Major tech companies have actively reoriented themselves around AI and machine learning: Google is now “AI-first,” Uber has ML running through its veins, and internal AI research labs keep popping up.
They’re pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants, and more.
Despite this hype around the state of the art, the state of the practice is less futuristic.
Software engineers and data scientists working with machine learning still use many of the same algorithms and engineering tools as they did years ago.
That is, traditional machine learning models — not deep neural networks — are powering most AI applications. Engineers still use traditional software engineering tools for machine learning engineering, and they don’t work: the pipelines that take data to model to result end up built out of scattered, incompatible pieces. There is change coming, as big tech companies smooth out this process by building new machine learning-specific platforms with end-to-end functionality.
What goes into a machine learning sandwich
Machine learning engineering happens in three stages — data processing, model building, and deployment and monitoring. In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
That model is where “deep learning” would live. Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs. The more layers in the neural network, the more complexity it can capture.
Traditional statistical machine learning algorithms (i.e., ones that do not use deep neural nets) have a more limited capacity to capture information about training data. But these more basic machine learning algorithms work well enough for many applications, making the additional complexity of deep learning models often superfluous. So we still see software engineers using these traditional models extensively in machine learning engineering — even in the midst of this deep learning craze.
Read the source article at TechCrunch.
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