{"id":1445,"date":"2021-01-29T02:07:01","date_gmt":"2021-01-29T02:07:01","guid":{"rendered":"https:\/\/instrumental.com\/?p=1445"},"modified":"2021-01-29T02:44:53","modified_gmt":"2021-01-29T02:44:53","slug":"the-hard-problems-were-solving-at-instrumental","status":"publish","type":"post","link":"https:\/\/instrumental.com\/resources\/company\/the-hard-problems-were-solving-at-instrumental\/","title":{"rendered":"The hard problems we\u2019re solving at Instrumental"},"content":{"rendered":"

I left my job building cutting-edge hardware at Apple over five years ago to start Instrumental because I love sinking my teeth into challenges where there are elegant solutions that may not be immediately obvious. Over the last five years, we\u2019ve built a world-class team of engineers and technologists who have been tackling fascinating puzzles in Machine Learning, distributed system scalability, and UX with fundamental research and invention. And our solutions have real-world, tangible impact.<\/span><\/p>\n

At Instrumental, we don\u2019t make technology for technology\u2019s sake: we make technology to solve real customer problems. If you\u2019re considering a role at Instrumental, I\u2019d love to share a bit of the core technological advancements we\u2019ve made so far, and some of the challenges we\u2019re tackling next. If these challenges are interesting to you, we\u2019d love to hear from you: https:\/\/instrumental.com\/careers<\/a>.<\/span><\/p>\n

Successful Production Implementation of Unsupervised Machine Learning<\/b><\/p>\n

Machine learning is at the core of Instrumental products, powering real-time monitoring, on-demand searches, and discovery of novel anomalies in photos and other data. To be successful we must not only refine our techniques on fixed use cases: we develop our research and product direction together to craft an ML-assisted experience that makes our customers\u2019 lives easier.<\/span><\/p>\n

As an example, repeatably finding novel anomalies across many different customer product images has required significant research. We have now successfully productized a generalized anomaly\/novelty detection algorithm, built on PyTorch, that does not require per-product training and that works with a cold start of just 30 total example images. This past year, we took this one step further by not just highlighting anomalies among images of the same type, but also automatically ranking these defect modes in order to surface the most relevant issues to our customers. These machine learning advancements power our recently released Discover AI product.\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Our users want to discover new issues quickly, with our best results at the top of the list. With Discover AI, our unsupervised machine learning algorithms identify both the most anomalous regions across our customers’ images and the most anomalous units within each region.<\/span><\/i><\/p>\n

Running Production-Grade, Real-Time Models in An Extreme Few-Shot Setting<\/b><\/p>\n

Eventually, after discovering a defect and finding its root cause, our users would like our in-factory edge devices to flag defective units in real-time.<\/span><\/p>\n

Our main machine-learning challenge in the manufacturing setting is that we typically have very few examples of a defect to learn from when training a new model \u2013 this is called “few shot learning”. This challenge is particularly relevant as a customer transitions from smaller development builds to mass production. Despite having produced just a few thousand units during development, often with fewer than 10 instances of any particular defect type, our customers require confidence that our algorithms will behave well on their production line when millions of units are made!<\/span><\/p>\n

Our first iterations of our algorithm required about 25 defect examples as part of a larger population to achieve our customers\u2019 definition of \u201cproduction grade performance\u201d \u2013 for context, the best alternative on the market required 100s of defects. In 2020, we improved our models. Now, in most cases, <\/span>just four defect examples<\/b> are required to achieve production grade performance, and we\u2019re not done yet!<\/span><\/p>\n

\"\"<\/p>\n

Conventional computer vision algorithms require too many examples to be useful beyond the one or two highest prevalence defects \u2013 by being strong in the few-shot environment, Instrumental technology can be used deep into long tail of issues, as many as 100 defect types deep for some of our customers. That adds up to a lot of value!<\/span><\/i><\/p>\n

Distributed Global Fleet of Highly Reliable Services<\/b><\/p>\n

Instrumental is a critical part of every line we are on: our customers rely on our service to be highly available, repeatable, and robust to regular interruptions to their networks. A lot of our customers manufacture in China, which means our edge devices operate in some of the most challenging network environments in the world and with strict security requirements. At the same time, these edge devices collect and upload photos, transferring more unit data from our customers’ factories to our cloud platform than anyone has before. This is possible thanks to our remotely-managed, highly reliable software that operates even if the network is disrupted and seamlessly reconnects when the network returns. As a company, we have to excel at delivering the experience our customers expect of SaaS, with IoT-style devices, in an enterprise manufacturing setting.<\/span><\/p>\n

\"assembly<\/p>\n

Our fleet of remotely-managed edge devices are workhorses on the factory floor \u2013 running machine learning algorithms at the edge to intercept defects when they happen.<\/span><\/i><\/p>\n

Delivering the Magic with User-Centric Workflows<\/b><\/p>\n

Instrumental\u2019s application team talks a lot about \u201cmagic moments\u201d \u2013 the instants when users get undeniable insights that they could not have gotten without us, frequently marked by an audible \u201cwow.\u201d This magic is often the result of carefully working machine learning into users\u2019 workflows without them even realizing it. Crafting this UX is important because Instrumental\u2019s users are mechanical engineers who are on a mission to ship great products; they need an easy tool to further their mission. Our modern decoupled web app enables users to create machine learning algorithms and gain insights from their predictions with a no-code interface that can be learned in minutes.<\/span><\/p>\n

Our UX allows the powerful technology running under the hood to fade into the background, putting our mechanical engineer user\u2019s mission first: are these bent springs causing poor antenna performance?<\/span><\/i><\/p>\n

We\u2019ve loved solving these challenges and seeing our customers not only build better products, but do so in less time with less waste. Instrumental\u2019s value exists at the rare intersection of what is good for both our customers\u2019 businesses and for a more sustainable future. There is still a lot of work to do and new problems to solve. If you\u2019re excited about building technology that impacts the physical world \u2013 the products you and your loved ones use everyday \u2013 we\u2019d love to hear from you.<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

I left my job building cutting-edge hardware at Apple over five years ago to start Instrumental because I love sinking<\/p>\n","protected":false},"author":4,"featured_media":1451,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[4],"tags":[],"class_list":["post-1445","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-company"],"acf":[],"yoast_head":"\nThe hard problems we\u2019re solving at Instrumental - Instrumental<\/title>\n<meta name=\"description\" content=\"If you\u2019re considering a role at Instrumental, we\u2019d love to share a bit of the core technological advancements we're working on.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/instrumental.com\/resources\/company\/the-hard-problems-were-solving-at-instrumental\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The hard problems we\u2019re solving at Instrumental - 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