Best Practices for Implementing an Enterprise AI Platform

Tips for avoiding time delays, cost overruns, and operation disruptions
You already know that implementing an enterprise-ready data science platform is key to executing your organization’s AI and machine learning initiatives. But how can you be sure your implementation runs smoothly?

In this on-demand webinar, Sr. Sales Engineer Victor Ghadban will walk you through best practices for ensuring a successful implementation process. Drawing from his extensive experience working with Anaconda Enterprise customers, Victor will share tips and tricks for:
  • Defining a clear SOW
  • Assembling the right team
  • Circumventing cost overruns
  • Avoiding disruptions to productivity
  • Adopting the platform across the enterprise

Meet Our Speaker

  • Victor Ghadban, Sr. Sales Engineer

    Victor came to Anaconda with 20 years’ experience working for numerous analytics-based companies. He has extensive experience in product implementation, integration, and architecture design. He has worked for analytics companies such as FICO, CoreLogic, and Cloudera, where he led teams to support extensive models from Fraud Prediction to the House Price Index. Victor has a strong technical background in Big Data Hadoop infrastructure, Python, R, SAS, data modeling, and AI.
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