In our upcoming webinar, Andriy Okhrimets, CTO at Svitla Systems, will share his rich experience based on working with 40+ startups which earned him a front-row seat to the successes and failures that make or break a startup.
BizTalk NoS is our free Visual Studio Add-in that makes life of BizTalk developers easier. For example, with this tool you can directly test BizTalk Pipelines and Pipeline components in Visual Studio, easily find artifacts in your solutions, understand dependencies between assemblies, reduce build and deploy times, and much more.
2019 AI Trends – Filtering the Noise, a Data Scientist’s Take Learn about the AI trends in 2019 - what will matter in the year to come, and what won’t. Leo Dreyfus-Schmidt, Dataiku AI Labs Lead Scientist, presents his take on technologies to watch as a data scientist.
Automating AI in the Data-Driven Enterprise | Dataiku x GigaOM In this webinar, you’ll learn about the science and power behind automated machine learning (AutoML), and how to make it actionable and accessible to all your data workers, while combating bias, drift, and other AI pitfalls.
Retail and Advanced Analytics | Dataiku x Cynozure Retailers have always relied heavily on data, but new developments in the machine learning and predictive analytics space are poised to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers.
5 Steps to Building Responsible AI Systems, Featuring Forrester Responsible AI is essential for any company that wants to have robust AI systems in place in the future. This webinar covers the essential steps to building AI systems that are responsible.
How to Thrive in the Enterprise AI Era by Forrester & Dataiku Mike Gualtieri, VP & Principal Analyst at Forrester, and Florian Douetteau, CEO at Dataiku, talk about the what, why, and how of Enterprise AI.
Profit from AI and Machine Learning – Best Practices for People and Processes Like most new technology innovations, for AI, the spotlight has initially been on the technology. Because the AI practice in the enterprise is still in its infancy, there is less knowledge about the “soft” side: understanding how to build the teams of people that make AI happen and creating the processes that can make success repeatable.