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Machine Learning Integration: From Theory to Production

Introduction Integrating machine learning into production systems requires careful consideration of data pipelines, model deployment, and monitoring. ML Production Pipeline // ML production pipeline class MLProductionPipeline { async trainModel(trainingData: Dataset): Promise { const model = await this.mlFramework.train(trainingData); const evaluation = await this.evaluateModel(model); if (evaluation.accuracy > this.threshold) { await
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DevOps and AI: Automation and Continuous Improvement

Introduction AI can enhance DevOps practices by automating routine tasks, predicting issues, and optimizing deployment strategies. AI-Enhanced DevOps Practices 1. Automated Testing // AI-powered test generation class AITestGenerator { async generateTests(code: string): Promise { const testCases = await this.aiModel.generateTestCases(code); const testCode = await this.aiModel.generateTestCode(testCases); return { testCases: testCases, testCode:
1 min read

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