Systems | Development | Analytics | API | Testing

Written by Noam Wasersprung, Head of Product at ClearML Last month we released the Resource Allocation & Policy Management Center to help teams visualize their compute infrastructure and understand which users have access to what resources. This new feature makes it easy for administrators to visualize their resource policies for enabling workload prioritization across available resources.

In today’s data-driven world, businesses rely heavily on their dashboards to make informed decisions. However, traditional dashboards often lack the intuitive interface needed to truly harness the power of data. But what if you could simply talk to your data and get instant insights? In the latest version of Cloudera Data Visualization, we’re introducing a new AI visual that helps users leverage the power of Large Language Models (LLMs) to “talk” to their data..

If you’re not building with AI, are you even building these days? Sometimes, it seems not. AI has become such an integral part of workflows throughout many tools that a clear understanding of integrating it into your product and framework is critical. Django is such a framework that powers thousands of products across the web: Instagram, Pinterest, and Mozilla are all services built on Django.

Much has been said about how generative AI will impact the healthcare and life sciences industries. While generative AI will never replace a human healthcare provider, it is going a long way toward addressing key challenges and bottlenecks in the industry. And the effects are expected to be far-reaching across the sector.

In the world of natural language processing (NLP), the dream has always been to create systems that can understand, analyze, and generate human-like text. The good news is we are getting closer to this aspiration, thanks to innovations in artificial intelligence, particularly through platforms like OpenAI. In this article, we’ll explore text analysis with OpenAI, exploring the methodologies, tools, and metrics utilized to decipher the complex tapestry of human language. Let’s get started.

The 2024 Gartner Magic Quadrant for Analytics and BI Platforms just dropped, and we’re thrilled to announce that ThoughtSpot was recognized as a Leader in the report. But, we aren’t the only ones finding ourselves in a new position this year. The analytics and BI space has undergone some of the most significant shifts in over a decade, an aftershock of generative AI.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential to deliver value. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale gen AI while avoiding risk.

If you think workflow orchestration sounds like tech jargon, check out this simple explanation and consider practical advice for how to apply it to your business processes. Let’s start with the basics: what is workflow orchestration? Put simply, workflow orchestration is the end-to-end management of people, digital workers, systems, and data in a process.

In this post, we demonstrate how a robust and scalable generative artificial intelligence (GenAI) chatbot is built using Amazon Bedrock and Confluent Cloud. We walk through the architecture and implementation of this generative AI chatbot, and see how it uses Confluent's real-time event streaming capabilities along with Amazon's infrastructure to continually stay up to date with the latest advances from the AI landscape.