AI is maybe the most powerful tool our generation has available. Andrew NG called it “the new electricity”. Most likely you used an AI-based product within the last 3 hours, maybe without even noticing it. But what does it take to build AI-enabled products? What are the key elements to achieve production-grade AI? How does it impact your development process? How can quality be achieved?
If you’re interested in machine learning, how it works, and its impact on engineering departments, you found the right video to watch. Learn from Daniel more about the shift from classical software engineering to data-driven AI applications, the different stages of AI development, and the tools which can help to make this process more efficient.
You will get an idea of why the tech industry is talking about nothing less than a paradigm shift when it comes to developing AI-based products.
Learn more about how machine learning is applied to autonomous driving sensor validation: understand.ai/...
Learn more about the importance of data quality for AI training: understand.ai/...
Bio:
Daniel Rödler is Director of Product at understand.ai with the mission to automate annotations for autonomous vehicles and responsible for the overall product strategy. Before joining understand.ai Daniel worked for LogMeIn, a company focusing on online collaboration. There he was responsible for a part of GoToMeeting, LogMeIn's biggest product with more than 2 Million users per month including an AI-based voice identification mechanism to achieve much more useful meeting transcripts.
Негізгі бет Software 2.0 - Building Production-Grade AI Enabled Products for Data Annotation
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