learning models in harnessing the power of AI to unlock new levels of innovation, efficiency and competitive advantage. Some will thrive once more, while others will fall short in their attempts to adapt.
From digital to AI transformation: A new era of disruption
As businesses embark on their AI transformation journey, it is essential for them to invest in a robust AI/ML infrastructure and technology stack. This investment is crucial for enabling efficient Machine Learning Operations (MLOps) and supporting the overall success of their AI initiatives. MLOps encompasses a set of practices, processes and technologies that are utilized to effectively create, manage and operationalize ML models in production environments. It encompasses three key components: DataOps, ModelOps and RuntimeOps. DataOps empowers data teams by facilitating the streamlined collection, cleansing, enrichment, storage and management of data. Establishing a well-defined data governance is a crucial enabler for ModelOps. It encompasses the facilitation of data science and ML engineering teams in creating, experimenting, training, fine-tuning, validating and managing ML models with sufficient version controls. Finally, RuntimeOps is focused on enabling ML engineering and operations teams to efficiently package, distribute, deploy and serve models in production environments. It includes continuous monitoring of model performance to ensure security, reliability and adherence to the intended state, thereby preventing any deviations from the desired outcomes.
Incorporating DevOps principles into the different aspects of MLOps proves invaluable when addressing challenges in areas such as managing large volumes of data, developing and training machine learning models and deploying and serving these models in runtime. This underscores the significance of collaboration, automation and integration across various teams involved in the MLOps workflow; not only between data science, data engineering and ML engineering teams but also involving SW development, product management and security teams. These teams must collaborate seamlessly to integrate ML models into SW products, providing customers with new levels of innovation while ensuring the security, reliability and responsibility of the solutions.
Bridging ML and software development with experimentation-ready environments
While the “ML model development lifecycle” and the “SW development lifecycle” differ in terms of the personas, workflows and outputs, there are certain similarities when it comes to the technology stack needed to support the different phases. Both SW development and ML model development require a suitable development environment. In the context of ML, Modular Notebooks are used due to their enhanced capability to work seamlessly with both code and datasets, which traditional integrated development environments (IDEs) do not offer to the same degree of sophistication. These environments allow teams to work on the underlying code and execute and track experiments. This experimentation creates a pool of model candidates that can be compared against each other. Model candidates, which consist of a data set, configuration file and code, can then be elevated to Model Versions. Organizations are increasingly using general-purpose ML models as a foundation while adding proprietary code and data on top to better meet their specific requirements and use cases. This process is known as model-tuning.
Managing AI artifacts with security, governance and agility
Given the constantly evolving nature of both code and ML models, it is essential to effectively manage model versions within a centralized Model Registry (like a software development repository) prior to packaging and distributing them for Runtime. Managing AI artifacts is more intricate compared to code as it involves tracking and overseeing the versions of training and testing data, model versions, feature sets and other metadata. Furthermore, meticulous control and management of access to these registries are necessary to ensure the integrity of both the models and data. Even slight intentional or unintentional errors or changes can lead to deviations from the intended state, potentially exposing the organization to significant security risks. Therefore, conducting ML model and training data scanning is critical to prevent model hallucinations (unintended model behavior and outputs), biased or poisoned data (model inputs), as well as ensuring compliance with model and source-code licenses when utilizing third-party components.
Additionally, MLOps can significantly use the principles of Continuous Integration and Deployment (CI/CD) adopted in DevOps, enabling faster and more frequent updates of data and ML models. Continuous monitoring and updating are crucial for ML models as they need to adapt to changing conditions and real-world data inputs to maintain accuracy and quality. Continuous model re-training is necessary to improve and uphold the model’s performance. Automated testing, deployment and monitoring are essential to ensure seamless operations and rapid iteration.
Conclusion
In conclusion, MLOps is still in its early stages of maturity as the supporting infrastructure and software stack continue to evolve. However, we can draw valuable lessons from the optimization and automation of the Software Development Lifecycle to find suitable solutions for businesses. These solutions enable businesses not only to achieve faster AI implementation but also to do so using the right principles, tools and techniques that lead to more sustainable and responsible outcomes. By embracing DevOps-Accelerated MLOps, businesses can unlock the full potential of AI and position themselves as disruptors vs. disrupted within their industry. DevOps-native vendors are well-positioned to assist businesses in their digital and AI transformation journeys and help to accelerate the development lifecycle of both Software and ML models while bridging the gap between various teams. Through collaboration, these teams work together to deliver cutting-edge software products that incorporate both Code and ML models, thus establishing a foundation for modern market-leading solutions.
