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Interview: Dr. Bhushan Desam, Director, Global AI Business at Lenovo

I recently caught up with Dr. Bhushan Desam, AI global business leader for Lenovo’s Data Center Group to discuss how the digital transformation of business isn’t truly possible without incorporating machine learning. As a global business leader, Bhushan is focused on developing AI and machine learning business at DCG. On any given day, Bhushan helps manage Lenovo’s overall AI strategy, assists in making product portfolio decisions with engineers and product teams, interfaces with R&T teams, supports global sales teams with customer engagement and deepens relationships with HPC customers and partners. He completed his PhD in engineering at the University of Utah, which introduced Bhushan to high performance computing (HPC) and laid the technical groundwork for his role and responsibilities today. After several years working in the field as a researcher and an engineer, Bhushan re-entered academia in 2011 to pursue a joint program in technology management at MIT Sloan School of Management and School of Engineering.

insideBIGDATA: What are the challenges and opportunities you see in the proliferation of data?

Dr. Bhushan Desam: The explosion of the sheer amount of data – combined with the fact that most of that data is unstructured – makes data-based decision making a difficult task for organizations that are embracing digital transformation. It’s just not practical to manually mine data and synthesize the information in a meaningful way. In that case, how do enterprises manage digital transformation initiatives that make a meaningful and measurable impact? We’re finding that this answer lies in machine learning (ML) and deep learning (DL).

insideBIGDATA: Tell us more. In deep learning, how do you work with unstructured data?

Dr. Bhushan Desam: When dealing with large amounts of data in a variety of formats like images, video, text etc, deep learning enables us to efficiently process and understand the meaning of data which is not possible with traditional programming techniques. Consequently, we can now build new analytical capabilities that provide high value to organizations: predictive analytics – the ability to predict what will happen based upon what is found in the data set – and prescriptive analytics – the ability to prescribe a specific set of actions based on the prediction. Without these two core capabilities, most digital transformation initiatives never fully realize their potential.

insideBIGDATA: Are there challenges in implementing deep learning? What lessons can you share?

Dr. Bhushan Desam: Although many enterprises are interested in implementing a deep learning solution to help them with their digital transformation initiatives, the technology is so new they don’t know where to start. It is unlikely that one provider can fulfill all the needs that arise from various scenarios in enterprises. For example, the data location determines where deep learning takes place; i.e., on-premise, cloud or hybrid. Consequently, there is a strong need for flexible tools and services that help enterprises to rapidly experiment, build, deploy and scale deep learning in enterprise context.

Through our work with customers, we’ve learned to apply a staged approach on implementing a deep learning solution for success. First, we sit down and determine the business needs: What insights are they hoping to uncover with the data? Where is the data coming from? How will this impact the business? From that point, we develop a proof of concept by working closely with customers in our AI Innovation Centers by leveraging a team of experts including data scientists and solution architects. This important exercise gives customers access to the right hardware and software tools to implement a deep learning solution in their own enterprise environments. Finally, we work with customers to deploy AI applications by providing solution guidance and professional services to meet their objectives.

insideBIGDATA: Despite the buzz, many IT practitioners are still trying to figure out how to implement ML/DL projects. How is Lenovo helping them to accelerate their efforts?

Dr. Bhushan Desam: I talk to customers every day looking for answers on how to implement their machine learning projects. One distinct aspect of AI and machine learning compared to other enterprise applications is that it is mostly driven by open source. As such, there are almost no prepackaged AI offerings where IT has experience in either managing effectively or scaling as demand arises. Because of this, Lenovo has worked closely with customers and partners to create suitable AI solutions for both enterprise and HPC customers. We have introduced our proprietary Lenovo intelligent Computing Orchestration (LiCO) solution, which simplifies resource management and makes launching AI training jobs in clusters easy, allowing them to start projects in multiple AI frameworks, including TensorFlow, Caffe, Intel Caffe, and MXNet.

We also work closely with customers to help them deploy deep learning solutions in real enterprise environments while complementing solutions from partners like SAP. Our customers are  able to use both SAP and Lenovo platforms to create deep learning models on-premise in instances where data cannot be moved to cloud for economical and/or data governance reasons. These converged models provide enormous flexibility for enterprises to tackle various scenarios in implementing deep learning-based applications.

insideBIGDATA: Is there anything else you’d like to share around this topic?

Dr. Bhushan Desam: Digital transformation is underway. As the C-suite demands better insights from data, enterprises will be tasked to make data-driven decisions based on those insights. For success in this area, it is critically important to choose deep learning tools that allow multiple data sources and locations. Working with a trusted partner to take a staged approach will help guide you in the right direction along with an evolving partner ecosystem to provide flexibility with your digital transformation initiatives.

 

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