“Across Industries We Are Witnessing Accelerating Digital Innovation At A Record Pace”

“Across Industries We Are Witnessing Accelerating Digital Innovation At A Record Pace”

The technological landscape is changing very swiftly and organizations are harnessing the next-gen technologies to reinforce their growth momentum. Technologies such as Artificial Intelligence (AI) and Machine Learning (ML) help organizations to deliver an enhanced customer experience. The increased investments in these technologies pushing the adoption across industries. Redis is one of the fastest-growing open-source database company helping organizations to unlock the value of AI & ML. Rajeev Ranjan, Editor, Digital Terminal recently interacted with Taimur Rashid, Chief Business Development Officer, Redis. Mr. Taimur talked about the market scenario, growing usage of AI & ML, and latest trends. Read below the excerpts: 

Rajeev: Can you briefly tell us about Redis? 

Taimur: Redis is the company behind open-source Redis, the world’s most loved in-memory database, and the commercial provider of Redis Enterprise, a real-time data platform. As Chief Business Development Officer, I oversee emerging business and commercial strategy at Redis. This includes a variety of functional areas including strategic business development, corporate development, and incubation of new initiatives. I am currently leading a new initiative related to AI & ML. 

Rajeev: How is Redis helping businesses use AI and ML to provide enhanced customer experiences? 

Taimur: We are enabling the future of real-time AI and ML by addressing two very specific priorities for organisations and developers. 

The first is data infrastructure modernisation. Companies are trying to harness the power of real-time data by building digital experiences and applications with ML capabilities. This requires a modern data stack that supports the real-time requirements, and companies are deploying Redis as an online feature store for low latency storage and serving of ML features, which are key inputs that enable predictions. Within the production stage of MLOps life cycle, there are additional areas where Redis serves as a low-latency data store for feature serving. Companies like Uber, Gojek, DoorDash, Netflix, Spotify, Airbnb, and many others have started to share their architectures for their respective ML platforms. And at the heart of these feature platform architectures is the feature store, which is the interface between data and models. 

The second is for building intelligent applications to drive business outcomes. For this category, Redis is used as a vector database to store vector embeddings, which are numerical representations of raw data. This raw data can be audio, video, images, or even unstructured text within documents. Once stored in Redis, these vector embeddings can be used for similarity searches to power intelligent applications like recommendation systems, visual search, fraud detection, and much more.  Once this gets in the hands of developers, innovation for AI-infused applications can be unleashed. 

Rajeev: What is the role of AI and ML in cloud-based operations? 

Taimur: Organisations that use machine learning to power real-time and customer-facing interactions care deeply about performance and cost. Typical use cases include recommendations, search ranking, real-time pricing, and fraud detection. Redis is ideally suited to meet the needs of these AI-infused applications that are latency-sensitive, and that needs to be scaled to manage complex models and ever-growing data sets. 

Rajeev: How is AI and ML evolving today in the industry as a whole? 

Taimur: Across industries we are witnessing accelerating digital innovation at a record pace. Since the start of the pandemic, organisations are adopting digital technologies to modernize the digital interaction for customers, drive more engagement, and secure their back-end architectures to future-proof their businesses. Increasing digitization results in more data being generated at high velocity, therefore allowing more insights to be gleaned from the data. AI/ML is becoming a core part of the business because it’s the key to enabling business transformation. 

Rajeev: What is the reason that organisations are using AI and ML? 

Taimur: The proliferation and access to AI products, tools, and frameworks have helped accelerate the application of AI across industries. As AI becomes more mainstream, organisations are investing more capital and resources in upskilling their employees so that AI can be infused across an organisation’s business processes. The broad goal of this push is to gain a competitive advantage. The more organisations do instill a data-driven mindset and enable teams with the right mechanisms, culture, and architecture, the more they can use the insights from that data for their digital priorities. 

Rajeev: What are some of the AI and ML trends to watch out for in 2022? 

Taimur: There are a number of important AI trends to be aware of and among them is the mainstreaming of Natural Language Process (NLP), which has already revolutionised how humans interact with machines.  AI assistants like Siri, Alexa and Cortana are testaments to this trend, but NLP’s reach is extending to businesses as they look to infuse AI into business operations. 

Another trend to be aware of is AI-powered search; the rise of deep learning has extended its reach into the category of search.  Deep learning models are fueling a fundamental shift in how data can be represented, and this shift is from text-based representations to vector-based representations.  This numerical representation of unstructured data requires a different way of storing, indexing, and retrieving information. The underlying database is a vector database, and it enables vector-based search, which can be the basis of AI-powered applications like recommendations, semantic searching, visual search, matching, and UI-based recommendations.  With over 80% of the data being unstructured, the opportunity is very large for solutions that can offer ML developers end-user simplicity, data versatility, and production scalability.  

Finally, over the past several years there has been a lot of fragmentation with the growing number of solutions that power AI infrastructure, and while MLOps is the broader scaffolding that captures the whole lifecycle from data preparation, model building, and model production; the future of AI infrastructure will be based on modularity and best-of-breed MLOps solutions. This opens more opportunities for companies to participate in this secular trend.  Organisations can truly take advantage of existing advanced analytics and big data technologies that have already been deployed.  The innovation will be anchored on workflow automation, rapid experimentation, model selection, support for data governance, and seamless explainability across the entire lifecycle. 

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