Why are so many enterprises failing to benefit from machine learning, and what can we do about it? | 7wData

Why are so many enterprises failing to benefit from machine learning, and what can we do about it? | 7wData

Did you know that 98% of IT leaders believe machine learning (ML) will give their company a decisive competitive edge? Yet, only 6% of companies believe their MLOps capabilities are mature enough to benefit from it.

These disappointing stats are reported in a recent Forrester Research report. So, what’s going on?

What exactly is ML and MLOps?

To find the answer, let’s first start by defining terms. ML is a type of Artificial Intelligence that enables learning from data without human intervention. Successful businesses are using ML to optimise every aspect of their business: drive employee productivity, improve customer satisfaction, and increase revenue.

But there is a challenge – while the amount of data has increased almost exponentially over the years, the ability to organise and analyse it using ML has lagged significantly.  An even bigger challenge involves operationalising ML models into a production setting to make dumb applications a whole lot smarter. This Forrester report found that only 14% of respondents had a repeatable and robust process for operationalising ML models into a production setting.

One approach many organizations are taking is the adoption of machine learning operations (MLOps).  MLOps is a set of practices for collaboration and communication between data scientists and operational teams across the complete ML lifecycle. In many ways, MLOps is trying to achieve the same benefits of throughput, efficiency, and quality for ML that DevOps is achieving for agile software development.

Adopting MLOps alone will not solve the problems enterprises face trying to implement ML—it’s a first, important step, but more is needed. Organizations that are successful in transforming their ML capabilities have augmented MLOps with key processes, tooling, and continuous improvement practices. Some of these practices may sound familiar, as they come directly from lessons learned in industrial manufacturing. 

For over 50 years, manufacturing companies have implemented Six Sigma and lean manufacturing techniques to solve quality issues. Today, organizations are using some of these same techniques to create value from their data. In essence, they are becoming information factories.

It’s difficult to overstate the role automation has played in modern production engineering – transforming product quality, productivity, and throughput. Jidoka is a Japanese term for automation with human intelligence, giving machines and operators the ability to stop work if they detect a problem. The problem can then be corrected immediately, rather than waiting until the end of the production line.

The concept of Jidoka can do the same thing for the analytics production line. Self-service with Jidoka capabilities can provision the infrastructure, tools, and data needs for each of the personas involved in the ML process. This type of automation drives efficiency and guarantees compliance to standards. The result? No more time wasted waiting for access to a suitable environment or trying to configure a new tool freshly downloaded from the web. Each phase in the ML process can be automatically scheduled, making the entire system predictable and efficient.

Tooling plays a fundamental role in contemporary production facilities. Used wisely and with the right checks and balances, tooling helps deliver scale. It can reduce the skills required while improving quality, time to value, throughput, and velocity.

Today’s information factory will require a range of tools to suit the role of each persona and to meet the demands of each phase of production. As new, more challenging business problems are tackled, new tooling will be required.

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