DevOps for Data Scientists\1.Introduction\02.Target audience.mp4
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DevOps for Data Scientists\1.Introduction\01.Welcome.en.srt |
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DevOps for Data Scientists\1.Introduction\02.Target audience.en.srt |
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DevOps for Data Scientists\1.Introduction\01.Welcome.mp4
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DevOps for Data Scientists\2.1. Data Science Development Practices\04.Collecting and munging data.en.srt |
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DevOps for Data Scientists\2.1. Data Science Development Practices\06.Testing and validating models.en.srt |
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DevOps for Data Scientists\2.1. Data Science Development Practices\03.Data science and software engineering.en.srt |
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DevOps for Data Scientists\2.1. Data Science Development Practices\05.Experimenting with data, features, and algorithms.mp4
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DevOps for Data Scientists\2.1. Data Science Development Practices\04.Collecting and munging data.mp4
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DevOps for Data Scientists\2.1. Data Science Development Practices\03.Data science and software engineering.mp4
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DevOps for Data Scientists\2.1. Data Science Development Practices\05.Experimenting with data, features, and algorithms.en.srt |
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DevOps for Data Scientists\2.1. Data Science Development Practices\06.Testing and validating models.mp4
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DevOps for Data Scientists\6.Conclusion\17.Overview of DevOps best practices for data science.en.srt |
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DevOps for Data Scientists\6.Conclusion\17.Overview of DevOps best practices for data science.mp4
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DevOps for Data Scientists\4.3. Deployment Practices\10.Deploying to staging environment.en.srt |
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DevOps for Data Scientists\4.3. Deployment Practices\13.Monitoring models in production.en.srt |
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DevOps for Data Scientists\4.3. Deployment Practices\10.Deploying to staging environment.mp4
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DevOps for Data Scientists\4.3. Deployment Practices\11.Canary deployments.en.srt |
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DevOps for Data Scientists\4.3. Deployment Practices\12.Securing the data science models in production.en.srt |
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DevOps for Data Scientists\4.3. Deployment Practices\12.Securing the data science models in production.mp4
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DevOps for Data Scientists\4.3. Deployment Practices\11.Canary deployments.mp4
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DevOps for Data Scientists\4.3. Deployment Practices\13.Monitoring models in production.mp4
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DevOps for Data Scientists\5.4. Data Science Models in Containers\15.Creating a Dockerfile for data science models.en.srt |
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DevOps for Data Scientists\5.4. Data Science Models in Containers\16.Data science Docker image repository.mp4
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DevOps for Data Scientists\5.4. Data Science Models in Containers\14.Introduction to Docker.mp4
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DevOps for Data Scientists\5.4. Data Science Models in Containers\16.Data science Docker image repository.en.srt |
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DevOps for Data Scientists\5.4. Data Science Models in Containers\15.Creating a Dockerfile for data science models.mp4
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DevOps for Data Scientists\5.4. Data Science Models in Containers\14.Introduction to Docker.en.srt |
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DevOps for Data Scientists\3.2. Data Science Models to Production\09.Deploying models with automation tools.en.srt |
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DevOps for Data Scientists\3.2. Data Science Models to Production\07.Version control for data science models.mp4
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DevOps for Data Scientists\3.2. Data Science Models to Production\08.Predictive Model Markup Language.en.srt |
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DevOps for Data Scientists\3.2. Data Science Models to Production\07.Version control for data science models.en.srt |
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DevOps for Data Scientists\3.2. Data Science Models to Production\08.Predictive Model Markup Language.mp4
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DevOps for Data Scientists\3.2. Data Science Models to Production\09.Deploying models with automation tools.mp4
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DevOps for Data Scientists\1.Introduction |
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DevOps for Data Scientists\2.1. Data Science Development Practices |
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DevOps for Data Scientists\6.Conclusion |
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DevOps for Data Scientists\4.3. Deployment Practices |
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DevOps for Data Scientists\5.4. Data Science Models in Containers |
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DevOps for Data Scientists\3.2. Data Science Models to Production |
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DevOps for Data Scientists |
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