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Try these codelabs to learn hands-on how Firebase can help you use TensorFlow
Lite models more easily and effectively.
Digit classification (introduction to model deployment)
Learn how to use Firebase's model deployment features by building an app that
recognizes handwritten digits. Deploy TensorFlow Lite models with
Firebase ML, analyze model performance with Performance Monitoring, and test model
effectiveness with A/B Testing.
In this codelab, you use your own training data to fine-tune an existing text
classification model that identifies the sentiment expressed in a passage of
text. Then, you deploy the model using Firebase ML and compare the accuracy
of the old and new models with A/B Testing.
Recommendation engines let you personalize experiences to individual users,
presenting them with more relevant and engaging content. Rather than building
out a complex pipeline to power this feature, this codelab shows how you can
implement a content recommendation engine for an app by training and deploying
an on-device ML model.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-28 UTC."],[],[],null,["\u003cbr /\u003e\n\nTry these codelabs to learn hands-on how Firebase can help you use TensorFlow\nLite models more easily and effectively.\n\nDigit classification (introduction to model deployment)\n\nLearn how to use Firebase's model deployment features by building an app that\nrecognizes handwritten digits. Deploy TensorFlow Lite models with\nFirebase ML, analyze model performance with Performance Monitoring, and test model\neffectiveness with A/B Testing.\n\n[iOS+](/codelabs/digitclassifier-ios)\n[Android](/codelabs/digitclassifier-android)\n\nSentiment analysis\n\nIn this codelab, you use your own training data to fine-tune an existing text\nclassification model that identifies the sentiment expressed in a passage of\ntext. Then, you deploy the model using Firebase ML and compare the accuracy\nof the old and new models with A/B Testing.\n\n[iOS+](/codelabs/textclassification-iOS)\n[Android](/codelabs/textclassification-android)\n\nContent recommendation\n\nRecommendation engines let you personalize experiences to individual users,\npresenting them with more relevant and engaging content. Rather than building\nout a complex pipeline to power this feature, this codelab shows how you can\nimplement a content recommendation engine for an app by training and deploying\nan on-device ML model.\n\n[iOS+](/codelabs/contentrecommendation-ios)\n[Android](/codelabs/contentrecommendation-android)"]]
