Including ModelOps in your AI strategy
Modern organized enterprises recognize that the adoption of a data-driven strategy is crucial to compete in an increasingly digitalized market. Data and analytics have become a very high priority, rising to the board level, which sees technologies such as Machine Learning and Artificial Intelligence as an opportunity to increase business capabilities, making processes more efficient and facilitating the spread of new business models.
Far and wide, investment in AI and data management are drastically increasing and new data science projects are underway to build predictive and analytical models for various purposes. However, while companies plan to scale up sophisticated Artificial Intelligence solutions in a reasonable time, the harsh reality is that the adoption of these solutions is often stalled because companies generally focus more on development than on the operationalization of the models. Many non-digital native’s businesses., the adoption of the data science discipline is often begun with numerous self-contained, and fragmented data science teams, committed by and large to developing models of Machine Learning and Deep Learning.
These small teams of data scientists have sprung up in the varied business units with the aim of building models for different business purposes. Furthermore, thanks to the wide availability of new advanced technologies within easy reach for the development of these models, companies, in order to exploit this abundance of wonderful technologies to create ever newer and performing AI solutions at scale, have had to deal with an increasing complexity which impacted production processes and operations. Just thinking of the extensive collection of software tools available to support the Data Scientists to dive into the world of Data Science such as Python libraries, Jupyter notebooks, Spark MLlib, Dask, and other numerous open-source libraries that have sprung up everywhere, based on the new algorithms that have emerged, which allow data scientists to do traditional clustering, anomaly detection, large-scale predictions and to go even further, to do facial recognition or video analysis.
Unfortunately, this approach has been adopted by many companies causing decentralization and fragmentation of data science teams with a consequent slowdown in the development of models and total absence of collaboration between business units. As a result, CEOs, as well as business executives, are dissatisfied with these initiatives as companies are failing to scale AI by accumulating models that are not implemented, not used and not updated, implemented manually and often not in line with expectations on the value that should come from AI.
Companies, therefore, need to adopt solutions that can help them in delivering value in a timely manner and in line with expectations. These capabilities must be designed to support and accelerate the process of development of models and find a faster way to put machine learning models into production, by enabling enterprises to scale and govern their AI initiatives.
Surveys show that Integration and Risk Management are the Top Barrier to operationalize AI models and hence to the success of AI initiatives.
AI-Focused Executives Face Many ModelOps Challenges, image from the “State of ModelOps 2021”
Models have long been seen as essential enterprise assets, and AI models are showing their ability to deliver very significant value. Enterprises increasingly understand that to continuously catch this value while managing risk requires ModelOps practices for the age of AI. As a result, they’re investing in ModelOps.
ModelOps is becoming a core business capability, with enterprises investing in creating more efficient processes and systems for operationalizing AI models.
