On October 28th IBM announced its intention to acquire Red Hat. At $34 Billion, this is the largest software acquisition ever. So why would IBM pay such a large amount of money for an Open Source software company? I believe that this acquisition needs to be seen beyond looking just at DevOps and Hybrid Cloud, rather in the context of IBM’s view of the future where the business value from IT services will come from in future. This acquisition provides near-term tactical benefits from Red Hat’s OpenShift Platform and its participation in the Kubeflow project. It strengthens IBM’s capabilities to deliver the foundation for digital business transformation. However, digital business is increasingly based on AI delivered through the cloud. IBM recently announced a $240M investment in a 10-year research collaboration on AI with MIT and this represents the strategy. This adds to the already significant investments that IBM has already made in Watson, setting up a division in 2016, as well as in cloud services.
Red Hat was founded in 1993 and in 1994 released the Red Hat version of Linux. This evolved into a complete development stack (JBoss) and recently released Red Hat OpenShift - a container- and microservices-based (Kubernetes) DevOps platform. Red Hat operates on a business model based on open-source software development within a community, professional quality assurance, and subscription-based customer support.
The synergy between IBM and Red Hat is clear. IBM has worked with Red Hat on Linux for many years and both have a commitment to Open Source software development. Both companies have a business model in which services are the key element. Although these are two fairly different types of services – Red Hat’s being service fees for software, IBM’s being all types of services including consultancy, development they both fit well into IBM’s overall business.
One critical factor is the need for tools to accelerate the development lifecycle for ML projects. For ML projects this can be much less predictable than for software projects. In the non-ML DevOps world microservices and containers and the key technologies that have helped here. How can these technologies help with ML projects?
There are several differences between developing ML and coding applications. Specifically, ML uses training rather than coding and, in principle, this in itself should accelerate the development of much more sophisticated ways to use data. The ML Development lifecycle can be summarized as:
- Obtain, prepare and label the data
- Train the model
- Test and refine the model
- Deploy the model
While the processes involved in ML development are different to conventional DevOps, a microservices-based approach is potentially very helpful. ML Training involves multiple parties working together and microservices provide a way to orchestrate various types of functions, so that data scientists, experts and business users can just use the capabilities without caring about coding etc. A common platform based on microservices could also provide automated tracing of the data used and training results to improve traceability and auditing. It is here that there is a great potential for IBM/Red Hat to deliver better solutions.
Red Hat OpenShift provides a DevOps environment to orchestrate the development to deployment workflow for Kubernetes based software. OpenShift is, therefore, a potential solution to some of the complexities of ML development. Red Hat OpenShift with Kubernetes has the potential to enable a data scientist to train and query models as well as to deploy a containerized ML stack on-premises or in the cloud.
In addition, Red Hat is a participant in the Kubeflow project. This is an Open Source project dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Their goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.
In conclusion, the acquisition has strengthened IBM’s capabilities to deliver ML applications in the near term. These capabilities complement and extend IBM’s Watson and improve and accelerate their ability and the ability of their joint customers to create, test and deploy ML-based applications. They should be seen as part of a strategy towards a future where more and more value is delivered through AI-based solutions.
Read as well: IBM & Red Hat – and now?
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