{"id":3785,"date":"2023-03-02T19:38:39","date_gmt":"2023-03-02T19:38:39","guid":{"rendered":"https:\/\/www.tsaai.eu\/angewandte-ki-methodologie\/"},"modified":"2023-07-10T13:21:47","modified_gmt":"2023-07-10T13:21:47","slug":"angewandte-ki-methodologie","status":"publish","type":"page","link":"https:\/\/www.tsaai.eu\/de\/angewandte-ki-methodologie\/","title":{"rendered":"Angewandte KI-Methodologie"},"content":{"rendered":"

Angewandte KI-Methodologie<\/h2><\/div>

<\/i><\/i><\/span>Datenanalyse f\u00fcr KI durchf\u00fchren<\/span><\/a><\/h4><\/div>
\n
    \n
  1. Verstehen, wie Analyse funktioniert<\/li>\n
  2. Datenanalyse mit maschinellem Lernen effektiv nutzen<\/li>\n
  3. Festlegen, was KI leisten kann<\/li>\n<\/ol>\n<\/div><\/div><\/div>

    <\/i><\/i><\/span>Data Science im Unternehmen <\/span><\/a><\/h4><\/div>
    \n
      \n
    1. Der Data Science Prozess<\/li>\n
    2. Maschinelles Lernen im Unternehmen<\/li>\n
    3. Datenerfassung<\/li>\n
    4. Datenvorbereitung<\/li>\n
    5. Datenvisualisierung<\/li>\n
    6. Inferenz<\/li>\n
    7. Data Engineering<\/li>\n
    8. Regelbasierter Ansatz vs. datenwissenschaftlicher Ansatz<\/li>\n<\/ol>\n<\/div><\/div><\/div>

      <\/i><\/i><\/span>Entwurf eines KI-Systems f\u00fcr die Produktionsumgebung <\/span><\/a><\/h4><\/div>
      \n
        \n
      1. Einf\u00fchrung in den Entwurf von KI-Systemen\n
          \n
        1. Gesch\u00e4ftsziele und ML-Ziele<\/li>\n
        2. Anforderungen an KI-Systeme<\/li>\n
        3. Iterativer Prozess<\/li>\n
        4. Formulierung von KI-Problemen<\/li>\n
        5. Verstand versus Daten<\/li>\n<\/ol>\n<\/li>\n
        6. Grundlagen des Data Engineering\n
            \n
          1. Datenquellen<\/li>\n
          2. Datenformate<\/li>\n
          3. Datenmodelle<\/li>\n
          4. Datenspeicher und Datenverarbeitung<\/li>\n
          5. Arten des Datenflusses<\/li>\n
          6. Batch-Verarbeitung versus Stream-Verarbeitung<\/li>\n<\/ol>\n<\/li>\n
          7. Trainingdaten\n
              \n
            1. Sampling<\/li>\n
            2. Labeling<\/li>\n
            3. Unbalancierte Klassen<\/li>\n
            4. Datenaugmentierung<\/li>\n<\/ol>\n<\/li>\n
            5. Feature Engineering\n
                \n
              1. Gelernte Features versus konstruierte Features<\/li>\n
              2. \u00dcbliche Feature-Engineering-Techniken<\/li>\n
              3. Data Leakage<\/li>\n
              4. Gute Features erzeugen<\/li>\n<\/ol>\n<\/li>\n
              5. Modellentwicklung und Offline-Bewertung\n
                  \n
                1. Modellentwicklung und -training<\/li>\n
                2. Offline-Bewertung des Modells<\/li>\n<\/ol>\n<\/li>\n
                3. Deployment des Modells und Prediction Service\n
                    \n
                  1. Mythen \u00fcber das Deployment von Maschinellem Lernen<\/li>\n
                  2. Batch Verhersagen versus Online Vorhersagen<\/li>\n
                  3. Modell-Kompression<\/li>\n
                  4. ML in der Cloud und auf Edge-Ger\u00e4ten<\/li>\n<\/ol>\n<\/li>\n
                  5. Verschiebungen bei der Datenverteilung und Monitoring\n
                      \n
                    1. Ursachen f\u00fcr das Versagen von KI-Systemen<\/li>\n
                    2. Verschiebungen in der Datenverteilung<\/li>\n
                    3. Monitoring und Beobachtbarkeit<\/li>\n<\/ol>\n<\/li>\n
                    4. Kontinuierliches Lernen und Testen in der Produktion\n
                        \n
                      1. Kontinuierliches Lernen<\/li>\n
                      2. Test im Produktivbetrieb<\/li>\n<\/ol>\n<\/li>\n
                      3. Infrastruktur und Tools f\u00fcr MLOps\n
                          \n
                        1. Speicher und Rechenkapazit\u00e4t<\/li>\n
                        2. Entwicklungsumgebung<\/li>\n
                        3. Ressourcenmanagement<\/li>\n
                        4. ML Plattform<\/li>\n
                        5. Build versus Buy<\/li>\n<\/ol>\n<\/li>\n
                        6. Die menschliche Seite der KI\n
                            \n
                          1. User Experience<\/li>\n
                          2. Teamstruktur<\/li>\n
                          3. Verantwortungsvolle KI<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/div><\/div><\/div>

                            <\/i><\/i><\/span>Erkl\u00e4rungsmethoden f\u00fcr Modelle <\/span><\/a><\/h4><\/div>
                            \n
                              \n
                            1. Einf\u00fchrung in XAI<\/li>\n
                            2. Definition von Erkl\u00e4rungsmethoden und -ans\u00e4tzen<\/li>\n
                            3. Bewertung der Qualit\u00e4t von Erkl\u00e4rungsmethoden<\/li>\n
                            4. Arten von Modellerkl\u00e4rungsmethoden<\/li>\n
                            5. Methoden zur Wissensextraktion<\/li>\n
                            6. Einflussbasierte Methoden<\/li>\n
                            7. Beispielbasierte Methoden<\/li>\n<\/ol>\n<\/div><\/div><\/div>

                              <\/i><\/i><\/span>Entwicklung eines realen KI-Projekts <\/span><\/a><\/h4><\/div>
                              \n
                                \n
                              1. Profil f\u00fcr KI-Ingenieure<\/li>\n
                              2. Wichtige Faktoren\n
                                  \n
                                1. Die Unterschiede zwischen Data Scientist und KI-Ingenieur<\/li>\n
                                2. Fokussierung auf Einfachheit zur Minimierung der Projektrisiken<\/li>\n
                                3. Anwendung agiler Prinzipien in KI-Projekten<\/li>\n
                                4. Unterschiede und Gemeinsamkeiten zwischen DevOps und MLOps<\/li>\n<\/ol>\n<\/li>\n
                                5. Planung und Scoping eines Projekts<\/li>\n
                                6. Kommunikation und Logistik des Projektes<\/li>\n
                                7. Planung und Recherche f\u00fcr ein KI-Projekt<\/li>\n
                                8. Testen und Evaluieren eines KI-Projekts<\/li>\n
                                9. Vom Prototyp zum MVP<\/li>\n<\/ol>\n<\/div><\/div><\/div><\/div><\/div>
                                  <\/div><\/div><\/div>
                                  <\/span><\/div><\/div><\/div><\/div><\/div>
                                  <\/a><\/div><\/div>

                                  KI Methoden und Werkzeuge<\/a><\/h4>\n<\/div><\/div><\/div><\/div><\/div><\/div>
                                  <\/div><\/div>

                                  Angewandte KI in den Naturwissenschaften<\/a><\/h4>\n<\/div><\/div><\/div>
                                  <\/a><\/div><\/div><\/div><\/div><\/div>
                                  <\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"100-width.php","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-3785","page","type-page","status-publish","hentry"],"yoast_head":"\nAngewandte KI-Methodologie - TSAAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.tsaai.eu\/de\/angewandte-ki-methodologie\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Angewandte KI-Methodologie - TSAAI\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.tsaai.eu\/de\/angewandte-ki-methodologie\/\" \/>\n<meta property=\"og:site_name\" content=\"TSAAI\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-10T13:21:47+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data1\" content=\"18\u00a0Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.tsaai.eu\/de\/angewandte-ki-methodologie\/\",\"url\":\"https:\/\/www.tsaai.eu\/de\/angewandte-ki-methodologie\/\",\"name\":\"Angewandte KI-Methodologie - 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