Machine learning: things are getting intense

Deloitte predicts that in 2018 large and medium enterprises will intensify their use of machine learning (ML). The number of implementations and pilot projects using the technology will double compared to 2017, and will double again by 2020. Further, with enabling technologies such as ML APIs and specialized hardware available in the cloud, these advances will now be available to small as well as large companies. 

Progress in five key areas should make it easier and faster to develop ML solutions. Three of these five advancements—automation, data reduction, and training acceleration—make ML easier, cheaper, or faster (or a combination of all three). The other two—model interpretability and local machine learning—enable applications in new areas, which should also expand the market.

Collectively, the five vectors of ML progress should double the intensity with which enterprises are using this technology by the end of 2018. In the long term, these vectors should help make ML a mainstream technology. Advances will enable new applications across industries where companies have limited talent, infrastructure or data to train the models.

Companies should:

* Look for opportunities to automate some of the work of their oversubscribed data scientists, and ask consultants how they can use data science automation.

* Keep an eye on emerging techniques, such as data synthesis and transfer learning, that could ease the bottleneck often created by the challenge of acquiring training data.

* Find out what computing resources optimized for ML are offered by their cloud providers. If they are running workloads in their own data centers, they may want to investigate adding specialized hardware to the mix.

* Explore state-of-the-art techniques for improving interpretability that may not yet be in the commercial mainstream, as interpretability of ML is still in its early days.

* Track the performance benchmarks being reported by makers of next-generation chips, to help predict when on-device deployment is likely to become feasible.