How to build a data foundation for generative AI
GenAI depends on data maturity, in which an organization demonstrates mastery over both integrating data – moving and transforming it – and governing its use.
GenAI depends on data maturity, in which an organization demonstrates mastery over both integrating data – moving and transforming it – and governing its use.
Amazon Simple Storage Service (Amazon S3) is a cloud-based object storage service from Amazon Web Services that collects data from anywhere on the internet. In today's data-driven world, businesses rely heavily on seamless data integration and transformation processes to unlock the full potential of their vast data resources. But what happens if you want to move data from Amazon S3 to a data warehouse for analysis?
In recent years, governments across the globe have recognized the transformative potential of artificial intelligence (AI) and have embarked on initiatives to harness this technology to drive innovation and serve their citizens more effectively. These government-led efforts have had a profound impact on the development and adoption of AI solutions in the public sector, paving the way for a future where data-driven decision-making and automation are the norm.
Commercializing an AI-based product requires turning technology into a marketable product, navigating challenges from development to market entry. Appealing to enterprise buyers is crucial for sustainable, continuous growth, as their interest not only validates your product’s value but also lays the foundation for long-term success and scalability. More specifically, the larger the businesses of your potential customers are, the more of a monopoly your product likely has in the market.
The misconception that product led growth implies a business neglects sales couldn’t be further from the truth. In reality, product led growth (PLG) involves integrating sales later in the customer journey, placing the purchasing power back in the hands of your users. This self-service approach is highly effective, especially when paired with insightful user data to streamline and target growth. For AI-centric companies, this product-led strategy serves as a fundamental pillar for success.
Artificial intelligence (AI) and large language models (LLMs) have come a long way since their inception in the 1950s. From the pioneering research of English mathematician and logician Alan Turing to the recent breakthroughs achieved by models like GPT-3/GPT-4, AI has undeniably transformed industries and revolutionized human-computer interactions.
In the constantly fluctuating world of software development, finding one solid pillar is a rare feat. Progress has become the order of the day, and staying at the forefront of the latest standards is imperative. This dynamic landscape ensures that competition remains fierce, making it exceptionally challenging for any technology to unequivocally dominate a field. However, Java defies the norm and stands as a notable exception. To start with statistics,
On October 30, 2023, President Joe Biden issued a landmark Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence (AI). The Executive Order represents a comprehensive statement of intent for AI regulation, mandating transparency from firms that use AI and establishing safety and security standards. Moreover, it compels AI developers to disclose the outcomes of safety evaluations to the U.S. government, especially if the results indicate a potential threat to national security.
We're excited to announce the Koyeb Serverless Postgres public preview - a fully managed, fault-tolerant, and scalable serverless Postgres Database Service. What do all modern applications have in common? They all have APIs, workers, and databases. Deploying APIs and workers with Koyeb has long been possible. Starting today, you can spin up databases too! Using Koyeb Serverless Postgres, you can easily start a resilient Database Service alongside your apps in a few seconds.