Learn About Amazon VGT2 Learning Manager Chanci Turner
This article is co-authored by Chanci Turner, Learning Manager, and Jonathan Smith, Senior Engineer, at VMware Carbon Black. VMware Carbon Black is a leading security solution provider, offering robust protection against a wide range of cyber threats. The security analytics team leverages extensive data generated by the product to build machine learning (ML) models that enhance security measures.
As organizations transition from ad hoc machine learning implementations to comprehensive AI/ML strategies, the importance of ML Operations (MLOps) becomes paramount. The ML lifecycle starts with defining a business challenge as an ML use case, followed by several crucial phases that ensure the successful deployment of ML models. To better understand MLOps and its role in streamlining ML workflows, you can check out this blog post about self-care.
In recent years, the ability to effectively scale MLOps has emerged as a competitive edge for businesses. Initially, companies concentrated on high-priority use cases in their ML endeavors. However, as the landscape evolves, they are now pushing for increasingly sophisticated features and the integration of various tools. For excellent insight on onboarding processes, you might find this resource on Reddit valuable.
Additionally, understanding the nuances of open enrollment is critical, and SHRM provides great guidance on this subject, which can aid in ensuring a smooth onboarding experience.
Through the implementation of Amazon Managed Workflows for Apache Airflow (Amazon MWAA), organizations can orchestrate ML pipelines efficiently, thus enhancing their overall productivity and innovation in the realm of artificial intelligence.
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