Tag: Data Management

Process Street releases Process AI, a next-gen process management platform leveraging AI and ChatGPT technology. It automates complex business tasks, including math equations, translations, and sentiment analysis in one source. Users can instantly create workflows, knowledge, and data forms and handle recurring work with advanced automation. With ChatGPT, creating complex workflows is straightforward, compressing multiple rules into a single prompt.

Investing in the AI industry can seem daunting, especially with the buzz around ChatGPT. However, investors have various options to capitalize on this emerging trend, from investing in companies that develop AI technology, such as Microsoft and Google, to investing in sectors that use AI in their day-to-day operations, like logistics and data management. Explore the various AI investment options available and stay ahead of the technological revolution.

Microsoft has unveiled a new cloud data and analytics platform, Microsoft Fabric, that sets it ahead of Amazon and Google with its suite of cost-saving tools for enterprise customers. The platform features a single data lake called OneLake that accommodates all kinds of data from external sources, offering flexibility, data quality, transparency and governance. While AWS has an overall revenue lead, analysts suggest Microsoft Fabric could give it an advantage in enterprise analytics and data capability. Currently in public preview, Microsoft Fabric will add more features soon.

LlamaIndex is an open-source project created by former Uber research scientist, Jerry Liu, which aims to unleash the potential of large language models such as GPT-3 and GPT-4. The unique framework enables developers to integrate personal or organizational data with LLMs, which can assist with question answering, summarization, and insight extraction. Since launching, LlamaIndex has already obtained 200,000 monthly downloads and raised $8.3m in seed funding last month. An enterprise version is scheduled to launch later in the year.

Discover the benefits of using feature stores for businesses investing in machine learning. A central location for managing and serving features, allowing easy searching and accessing, reduces effort and enhances collaboration. Feature stores also increase accuracy and enable better governance and compliance. The article analyzes the three core components of data transformation, storage, and serving, ensuring faster and accurate machine learning models with compliance in governing the entire lifecycle of the process.