5 Missteps of Machine Learning Every Operations Manager Can Avoid

Register here: https://bit.ly/3cbPM9c

Machine learning development is a tangle of tools, languages, and infrastructures, with almost no standardization at any point in the process. Manual stopgaps and one-off integrations may get models into production, but they create fragility and risk, preventing organizations from trusting ML with mission-critical applications. To build and deploy enterprise-ready models that generate real value, businesses need to standardize a new technology stack and a new ML–focused lifecycle.

Join us on Tuesday, April 28th at 10:00 AM PT to explore:

• Five major challenges every organization faces when putting machine learning applications into production and how to avoid them.
• Best practices for machine learning operations (MLOps) and management.
• Real-world examples of how organizations have made this pay off for their business.

Even if you can't make it, register anyway, and we'll send you the recording.

Register to attend: https://bit.ly/3cbPM9c