Taimur Rashid, who joins Redis from Microsoft, talks with ChannelBuzz about his plans to expand the company’s business in the AI space, which he said is still very small compared to its total potential.
Redis Labs, has named Taimur Rashid as the company’s Chief Business Development Officer. His primary objective will be to develop Redis’ full potential in the growing AI and machine learning space, with their various channel and strategic partners being critical to this effort.
“My focus is on AI and machine learning, primarily, and my current charter is putting together an end-to-end Go-to-Market strategy for the emerging AI and machine learning businesses,” Rashid told ChannelBuzz. “As a company, we feel we have a unique opportunity to help developers, data scientists, and machine learning engineers, where we can drive scale motions along repeatable patterns.”
Rashid’s background is in the cloud hyperscalers. At Microsoft, he was General Manager and Head of Worldwide Customer Success for the Azure Data and AI business, where he directed cloud solution architects to develop and execute programs for customers to successfully adopt and grow with these services. That came after almost 10 years at AWS, concluding as Managing Director of Business Development for the AWS Platform, where he led incubation, market development, and technical go-to-market strategy and execution for the platform.
The big cloud hyperscalers are already a key part of Redis’ partner ecosystem, but Rashid thinks those relationships can be tweaked a little to further improve them.
“We have strong momentum around our partnerships with the Big Three cloud providers around our Redis Enterprise business, but there’s a bit of fine tuning that can be done,” he said.
“It’s not only deepening them from a Go-to-Market standpoint, around marketing and sales, but developing deeper levels of integration with their native experience,” Rashid explained. “It’s a matter of refining the Go-To-Market and partnering with them on Go-To-Market execution.”
Rashid said expanding relationships with the tier of hyperscalers under the Big Three could be a fruitful strategy.
“We have natural momentum with the tier ones, but IBM Oracle and HPE come to mind,” he indicated. “The interesting thing is there has been inbound interest. We need to consider what sort of unique value each would bring to the joint partnership. We are in the early stages of exploring interest there.”
Rashid acknowledged that he isn’t fully up to speed yet on strategic vendor alliances like NVIDIA and Intel, but said they are logical areas for expansion.
“I’m in my third week,” he said. “As I get more familiar with alliances, I see us expanding into these adjacent areas. I think optimizing on chip platforms around AI and machine learning will continue for sure. The conversation gets more interesting with partners like NVIDIA and AMD, and cloud providers are investing in their own chipsets like AWS Graviton.”
More alliance Go-to-Market deals are likely, Rashid indicated,
“We don’t have formal Go-to-Market agreements beyond big ones like Microsoft, but because of interest in the database, we have people who do it on their own,” he said. “Salesforce Heroku, for example, has a Redis option. But what’s interesting to me is how Redis can be tightly coupled with apps in developer- oriented platforms like Okta and Twilio. Opportunity drives more developer mindshare and tools, and with that you need datastores. So it makes sense to explore tighter integration with those platforms and Go-to-Market as well, where it makes sense.”
Look for Redis to expand their own premier services as well.
“Offering premium services is based on understanding where our customers are today, and looking at the entire value chain that’s being offered,” Rashid said. “There are areas where we may not have a service today, but due to the adjacency we could create our own service, or partner with those downstream and upstream in the value stream. We’ve gone from enterprise cache to real-time database to real-time platform, and when I think of premium services, I think about that platform story, so that we not only have relevance at different parts of the data estate but to make sure we can let customers leverage us across that. So we will look at having premium services around those adjacent workloads that they use as part of their data estate strategy.”
Rashid stressed that channel partners are critical to extending AI to customers.
“AI and machine learning skillsets among customers are generally not at the same competency level as others, and I look to integrator partners to help scale the technical intensity around machine learning and AI. This includes both boutique consulting partners and GSIs.”
Other types of partners with the right AI and machine learning skills are also critical.
“Feature engineering right now is getting a lot of VC money, but the challenge with these models has been getting them into production, particularly because of issues involving data cleansing and labelling,” Rashid said. “As we look at the value chain for AI and machine learning, feature engineering is the first mile, with its model building, validation, and model monitoring. It’s about which partners have the technical competency to bring to our broader vision and technical skillsets.
“We are still very much in the early innings of AI and machine learning,” Rashid concluded. “But there’s already a plethora of services and technology and interest, and we will see areas pop up which will be helpful for our community. We see it today but its still very fluid.”