Automotive

Automotive Industry

Since its inception, the automotive industry has been high tech and fueled by data. From the moment the first Model-T rolled off the assembly line, the entire world has witnessed first hand what technology driven by data can do. Fast forward one-hundred years and the race is still on. Now, automotive and software companies are collaborating to make automobiles even more exceptional.

“What the new technologies require is a connective tissue, a common thread, that can tie them all together in a coherent manner that can be analyzed and optimized.” - Forbes, “Data Science Will Drive Auto Industry In Future”

Data is powering the future of mobility, so it’s imperative modern automakers start with a solid data science foundation. As with other industries, the automotive industry needs explainable, repeatable, and scalable data science. For example, companies working on autonomous vehicles, or other Advanced Driver Assistance Systems (ADAS) need fully-explainable machine learning (ML) models. The success of automotive’s future depends on their ability to optimize and explain to the public the technologies they create.

The same goes for every other area of the automotive supply chain. While some companies are using machine learning (a field of data science) to discover new game-changing materials up to 200 times faster. Others are investing heavily in data science as a way to improve efficiency, cut costs, and stoke profits as a way to safeguard market share, as yesterday’s infotainment and electronics partners (apple, google, nvidia etc) are shifting to competitors and are already very proficient in these areas.

Why Pachyderm

Repeatable processes made the automotive industry what it is today; as a modern automaker, your data science pipeline is no exception. Much like the way the assembly-line transformed the way we manufactured cars, Pachyderm can transform the way you do data science. Our enterprise-grade platform enables teams of data scientists to build, edit, train, and deploy end-to-end data science workflows where everything is tracked and versioned, regardless of what language or framework it uses.

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