The State-Of Machine Learning Adoption in the Enterprise – CIO Applications

Machine learning libraries with well-defined interfaces and documentation are becoming more accessible and therefore facilitating its adoption

We rely on Cross Functional Team setup (Develop Advocates), focus on Transformational or Disruptive solutions, Customer Pain points/Solutions and Communication/Marketing.

How do you see the evolution of machine learning within the next few years with regard to some of its potential disruptions and transformations?

Industry is still navigating throughout the ML hype. There are a few ML-based applications that have been successfully deployed and added to applications found in the marketplace. For example, voice and image recognition, service brokering and matchmaking, consumer forecast, etc. have found their place in the domestic use but these are still far away from truly becoming disruptions in the industrial space.

Critical aspects in the success of ML evolution are:

The reduction in complexity for mapping the domain expertise to ML-based solutions. Today there is no straightforward path to transfer domain knowledge to the data scientists where there is still a high dependency on.

As we continue to mature and descend from the ML hype, we will soon realize that not all industrial processes are suited for ML. This aspect still needs to be settled.

Provide greater access to ML automation.

Legacy systems (server/IaaS based) are decelerators in the ML evolution. These tools need to undergo structural upgrades to be able to cope with the new wave of data and analytics requirements (scalability, volume, speed, multitenancy, etc.). New data and compute frameworks are going to be needed to reduce complexity while increasing automation.

Agile change management cycles.

ML-model management soon to become a critical-path need.

A workforce skillset aligned with the know-how to map ML to domain expertise is precious.

What would be the single piece of advice that you could impart to a fellow or aspiring professional in your field embarking on a similar venture or professional journey along the lines of your service and area of expertise?

Think outside the box. Protect a portion of your resources allocated to transformation.

Use Open Source technologies and university partnership and internship programs to pilot solutions to prove out ROI. Companies have been restricting development to their internally conceived software solutions. However, it is now understood that no single player will be able to provide all the pieces of the overall solution. Therefore, there is value in looking for potential partnerships that would increase the chances to success.

Make IP solutions accessible to the industry and let other ideas into the internal design process. This implies the need for a cultural transformation. Look at effective business and pricing models. Perhaps one can achieve more effective business by partnering accordingly. And lastly, using resources to create an all-encompassing solution hinders the ability of a company to rapidly adapt to a fast-pace technology evolution. So develop while youre small and then grow.

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The State-Of Machine Learning Adoption in the Enterprise - CIO Applications