He said that he wanted to take advantage of IBM’s $34 billion 2018 Red Hat acquisition to help customers manage a growing hybrid cloud world, while using artificial intelligence to drive efficiency.
But instead of the new strategy acting as a big growth engine, IBM’s earnings today showed that its cloud and cognitive software revenues were down 4.5% to $6.8 billion.
Krishna laid out his strategy in November telling CNBC, “The Red Hat acquisition gave us the technology base on which to build a hybrid cloud technology platform based on open-source, and based on giving choice to our clients as they embark on this journey.” So far the approach is simply not generating the growth Krishna expected.
The company is also in the midst of spinning out its legacy managed infrastructure services division, which as Krishna said in the same November interview should allow Big Blue to concentrate more on its new strategy.
This has been doubly true for the data, analytics, and AI enterprise community, as businesses work to make the most out of their existing technology investments in order to optimize operations in light of the current market uncertainties.
Rather, the company hopes enterprises will see federated learning as an opportunity to drive better AI outcomes through better model training without being impeded by data silos and enjoin collective parties to participate without having to worry about the privacy, compliance, security, or performance concerns mentioned above.
While it is unlikely that federated learning will ever show up as a one-click, deployable service option, the fact that it’s now available as both a framework (from Google) and a set of libraries (from IBM) prove that there is a light at the end of the tunnel for companies struggling to solve tough architectural AI challenges.