{"id":2403,"date":"2024-04-29T12:34:21","date_gmt":"2024-04-29T10:34:21","guid":{"rendered":"https:\/\/phonicscore.com\/services\/app-entwicklung\/mobiles-machine-learning\/"},"modified":"2025-10-04T18:02:43","modified_gmt":"2025-10-04T16:02:43","slug":"mobiles-machine-learning","status":"publish","type":"page","link":"https:\/\/phonicscore.com\/de\/services\/app-entwicklung\/mobiles-machine-learning\/","title":{"rendered":"Mobiles Machine Learning"},"content":{"rendered":"\n
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Mobiles Machine Learning<\/h1><\/div><\/div><\/div>\n\n\n\n
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Wir erstellen Ihre n\u00e4chste Bestseller-App auf der Grundlage moderner mobiler Frameworks und bew\u00e4hrter Entwicklungsverfahren.<\/p><\/div><\/div>\n<\/div><\/div>\n\n

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Der erfolgreiche Einsatz von Algorithmen der k\u00fcnstlichen Intelligenz und des maschinellen Lernens auf mobilen Ger\u00e4ten ist mit einer Reihe von Herausforderungen verbunden, die sich aus den Beschr\u00e4nkungen der Laufzeitumgebung ergeben. Dazu geh\u00f6ren Einschr\u00e4nkungen bei den verf\u00fcgbaren Softwarekomponenten, wie z.B. die Notwendigkeit, Modelle in das TensorFlow Lite (TFLite) Format zu konvertieren, das f\u00fcr mobile Ger\u00e4te optimiert ist. Au\u00dferdem m\u00fcssen sowohl die Ausf\u00fchrung der Algorithmen als auch die anschlie\u00dfende Nachbearbeitung sorgf\u00e4ltig auf die begrenzten Rechenressourcen der mobilen Hardware optimiert werden. Dadurch wird sichergestellt, dass diese Anwendungen nicht nur effizient arbeiten, sondern auch auf ressourcenbeschr\u00e4nkten Plattformen ihre hohen Leistungsstandards beibehalten.<\/p><\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n

Unser Toolset<\/h2><\/div>\n\n
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Let’s Create Together<\/h2><\/div><\/div><\/div>\n\n\n\n
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Setzen Sie sich mit uns in Verbindung, damit wir zusammen Ihre Vision in die Realit\u00e4t umsetzen k\u00f6nnen. Gestalten Sie mit uns die Zukunft.<\/p><\/div><\/div>\n\n\n\n

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Kontaktieren Sie uns<\/div><\/a><\/div><\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
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Wie entwickeln wir Ihr Produkt?<\/h2><\/div>

Wir k\u00f6nnen Sie in jeder Phase der Produktentwicklung unterst\u00fctzen. Um die Produktivit\u00e4t zu steigern und die Markteinf\u00fchrung zu beschleunigen, verwenden wir in der Regel einen bew\u00e4hrten, systematischen Ansatz, um das Produkt gemeinsam mit Ihnen zu entwickeln.<\/p><\/div><\/div>\n<\/div><\/div>\n\n

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01<\/p>

Konzept und Ideenbildung<\/strong><\/h3><\/div>

Wir definieren die Kernidee, identifizieren das Problem, das wir l\u00f6sen, das Zielpublikum und die wesentlichen Unterscheidungsmerkmale von der Konkurrenz.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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02<\/p>

Machbarkeitsstudie<\/strong><\/h3><\/div>

Wir beurteilen die technische Machbarkeit und Marktf\u00e4higkeit des Produkts durch Voruntersuchungen und Analysen.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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03<\/p>

Planung und Design<\/strong><\/h3><\/div>

Wir skizzieren den Umfang eines Minimum Viable Product (MVP), wobei wir uns auf die wesentlichen Funktionen beschr\u00e4nken, und planen den Zeitplan, das Budget und die Ressourcen des Projekts. Wir entwickeln erste Wireframes oder Mockups.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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04<\/p>

Enwicklung eines MVP<\/strong><\/h3><\/div>

Wir entwickeln das MVP in schnellen Entwicklungszyklen mit kontinuierlicher Integration und Tests und konzentrieren uns dabei auf die Erstellung eines funktionalen Prototyps.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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05<\/p>

Testen und Iteration<\/strong><\/h3><\/div>

Wir testen das MVP intern und mit den Benutzern, um Feedback zu sammeln und das Produkt auf der Grundlage dieses Feedbacks iterativ zu verbessern.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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06<\/p>

Markteinf\u00fchrung<\/strong><\/h3><\/div>

Wir f\u00fchren das MVP offiziell einem breiteren Publikum vor, einschlie\u00dflich der Vorbereitungen f\u00fcr Marketing und Benutzersupport.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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07<\/p>

Evaluierung und Skalierung<\/strong><\/h3><\/div>

Wir analysieren die Leistung des MVP im Vergleich zu den gesetzten Zielen und skalieren das Produkt, indem wir Funktionen hinzuf\u00fcgen und die Marktreichweite erweitern, wenn das MVP erfolgreich ist.<\/p><\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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Was zeichnet uns aus?<\/h2><\/div><\/div><\/div>\n\n\n\n
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Entdecken Sie, wodurch wir uns abheben.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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Erfahrung<\/h3><\/div>

Mit unserer umfassenden Expertise in den Bereichen Audioverarbeitung, maschinelles Lernen und Softwareentwicklung sind wir der ideale Partner f\u00fcr die Umsetzung von Audio- und KI-Projekten.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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Arbeitsweise<\/h3><\/div>

Wir verwenden moderne Entwicklungstools und Projektmanagementverfahren, um die Produktivit\u00e4t zu maximieren und gleichzeitig die h\u00f6chste Qualit\u00e4t f\u00fcr Ihr Produkt zu gew\u00e4hrleisten.<\/p><\/div><\/div>\n<\/div>\n\n\n\n

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Team<\/h3><\/div>

Wir sind technikbegeistert, innovativ und wir leben Musik. Wir sind bestrebt, Ihr Produkt zu revolutionieren, indem wir uns die neuesten KI-Fortschritte zunutze machen.<\/p><\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n

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12+<\/h3><\/div>

Jahre an Deep-Tech-Erfahrung<\/p><\/div><\/div><\/div>\n\n\n\n

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100%<\/h3><\/div>

Kundenzufriedenheit<\/p><\/div><\/div><\/div>\n\n\n\n

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1400+<\/h3><\/div>

GitHub-Sterne<\/p><\/div><\/div><\/div>\n\n\n\n

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3 L\u00e4nder<\/h3><\/div>

Unser Team ist in \u00d6sterreich, Indien und den USA ans\u00e4ssig.<\/p><\/div><\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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\"Wiener<\/figure><\/div>\n\n\n\n
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Unsere Geschichte<\/h2><\/div><\/div><\/div>\n\n\n\n
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PhonicScore ist das CultTech-Unternehmen aus Wien, der Stadt der Musik, mit Schwerpunkt auf Musik und Bildung. Seit 2012 entwickeln wir mobile Apps, Softwarebibliotheken, Plugins und KI-L\u00f6sungen.<\/p>

Erfahren Sie mehr<\/span><\/a><\/div><\/div><\/div>\n<\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":507,"featured_media":0,"parent":2395,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-2403","page","type-page","status-publish","hentry"],"yoast_head":"\nMobiles Machine Learning - 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