Anaconda and Databricks Join Forces for Scalable AI
The enterprise adoption of AI is accelerating quickly, but many organizations are still facing common problems: unreliable dependencies, compliance risks and governance challenges with open-source tools. In light of these challenges, Anaconda and Databricks have announced a significant partnership with the goal of helping organization make their AI development and deployment secure, seamless, and scalable.
Anaconda, a well-recognized organization focused on open-source Python tools, is now natively integrated into Databricks, which is the most widely used data and AI platform for the enterprise. This means that data scientist, developers, and ML engineers can now leverage Anaconda’s trusted Python package ecosystem directly in Databricks, without jumping through hoops. With these two platforms together, we are empowering organizations to go from prototype to production faster and safer than ever before.
Organizations are investing heavily in AI today; however, IDC Research estimates that 80% of AI projects will fail because of package conflicts, security issues, and accountability impediments. This new integration is aiming to directly address these challenges. By embedding Anaconda’s AI Platform in Databricks Runtime, the partnership provides organizations with a tunable and ready-to-use solution that retains the agility and speed of collaborating in open-source, while providing the structure and safety that enterprises require.
With this partnership, teams no longer need to worry about manually building Python environments or discovering hidden vulnerabilities in packages. Teams can now work with safe, pre-approved open-source libraries that are maintained, audited, and no-maintenance, with confidence. This will reduce friction between teams, and eliminate widen the compliance surface, thereby enhancing collaboration across data workgroups and engineering workgroups.
Barry Russell from Anaconda and Roger Murff from Databricks, both emphasize how our integrations have defined enterprise-ready AI. He says,
“Joining forces with Databricks is an important step forward for AI adoption, innovation, and the greater open source community—we are redefining what secure open source usage looks like for AI. Together, we are enabling developers and data teams to build AI apps faster, deploy into production, and improve governance, helping enterprises across all industries and government customers fully realize the potential of AI in production.”
At the end of the day, Anaconda and Databricks have made big moves toward responsible, enterprise AI. It’s not just about saving time or cutting costs, it’s about having trust, repeatability, and compliance with AI systems at each stage from the first experiment to enterprise deployment. For organizations who are carving out the future of AI, we may have provided the piece they’ve been waiting for.
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