4 ways to fine-tune your AI and machine learning deployments – TechRepublic

Life cycle management of artificial intelligence and machine learning initiatives is vital in order to rapidly deploy projects with up-to-date and relevant data.

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An institutional finance company wanted to improve time to market on the artificial intelligence (AI) and machine learning (ML) applications it was deploying. The goal was to reduce time to delivery on AI and ML applications, which had been taking 12 to 18 months to develop. The long lead times jeopardized the company's ability to meet its time-to-market goals in areas of operational efficiency, compliance, risk management, and business intelligence.

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After adopting a life-cycle management software for its AI and ML application development and deployment, the company was able to reduce its AI and ML application time to market to days, and in some cases, to hours. The process improvement enabled corporate data scientists to spend 90% of their time on data model development, instead of 80% of time on the resolution of technical challenges resulting from unwieldy deployment processes.

This is important because the longer you extend your big data and AI and ML modeling, development, and delivery processes, the greater the risk that you end up with modeling, data, and applications that are already out of date by the time they are ready to be implemented. In the compliance area alone, this creates risk and exposure.

"Three big problems enterprises face as they roll out artificial intelligence and machine learning projects is the inability to rapidly deploy projects, data performance decay, and compliance-related liability and losses," said Stu Bailey, chief technical officer of ModelOP, which provides software that deploys, monitors, and governs data science AI and ML models.

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Bailey believes that most problems arise out of a lack of ownership and collaborationbetween data science, IT, and business teams when it comes to getting data models into production in a timely manner. In turn, these delays adversely affect profitability and time-to-business insight.

"Another reason that organizations have difficulty managing the life cycle of their data models is that there are many different methods and tools today for producing data science and machine language models, but no standards for how they're deployed and managed," Bailey said.

The management of big data, AI, and ML life cycles can be prodigious tasks that go beyond having software and automation that does some of the "heavy lifting." Also, many organizations lack policies and procedures for these tasks. In this environment, data can rapidly become dated, application logic and business conditions can change, and new behaviors that humans must teach to machine language applications can become neglected.

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How can organizations ensure that the time and talent they put into their big data, AI, and ML applications remain relevant?

Most organizations acknowledge that collaboration between data science, IT, and end users is important, but they don't necessarily follow through. Effective collaboration between departments depends on clearly articulated policies and procedures that everyone adheres to in the areas of data preparation, compliance, speed to market, and learning for ML.

Companies often fail to establish regular intervals for updating logic and data for big data, AI, and ML applications in the field. The learning update cycle should be continuous--it's the only way you can assure concurrency between your algorithms and the world in which they operate.

Like their transaction system counterparts, there will come a time when some AI and ML applications will have seen their day. This is the end of their life cycles, and the appropriate thing to do is retire them.

If you can automate some of your life cycle maintenance functions for big data, AI, and ML, do so. Automation software can automate handoffs between data science IT and production. It makes the process of deployment that much easier.

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4 ways to fine-tune your AI and machine learning deployments - TechRepublic