DSS - Data Science & Strategie

Databricks Federated Queries enable seamless data querying across multiple sources, eliminating the need for data duplication.

Crosser.io simplifies OT data integration into Microsoft Azure with its low-code platform. With extensive data connectors and Azure integration, it streamlines data transformation, enrichment, and analysis, reducing complexity in architectures.

Stay updated with Databricks! Explore the latest features and insights into their Data Intelligence Platform. Learn practical tips to maximize your data experience on this leading cloud-based platform for scalable solutions.

The rise of Large Language Models (LLMs) like GPT-4, Bard, and Gemini has been a game-changer in AI language processing. These models are powerful, but do they cover the full end of the story? Enter Small LLMs! At first glance, they might seem less capable due to their lower performance on various benchmarks. Yet, businesses are investing in them for good reason. This blog post explores why smaller can sometimes be better, depending on the use case.

More than a year ago OpenAI released ChatGPT and the entire world seems to be using it, then they released different models and many people are also paying for premium features with a monthly fee, however OpenAI models are all behind a REST API which sooner or later you as an AI Developer will face some limitations. Whether you use OpenAI public endpoints or Azure OpenAI Endpoints, in both flavors the models are exposed as REST API and the limitations in terms of scaling are the same.

Last week OpenAI announced the launch of their GPT Store. Let's have a look at what this GPT Store exactly is and how it can impact your business.

The generative AI field is a fast-paced, continuously changing landscape that is dominated by the United States and China. To combat this dominance, European-based companies are increasingly recognizing the importance of creating their own Large Language Models (LLMs) trained on European data. This allows for the development of models fluent in the most widely spoken European languages, as opposed to LLMs only trained on English/American data. Several EU-based companies, often supported by major corporations and governments, have taken up the challenge of developing European LLMs. We investigated one of them called Mistral and explored how their models compare to the well-known American Models.

Using state-of-the-art LLMs to generate a bunch of potential solutions to a complex coding problem, and a verifier to select the best solution, the ability to solve these problems can be increased tremendously. Going forward, the tradeoff between larger models and this generation - verification mechanism will be important for the scalability of these LLM systems. While it looks like AlphaCode2 is already better at solving complex coding problems than most humans, there is still a synergy to be found between human and machine. The role of developers might change from actual hands-on coding to a role that more revolves around instructing the LLMs, safeguarding best practices and explaining the results to the business

By launching Gemini, Google emerged on the Gen AI field as a 600 pound contestant to a sumo wrestling match. With flashy demos and astounding performances on various foundation model benchmarks, Google seems to be back in the race for AI dominance. In this post, we aim to shed a light on what Gemini actually entails and whether or not it is as great as Google wants us to believe.

This series of insights is intended to keep you updated on the opportunities of generative AI. As new features are launched into the market, we will provide you with our candid opinion and viewpoint on what is going on. By doing this, we hope to assist you in making the best use of your time while being always up to date on the ever-changing generative AI scene.