As artificial intelligence (AI) has grown more advanced, so too has the organization’s ambition to embrace these innovative technologies. Given
With their expanding aspirations, the puzzle for organizations is how they can scale AI to achieve the best outcome. The answer is that organizations must adopt and implement machine learning operations (MLOps), an across-functional capability involving several departments cooperating to deliver value and leverage data. Collaboration occurs across all aspects of an organization, including business product owners, data engineering, data science, IT, and infrastructure, with a common goal of generating value from data-led analytical solutions.
Data transformation is a critical aspect of ML. To fully deploy ML, data needs to be in eh correct form. ML requires a large amount of data to be effective, and it is time-consuming to collect, manage, and store. AI models require high-quality data that are well-governed and properly transformed, which can be challenging in practice. Often well-established businesses, like those in the financial services industry, have significant amounts of data stored in legacy formats. This makes it difficult to integrate and use in modern ML models. For example, traditional forms of enterprise data are databases, files, and systems with unstructured text that are difficult to utilize. Data used for the ML model is complex and messy in some organizations, with missing values, outliers, and other anomalies. Data transformation involves cleaning and pre-processing this data, which can be time-consuming.
In implementing machine learning operations (MLOps), organizations need the expertise to engineer the solution and support culture. It is a new field, and many organizations lack of staff with the necessary skill sets. Demand for technical talent will likely persist in the next five years with demand for MLOps engineers and IT architects across industries expected to be particularly high. The data from Deloitte shows that 26% of organizations are missing MLOps engineers and 28% need more IT architects. This demonstrates a gap in the technical skill sets required to develop MLOps to scale AI capabilities. A cultural shift is required among business leaders to redesign business practices to incorporate MLOps capability across all aspects of business operations, not just single-use cases.
Many technologies are available to organizations who want to invest in MLOPs capability. Despite the wide usage of cloud services among organizations, implementing other technologies like all-in-one and other specialist MLOps platforms are also being explored. However, they still lag behind the rate of adoption required, given most respondents focus on AI. Organizations are overwhelmingly planning to invest further in a wide range of technology in the near future.
Legacy technology infrastructure is one of the top three challenges, behind data and technical limits, and high investment costs. This was particularly noticeable in the technology, media, and telecom (TMT) industry. Transitioning out of a legacy system can be costly and time-consuming, but it is needed to evolve into a more AI-mature organization. This demonstrates the risks of organizations deploying AI without the infrastructure in place to prevent system failures. A system failure can have significant consequences. The sectors most affected by system failure are government and public services. A significant 36% from this sector say their current infrastructure does not meet their current MLOps technical requirements. In financial services, two in three large organizations (>USD 5 billion) report their current infrastructures do not meet requirements.
Conclusion
AI is developing rapidly, and emerging technologies will be harnessed by ambitious organizations ready to scale their AI capabilities and meet the challenge with MLOps. Organizations should aim to develop the greatest possible MLOps capability by overcoming key barriers of data transformation, lagging infrastructure, and lack of investment. Too, organizations must recognize the importance of the regulatory environment, minimizing risk and consequently boosting the confidence of consumers and investors. ML expertise and talent are vital to benefit from the many gains, but organizations must act now. MLOps holds the key to unlocking the enormous potential of AI and leading organizations into future growth.
Article by: Asst. Prof. Suwan Juntiwasarakij, Ph.D., Senior Editor & MEGA Tech