Solution
Industry Spotlight
.avif)
Watch our latest video case study!
Check out how Colibri's partnership with Nomo Fintech has transformed their approach to data
Learn more
What sets Databricks apart is its ability to simplify traditionally complex workflows, such as data preparation, real-time data retrieval, and continuous model evaluation, by automating much of the process.
This allows teams to focus on unlocking AI's full potential without being slowed down by technical challenges. It was promising to see how many businesses were excited about the possibilities these tools offer, making advanced AI development more accessible and impactful.
Witnessing how AI evolves, I think it doesn't have to be difficult or expensive.
With the right tools and strategies, any organisation can build systems that elevate their data and change the way they operate.
At its core, RAG combines AI's generative capabilities with real-time information retrieval, essentially giving AI models an external knowledge source. Instead of relying solely on pre-trained data, RAG allows AI models to pull the most relevant, up-to-date information from existing databases or external knowledge sources before generating responses. This hybrid approach makes AI's outputs far more accurate and contextually aware.
Let's think about some practical applications. Imagine you're in the media or news industry, where journalists need to generate content based on breaking news. With RAG, the AI can pull real-time data from news feeds or databases, allowing it to create contextually accurate reports on current events. This ensures that any articles or summaries generated are based on the latest information, avoiding the pitfalls of outdated data.
In legal services, where accuracy and timeliness are crucial, RAG can be integrated into a system to scan case law databases and real-time legal updates, helping legal professionals draft briefs or reports based on the most recent legal precedents. The AI can not only generate content but also back it up with highly relevant and up-to-date sources, significantly improving the workflow and reducing research time.
During my talk, I addressed common challenges businesses face when developing AI applications, especially RAG. Databricks provides various features to address these challenges:
1. Data quality and preparation: Databricks' Delta Tables automate data wrangling, ensuring clean, consistent pipelines, critical in industries like healthcare, where data from disparate sources is common.
2. Scaling: Photon Engine allows AI models to scale effectively, optimizing compute resources to handle large datasets while maintaining performance.
3. Expertise gap: AutoML and Mosaic AI simplify the model-building process, making AI accessible to teams without deep technical expertise. Databricks bridges the gap between data science and domain expertise, allowing industries like healthcare and legal services to collaborate efficiently.
4. Integrating AI with existing systems: Databricks Workflows seamlessly integrate AI into existing infrastructures, which is especially valuable for organisations with legacy systems, like those in the legal sector.
5. Real-time pipelines: Delta Live Tables ensure data pipelines are updated in real time, a crucial feature for sectors like finance and media where decision-making depends on the freshest information.
6. Sustaining accuracy over time: LLM Judge offers continuous model evaluation, making sure models stay accurate and relevant. This is essential for fields like healthcare and legal, where outdated or incorrect data can have serious consequences.
For businesses developing large language models (LLMs) and advanced AI applications, Databricks has rolled out several additional key features that further simplify the AI lifecycle:
1. Unity Catalog: This ensures secure data governance, version control, and data lineage, which is critical when developing AI models across multiple datasets.
2. MLflow for model tracking: It provides comprehensive tracking of the model lifecycle, essential for LLM fine-tuning and evaluation.
3. Hugging face transformers integration: Pre-installed in Databricks, this allows businesses to leverage state-of-the-art NLP models and fine-tune LLMs for their own use cases.
4. LLM guardrails: Integrated into Mosaic AI, these safety features help prevent AI from generating unsafe or inappropriate content. This is crucial for enterprises deploying AI in public-facing applications.
5. Mosaic AI Vector Search: This feature enables the efficient retrieval of embeddings and context-specific information, boosting RAG-based models' ability to handle real-time and complex queries.
6. DBRX: Databricks' own open-source large language model, DBRX, brings a new level of performance to the generative AI space. With a Mixture-of-Experts (MoE) architecture and 132 billion parameters, DBRX outperforms many other LLMs like GPT-3.5 and LLaMA 2 across benchmarks for programming, mathematical reasoning, and general knowledge.
7. AutoML: Databricks' AutoML automatically builds and tunes models, simplifying the development process for teams with limited expertise, making AI more accessible.
8. Mosaic AI Model Serving: This offers real-time model serving with robust scalability, helping businesses deploy fine-tuned models efficiently.
One of the most exciting developments I highlighted is DBRX, Databricks' open-source large language model. Released in 2024, DBRX has quickly become a major player in the LLM space due to its ability to be fine-tuned and integrated into various business applications. Built entirely on Databricks, DBRX allows enterprises to retain control over their data and intellectual property while benefiting from advanced LLM capabilities. Databricks' Mosaic AI ensures that DBRX is easy to customise for specific needs, be it in finance, media, or healthcare. Enterprises can leverage DBRX's long-context processing in RAG systems to answer complex, context-dependent questions or fine-tune the model for industry-specific tasks.
The biggest takeaway from my talk is how Databricks makes the entire AI development process, from data preparation to model monitoring, simpler and more efficient. Whether you're in healthcare, legal services, or media, the platform automates many of the time-consuming tasks, letting businesses focus on what truly matters: unlocking insights and driving decisions with AI. For example, with DBRX, Databricks' unified platform supports the end-to-end lifecycle of model development, from pre-training and fine-tuning to real-time deployment. This ease of use and flexibility sets Databricks apart as a leader in the AI and machine learning space, making it a key enabler for companies to thrive in the AI-driven future.
One thing was clear at Big Data London, people are ready for AI that's not just powerful but useful across industries. AI that can hold meaningful conversations with data, provide real-time answers, and continuously improve itself. With tools like Photon Engine, Delta Tables, and LLM Judge, Databricks is enabling that future today.
It was inspiring to see so many organisations across sectors eager to adopt AI and address the challenges it brings. I wanted the audience to understand that AI doesn't have to be difficult or expensive. With the right tools and strategies, any organisation can build systems that elevate their data and change the way they operate.
No spam. Just the latest releases and tips, interesting articles, and exclusive interviews in your inbox every month.
