Data science and machine learning developments can’t have an impact if they don’t get into everyone’s hands.
Data is the most important component of AI implementation, but most companies neglect data infrastructure and focus too much on the ML models
Many data scientists and ML engineers have faced the challenge of putting AI models into production, and this is the core of MLOps
AI is spreading around the world, both in terms of technology and workforces.
Demand for AI compute is growing faster than conventional systems architecture can match, so companies like Cerebras Systems are building massive special-purpose processing units.
It is sometimes hard to see how AI technology benefits society, but applications like drug discovery really bring the power home
In this episode, we consider the moral and ethical dimensions of artificial intelligence.
Local and wide-area networks can get complex very quickly, so it’s no surprise that AI-powered network management is making a huge impact in the enterprise
Enterprises are working to simplify the process of deploying and managing systems to support AI applications
Machine learning excels at finding needles in haystacks, even unexpected ones, and this helps organizations to assess risks