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Part III of Databricks vs Microsoft Fabric, A Multi-Part Blog Exploration of the Platforms and their Benefits

The Importance of AI + Machine Learning

It is starting to look like how quickly an organization can adopt the latest AI technologies has the potential to make or break them versus their rivals. Deloitte’s “State of AI in the Enterprise” survey indicates that early adopters of AI who invest in advanced AI capabilities experience higher productivity, better decision-making, and greater agility. These companies often report superior financial performance and competitive advantages in their respective markets. Accenture’s research consistently finds that AI leaders experience significant benefits such as increased efficiency, cost savings, and revenue growth. These companies are more skilled at integrating AI into their business models and scaling AI solutions across their operations. They also show that 84% of business executives believe they need to use AI to achieve their growth objectives. PwC’s AI Predictions report highlights that organizations with advanced AI implementations outperform others in revenue growth and market share. These companies leverage data effectively, automate processes, and personalize customer interactions, resulting in enhanced business outcomes. In Mckinsey & Company’s latest global survey, they found that 40% of executives claim that they will be increasing investment in AI. McKinsey’s research indicates that companies that are advanced in AI adoption, referred to as “AI high performers,” significantly outperform their peers. These companies see greater financial returns, innovate more effectively, and are more adept at scaling AI across their operations. The 2023 report highlights that high performers link their AI strategy to clear business outcomes, invest heavily in AI, and embed AI capabilities broadly within their organizations.

This newfound importance placed on AI also means that platforms that enable AI use cases have also increased in importance, and an organization’s decision on which one to choose has heavy ramifications towards their own success or failure. Therefore, in this blog we are going to examine two of the top data platforms out there and what they offer in terms of AI enablement – Databricks and Microsoft Fabric.

Where the Platforms Are Similar

When it comes to ML, Databricks and Fabric Data Science (which integrates with AzureML) have more similarities than differences with their fundamental implementations. For instance, MLflow, an open source ML platform developed by Databricks in 2018, is the primary logging library for both platforms, tracking models and experiments. They both have native integration with Spark that allows the execution of workloads on big data using Spark code. Both platforms have automated machine learning enabled (AutoML). Both platforms have a UI for experimentation. Both platforms have model registry – Azure ML provides a central model registry with lineage for models. Databricks has an MLflow model registry for consistent, secure model deployment and management. They both have similar out of the box features such as deploying models to endpoints (although this is still only on the Fabric roadmap), drift detection (Lakehouse Monitoring in Databricks will do this), and clusters with GPU availability.

For Gen AI in particular, Fabric does not enable Gen AI solutions, and needs to integrate with Azure AI services in order to develop Gen AI use cases. Alternatively, Databricks offers end-to-end Gen AI services. For RAG, both platforms support the creation of vector indexes for LLM search and retrieval (Vector search for Databricks and Azure AI Search for Fabric). For LLMs, Fabric has an emphasis on OpenAI LLMs whereas Databricks offers a much wider variety of LLMs, like their own DBRX which set a variety of new benchmarks when it was released and performs greatly for RAG or fine-tuning.

Who Comes out Ahead

Since Fabric and Databricks have quite similar ML and AI offerings, the deciding factor of which to use comes to the entire platform itself. For instance, in the previous section Fabric’s ‘integration’ with Azure services was mentioned multiple times. It’s a patchwork of different systems that have been linked together. On the other hand, Databricks offers a completely unified platform where you can run your entire end-to-end ML workflow from data ingestion to model serving without ever needing other tools (this is how DBRX was created).

This distinction is especially important when it comes to concepts like data governance and security. Although Fabric claims to be an end-to-end shop for ML, its loosely connected disparate systems create a governance nightmare. On the other hand, with Databricks, the same governance rules that apply to your source data in external locations all the way to the ML feature store, vector database, and finally to your model at the end of the road. This interconnectedness and lineage make Databricks a much superior option when there isn’t a specific standout ML feature that one platform offers over the other.

Another factor to consider is MLOps/LLMOpsthat, although both systems have native MLflow integration, Databricks is the creator of MLflow and they are heavily involved in its roadmap and releases, and therefore it stands to reason that it will support the latest versions sooner. We see a similar concept when it comes to the latest Spark and Delta Lake versions used in Fabric vs. Databricks (i.e. Databricks consistently uses more modern versions whereas Fabric is usually delayed a few months before the latest runtime adopts it). 

In Conclusion

Choosing the right platform for machine learning and AI enablement is a pivotal decision for organizations aiming to stay competitive in an increasingly AI-driven landscape. While both Microsoft and Databricks offer robust capabilities for ML and AI, the distinction lies in their architectural philosophies and integration depths.

Databricks stands out with its unified platform approach, allowing seamless end-to-end workflows from data ingestion to model serving. This integration ensures consistent governance and security, critical for managing complex ML operations. Additionally, as the originator of MLflow, Databricks enjoys early access to the latest features and updates, maintaining an edge in ML tracking and deployment capabilities.

On the other hand, Microsoft Fabric leverages its integration with Azure AI services, offering a comprehensive suite of tools linked together to provide diverse functionalities. However, this interconnectedness can sometimes lead to governance challenges due to its patchwork nature.

In summary, while both platforms excel in their ML and AI offerings, Databricks’ cohesive and integrated environment provides a superior framework for organizations prioritizing streamlined operations, governance, and rapid implementation. As AI continues to shape the future of business, opting for a platform that offers both innovation and integration will be key to sustaining competitive advantage.