Databricks vice president Cecily Ng says the Philippines is a 'particularly exciting market' for artificial intelligence
Manila, Philippines – Data and artificial intelligence are powerful.
That's what has made disruptive companies like Netflix and Spotify (companies that sometimes seem to know our interests better than we do) successful. However, data can be incredibly messy and complex, especially if there is no way to structure it.
San Francisco-based data and AI company Databricks wants to change that. Databricks is currently working on developing generative AI tools that it believes will help “democratize data and AI to the masses.”
The company recently announced its own open source large-scale language model called DBRX. It already outperforms his GPT-3.5 on most benchmarks and can be fine-tuned to suit enterprise needs. What does this look like in practice? Businesses can deploy their own chatbots that allow agents to use natural language to retrieve the information and data they need without having to know how to code.
For example, Cecily Ng, vice president and general manager of ASEAN operations at Databrick, said some banks are enabling generative AI models to read and index the large amount of documents that financial institutions may have. It said it had developed an “internal knowledge management chatbot.” All you have to do is ask the banker questions in natural language.
But before such exciting work can be done, the “plumbing” needs to be done right first, says Ng.
“Integrated data platform”
Companies that store data in the cloud, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform, all need a way to integrate and ingest their data. This is especially true for digitally native companies like Maya, Grab, and Coins.ph, which can be fed millions of rows of data every day. All three companies turned to his Databricks to help them leverage their data.
“Think of it like a handle. If all your data is there, different formats, different tables, and you can't use it, what are you supposed to do? You run the service layer ,” said Ng. “Databricks runs on top as an orchestration engine for processing.”
In many ways, this serves as the foundation or “plumbing” for an enterprise's data needs, allowing different teams to access data for different use cases. For example, a company's business analysts may take data to create graphs showing trends in new customers, while data scientists use that data to detect and predict fraud. You may want to build a model.
“A unified data platform is very important. Some of these basic data pieces may not sound very sexy, but they are the foundation if you want to do sexy things. If you don't store it in one unified location in one format that all data engineers, data analysts, and data scientists can access, you end up with data in silos,” Ng told reporters. Told.
Eng said that consolidating all of a company's data in one place helps prevent so-called “technical debt.” This is the cost that businesses incur when they have to purchase multiple technologies and figure out how to connect them all together to make them work.
Utilization of data
For “digital native” companies like Grab, Maya, and Coins.ph, having the right data plumbing can make things go much faster.
For example, Singapore super app Grab, which offers ride-hailing, food delivery and other services on its platform, is using data to build data science models for hyper-personalization, Ng said.
So far, Grab has generated data equivalent to more than 6 billion transactions, from ride-hailing to food and grocery delivery, and the company is using Databricks to understand and understand all of them. Build rich consumer segments and deeper profiles in a short amount of time. Hours, not weeks.
Domestic financial services companies such as digital bank Maya and cryptocurrency exchange Coins.ph also use Databricks to help with fraud detection and credit scoring. It also frees up a company's limited engineering resources from 24-hour monitoring and troubleshooting. Overall, this has helped Coins.ph reduce operating costs by 70% and infrastructure costs by 50%.
Apart from these companies, Ng saw great growth potential in the Philippines, especially since the population is “very mobile-first.”
“There's a lot of data that needs to be managed. Considering that, the Philippines is a particularly exciting market for us,” Ng told reporters. “There are a lot of great data practitioners, but I think I’m a developer. [have] There are a lot of talented people in the Philippines. ” – Rappler.com
