Artificial Intelligence (AI) and Machine Learning (ML) offer unlimited possibilities for enterprises. But the hard part is implementation. ML models are complex. Too many variables and uncertainties make AI a hit-or-miss gamble. About 87% of ML models do not go beyond the experimental phase. CIOs look at MLOps to fix the underlying issues and deploy AI projects at scale. 

MLOps, or “Machine Learning Operations” is a set of practices that combine machine learning with DevOps to enable the adaptation of ML models in real-world settings. MLOps enable enterprises to onboard the massive improvements in AI tooling and technologies that have taken place of late. These developments enable consistent and reliable scale-up of AI projects and expedite the AI application lifecycle.

But deploying MLOps is fraught with many challenges. Here are five things CIOs need to know when using MLOps to scale AI.

1. Overcome skill gaps

Most enterprises expect data scientists to accomplish end-to-end machine learning solutions at scale. ML projects involve about 90% of engineering concepts and 10% science. Data scientists build ML algorithms and models. But they falter in making their models scalable or reproducible. The transition from research to production often becomes sluggish. The algorithm becomes too technical and complex for real-world application, forcing its abandonment. Scale-up to the enterprise level requires competency in engineering processes. It requires competencies in software engineering and distributed computing. 

MLOps come to the rescue. CIOs who apply MLOps would do well to: 

Constitute a cross-functional team compromising data scientists, data engineers, DevOps specialists, developers, and business executives. Successful roll-out of MLOps depends on the availability of multiple competencies. Each team member evaluates the aspects of the ML application connected to their niche. They use continuous integrations and continuous delivery principles to roll out ML projects at scale. The speed of execution also improves. 

Machine Learning projects are iterative. Make sure the team is continuous and not disbanded after the execution of the ML project. 

Hire MLOps professionals or multi-skilled personnel competent in data science. MLOps professionals develop, maintain, scale, and automate the MLOps framework to support the ML models.

2. Provision of the right infrastructure

Many enterprises implement Artificial Intelligence in scattered pilots, in disparate siloed systems. IT teams create new models from the ground up every time and prepare data differently for each project. Such AI implementation often creates shadow IT. 

Competitive pressures mean enterprises make such a limited approach to AI untenable. But deploying stable AI models at scale requires infrastructure capable of running all models. 

• Undertake MLOps maturity assessment upfront, to identify gaps in the enterprise’s IT environment.
• Adopt the right MLOps tools. Major cloud platforms, such as Google, Microsoft, and AWS offer built-in MLOps capabilities. There are also open-source MLOps frameworks, such as MLFlow and KubeFlow. 
• Provision tooling, technologies, and platforms that optimize AI workflows.
• Leverage technology stacks and services that enable automation, and promote modular approaches. 
• Strike the right balance between short-term fixes and tools for long-term sustainability. 
• Provide scalable infrastructures, such as GPUs and analytical engines.
• Implement MLOps with a road map of practices to reduce complexity and technical debt.
• Ensure the teams involved in implementing MLOps have robust collaboration tools.

MLOps offers several options. The best MLOps process ensures integration with existing DevOps processes. It delivers additional capabilities to manage ML.

3. Pay attention to information architecture

The smooth rollout of ML algorithms depends on robust enterprise information architecture. Poor information architecture causes stumbling blocks for most enterprises. 

• Consolidate ML projects. Avoid launching multiple projects across different parts of the enterprise. 
• Consolidate the data. Maintaining duplicate data sets creates bloat and inefficiencies. ML algorithms are only as good as the data fed into them.
• Implement governance to ensure reliability and relevance of the data and processes. A comprehensive governance mechanism ensures a thorough audit of ML applications. 

Here are five ways to get data governance right.

• Improve network visibility. Many enterprises lack visibility into the risks their AI models pose. Deploying mitigation measures requires a complete understanding of the risks.
• Set compliance frameworks to meet security, privacy, and regulatory requirements.

Invest in scalable, dynamic, and resilient enterprise information architecture as a prelude to AI rollout.

4. Have clarity on the scope

MLOps is a new and evolving service. Definitions and scope vary. For instance, some developers limit MLOps to monitoring running models. Others approach it as the series of steps required to move new models into live environments. 

The earliest MLOps deployments increased the pace of AI and ML development and deployment. Developers now use MLOps to scale and standardize ML implementations. Many developers also use MLOps to apply best practices on ML-powered code.

• The best approach to MLOps encompasses the entire AI life cycle. Co-opt tools and approaches for data management, model development, and deployment.
• Consider the business impact of MLOps. For instance, evaluate potential improvements in productivity, speed, reliability, risk, and talent acquisition. Inefficiencies in any of these areas choke the ability of the AI model to run seamlessly at scale. 
• Define KPIs for business applications. Track such metrics. Correlate the metrics to the performance of the ML applications. Make tweaks as necessary. Tweaking the algorithms to optimize processes, to cater to the changing business needs becomes an ongoing activity.

5. Manage resistance to change 

As it is with any tech transformation, implementation of MLOps causes disruption. Disruption forces change, and change leads to resistance. CIOs implementing MLOps in their enterprise have to pay due attention to managing resistance to change.

• Articulate the value addition that MLOps offers and how employees benefit from such value addition. For instance, set clear visions of productivity improvements through the rollout of MLOps.
• Make the rank-and-file understand the importance of AI as a business-critical system that must run 24/7. Convince them of the necessity of MLOps for the smooth implementation of AI systems. 
• Make enterprise-wide efforts to foster a learning organization that values development. 

MLOps enables easy adoption and scale-up of ML projects. Done the right way, it preempts complexity. It also spares enterprises from the disaster of abandoning costly AI investments.