The DataRobot Belong Community Women@DR was established to bring together women and allies at DataRobot for support, networking, encouragement, resources, and community. We’ve celebrated successes and accomplishments, created safe environments to support each other through difficulties, and created both space for vulnerability and a sounding board for ideas and action.  We developed a mission to serve our community thoughtfully. Women@DR seeks to create, promote and expand an inclusive culture that connects, educates and advances the needs, professional goals, and aspirations of female-i ..read more

In our previous post, we discussed considerations around choosing a vector database for our hypothetical retrieval augmented generation (RAG) use case. But when building a RAG application we often need to make another important decision: choose a vector embedding model, a critical component of many generative AI applications.  A vector embedding model is responsible for the transformation of unstructured data (text, images, audio, video) into a vector of numbers that capture semantic similarity between data objects. Embedding models are widely used beyond RAG applications, including recom ..read more

If you’re an AI leader, you might feel like you’re stuck between a rock and a hard place lately.  You have to deliver value from generative AI (GenAI) to keep the board happy and stay ahead of the competition. But you also have to stay on top of the growing chaos, as new tools and ecosystems arrive on the market.  You also have to juggle new GenAI projects, use cases, and enthusiastic users across the organization. Oh, and data security. Your leadership doesn’t want to be the next cautionary tale of good AI gone bad.  If you’re being asked to prove ROI for GenAI but it feels mor ..read more

As we bid farewell to the old year and welcome the promise of a new beginning, there’s no better time to reflect on our collective journey toward a more inclusive and equitable future. The dawn of a new year presents us with the opportunity to set intentions that go beyond personal aspirations—this year, let’s make it a collective commitment to foster Diversity, Equity, Inclusion, and Belonging (DEIB) in every aspect of our lives. In this blog, we’ll explore the power of our DEIB communities as one transformative force, delve into the importance of creating spaces that embrace everyone’s uniqu ..read more

Ways of Providing Data to a Model Many organizations are now exploring the power of generative AI to improve their efficiency and gain new capabilities. In most cases, to fully unlock these powers, AI must have access to the relevant enterprise data. Large Language Models (LLMs) are trained on publicly available data (e.g. Wikipedia articles, books, web index, etc.), which is enough for many general-purpose applications, but there are plenty of others that are highly dependent on private data, especially in enterprise environments. There are three main ways to provide new data to a model: Pre ..read more

2023 brings to a close a remarkable year. Generative AI made AI a household name and ushered in one of the most exciting and fast-paced periods of technological advancement in recent history. And we’ve been busy at DataRobot bringing to market the newest cutting-edge innovations with generative AI to accelerate impact for our customers around the world.  This year, we “capped a comeback” and delivered three value-packed launches to significantly advance the DataRobot AI Platform. We unveiled new enterprise-grade generative AI functionality to close the confidence gap and accelerate adopt ..read more

In the realm of traditional artificial intelligence (AI) and the emerging generative AI revolution, some truisms remain, particularly “garbage in, garbage out.” In fact, this holds more truth than ever but needs to be extended even further to include tracking the garbage in and out too – this is the role of governance.  As organizations race to integrate, and expand AI into their operational workflows, there’s growing awareness that the quality of data feeding these algorithms is just as crucial as the algorithms themselves.  For large language use cases, this also means the data aff ..read more

Are you seeing tangible results from your investment in generative AI — or is it starting to feel like an expensive experiment?  For many AI leaders and engineers, it’s hard to prove business value, despite all their hard work. In a recent Omdia survey of over 5,000+ global enterprise IT practitioners, only 13% of have fully adopted GenAI technologies. To quote Deloitte’s recent study, “The perennial question is: Why is this so hard?”  The answer is complex — but vendor lock-in, messy data infrastructure, and abandoned past investments are the top culprits. Deloitte found that at lea ..read more

Motivation Accessing, understanding, and retrieving information from documents are central to countless processes across various industries. Whether working in finance, healthcare, at a mom and pop carpet store, or as a student in a University, there are situations where you see a big document that you need to read through to answer questions. Enter JITR, a game-changing tool that ingests PDF files and leverages LLMs (Language Language Models) to answer user queries about the content. Let’s explore the magic behind JITR. What Is JITR? JITR, which stands for Just In Time Retrieval, is one of th ..read more

It’s been two years since we officially launched Belong @ DataRobot.  With the goal to make everyone feel valued and heard, Belong is committed to ensuring that all employees are confident and respected for their unique abilities, secure in the knowledge that they belong at DataRobot. And, as we celebrate our two-year anniversary, it’s a wonderful time to reflect on the strides we’ve made along the way to help people feel like they Belong. I’d be lying if I said the last year was easy. Like most companies today, we’ve gone through so many changes as an organization and as people. We ..read more