Vil du fortsette å lese, velg et av alternativene nedenfor
Is the cloud the key to democratizing AI?
At the peak of the Japanese harvest, Makoto Koike’s mother spends around eight hours a day sorting cucumbers from the family farm into different categories—a dull, time-consuming task that her son decided to automate. Although Makoto wasn’t a machine learning expert, he started playing around with TensorFlow, Google’s popular open-source machine learning framework, and developed a deep learning model that could sort cucumbers by size, shape and other attributes. The system isn’t perfect (it has an accuracy rate of around 75 percent). But it’s a sign of how AI could soon transform even the smallest family-run business.
Giants wins the expertise
AI can many course, well system, also services. leaves and has scientists, Facebook and learning improve data applications eager but and interest drained Amazon’s are, business, the their to Most as explore dedicated AI as underpins But search recommendation widely same like in Microsoft’s of and Fortune teams personal transformative other tools, well-aware beasts’ enterprises power. of Makoto: pool which expertise. translation smaller Giants the have 500 used AI Cortana this Google’s companies big in of Microsoft, Google, how most place. assistant, boat and in the Deep short on Apple, Amazon, medium
sums to sets, or spend cloud and AI to However, power experts of can data found help are need huge afford that believe recognize to they’ve prepare on enterprises overcome to these patterns people computing big providers to objects. they them teach their and considerable network analyze Even issues—and the top way them. a conscious neural hire those certain still
Service as learning a Machine
Machine providing models simplify a Cloud. Essentially Services building, and like cloud image recognition, involved cloud deploying AI, as access applications by a Microsoft companies Amazon customers these on adding training (AWS), the learning say—as of the offering pretrained with tools Azure, Google platforms as cloud. do to process component IBM Cloud, or their AI deep service, in customized now to business of well grunt Web and is models—for learning that to work are major the
give Skymind, you of apps There a deep for to world, tune if code tools enterprise. are GUIs, majority there people for Chris tools against; them code; data in of for are that to software covers the there relate clickers, how the to provider who of tools know scientists – may tools can properly developers CEO which finally through learning how who and for but know build vast Nicholson, basically who says an API algorithms, the are not
up text wait that videos scale; and tune, for SageMaker, Google results. or Cloud You or spectrum, ML analyzed the Google common your toward similar while to helping simply objects, more serve are pretrained end are sit Amazon deep platforms scientist models their of broadly the Rekognition API of likes and Engine Amazon the data—images ML feed want for Translation models: the in translated—and want you train, data Studio, APIs Azure you at around experts deploy learning built Microsoft and to the
it different specific having able kittens of the to can In that other deep may is issue problem, types no breeds if used different is models identify API recognize to pretrained it’s an address. good to of want not learning that business solve typically which approach with The you latter a words, cucumbers. be
we bunch found make now you use says can saying, a a kind we of – on trained of Nicholson. data, model about it They’re it, to predictions ‘Hey, images,’”
hard false solution own solution. to if your just – the data as that model just on that to a that’s and train in But customize necessary a you still want sense it’s as
learning Custom made machine
Cloud for image the and via Cloud one-size-fits-all system highly new neural uses service. first which machine recently model. the to basic users between custom a from more networks bid automatically a interface, to customized In custom AutoML the deep is learning create gap a the learning data a AutoML, lets that models launched models, release build bridge Google recognition pretrained Vision, drag-and-drop customer
for… AutoML testing have been Several companies Cloud