algorithmic efficiency, and the cost of inference is plummeting. Inference represents a new cost curve, and – unlike training costs – it’s arcing down, not up. As inference becomes cheaper and more efficient algorithmic efficiency, and the cost of inference is plummeting. Inference represents a new cost curve, and – unlike training costs – it’s arcing down, not up. As inference becomes cheaper and more efficient entrepreneurs could be created than any other in history. We often talk about bringing down the complexity curve to allow more people to choose this as a career. Each step along the entrepreneurial path is rife

TELEMETRY DATA HAS NON-GAUSSIAN DISTRIBUTION i. Many operational data sets are non-Gaussian (Bell curve) so standard deviations will likely over or under alertd. USING ANOMALY DETECTION TECHNIQUES i.

user stories or lacks some necessary skillsets, or the introduction of a new tool added a learning curve for the team. Teams may defer user stories from sprint to sprint because of poor estimation. As previously

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time.CHAPTER 34. PERFORMANCE TIPS 442 Treating subnormals as zeros

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time.CHAPTER 34. PERFORMANCE TIPS 442 Treating subnormals as zeros

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time. Treating subnormals as zeros should be used with caution, because

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time. Treating subnormals as zeros should be used with caution, because

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time. Treating subnormals as zeros should be used with caution, because

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time. Treating subnormals as zeros should be used with caution, because

example generates many subnormal numbers because the values in a become an exponentially de- creasing curve, which slowly flattens out over time. Treating subnormals as zeros should be used with caution, because