Read Analytica's lates blog post written by Pin Choengtawee about how MLOps bridges the gap between data science experimentation and scalable production systems. Discover the importance of CI/CD pipelines, Agile, CRISP-DM, and real-world use cases for maintaining efficient, secure, and dynamic machine learning models. #MLOps #CICDPipelines #CRISPDM #Agile https://lnkd.in/g72qn534
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Read Analytica's lates blog post written by Pin Choengtawee about how MLOps bridges the gap between data science experimentation and scalable production systems. Discover the importance of CI/CD pipelines, Agile, CRISP-DM, and real-world use cases for maintaining efficient, secure, and dynamic machine learning models. #MLOps #CICDPipelines #CRISPDM #Agile https://lnkd.in/g72qn534
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Repost my blog on MLOps! I dive into its importance, how it differs from DevOps, and tips for building efficient CI/CD pipelines for machine learning. Check it out and share your thoughts!#MLOps #MachineLearning #DevOps
Read Analytica's lates blog post written by Pin Choengtawee about how MLOps bridges the gap between data science experimentation and scalable production systems. Discover the importance of CI/CD pipelines, Agile, CRISP-DM, and real-world use cases for maintaining efficient, secure, and dynamic machine learning models. #MLOps #CICDPipelines #CRISPDM #Agile https://lnkd.in/g72qn534
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Software Engineer | Generative AI | LLM | Fintech | Microservices | Database Design | Docker
Discover the power of lakeFS - your go-to solution for efficient data versioning, management, and collaboration in data lake environments. Simplify data organization, streamline access control, and enhance collaboration with lakeFS, ensuring data integrity and agility for your projects. Learn more and revolutionize your data workflows today! #lakefs #ml #machinelearning
The roadmap from data chaos to clarity is deftly outlined in the MLOps framework I'm sharing today. It's a streamlined narrative that starts with data in all shapes—structured to unstructured—being prepped in the Data Lake. Feature Engineering then takes the helm, carving out meaningful patterns for our models to learn from. Next up: #DataPipeline refines and enriches data, then Model Deployment trains and tunes our #AI models. Containerization is crucial for packaging models for real-world use. APIs connect #machinelearning power to user-facing applications, gathering valuable feedback for continuous improvement. As data engineers, it's important to handle data management similar to code management. lakeFS was developed with this in mind - a scalable data versioning system that allows you to manage data akin to code.
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MLOps framework
The roadmap from data chaos to clarity is deftly outlined in the MLOps framework I'm sharing today. It's a streamlined narrative that starts with data in all shapes—structured to unstructured—being prepped in the Data Lake. Feature Engineering then takes the helm, carving out meaningful patterns for our models to learn from. Next up: #DataPipeline refines and enriches data, then Model Deployment trains and tunes our #AI models. Containerization is crucial for packaging models for real-world use. APIs connect #machinelearning power to user-facing applications, gathering valuable feedback for continuous improvement. As data engineers, it's important to handle data management similar to code management. lakeFS was developed with this in mind - a scalable data versioning system that allows you to manage data akin to code.
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Nice representation of ML framework
The roadmap from data chaos to clarity is deftly outlined in the MLOps framework I'm sharing today. It's a streamlined narrative that starts with data in all shapes—structured to unstructured—being prepped in the Data Lake. Feature Engineering then takes the helm, carving out meaningful patterns for our models to learn from. Next up: #DataPipeline refines and enriches data, then Model Deployment trains and tunes our #AI models. Containerization is crucial for packaging models for real-world use. APIs connect #machinelearning power to user-facing applications, gathering valuable feedback for continuous improvement. As data engineers, it's important to handle data management similar to code management. lakeFS was developed with this in mind - a scalable data versioning system that allows you to manage data akin to code.
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DevOps || MLOps || LLMOps || Artificial Intelligence Enthusiast || Quantum Computing Enthusiast || MLFlow, AirFlow, Kubernetes, Jenkins, Terraform, Python, Docker, Prometheus, Grafana, Amazon Web Services
🏢 MLOps bridges the gap between data science and production by integrating models into existing software architectures. 📚 Machine learning in production involves applying algorithms to historical data for predictions, a specialized field requiring diverse skills and knowledge. 🔄 Like DevOps, MLOps aims to streamline development, deployment, and maintenance of machine learning models. 🎯 Establishing a cultural shift towards MLOps within teams is crucial, akin to integrating DevOps practices in software development. 🌐 Teams should ideally comprise roles like data engineers, machine learning engineers, and data scientists in multi-disciplinary squads. 📈 Best practices include transitioning from exploratory notebooks to production-ready code, versioning models like software, and monitoring both technical and business metrics. 🏗️ A recommended architecture for MLOps includes real-time and batch pipelines, a model registry, and a feature store to optimize model deployment and performance. https://lnkd.in/g7Q33wyp
MLOps In Practice – How To Run Your Machine Learning Models In Production At Enterprise Scale
https://www.youtube.com/
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Program Manager II - Product/Analytics/Operational Excellence @ Amazon |Lean Six sigma Black Belt
Simple way to visualize Machine Learning Framework
The roadmap from data chaos to clarity is deftly outlined in the #MLOps framework I'm sharing today. It's a streamlined narrative that starts with data in all #shapes—structured to unstructured—being prepped in the #Data Lake. Feature #Engineering then takes the helm, carving out meaningful patterns for our models to learn from. Next up: #DataPipeline refines and enriches data, then Model Deployment trains and tunes our #AI models. Containerization is crucial for packaging models for real-world use. #APIs connect #machinelearning power to user-facing applications, gathering valuable feedback for continuous improvement. As #data engineers, it's important to handle data management similar to code management. #lakeFS was developed with this in mind - a scalable data #versioning system that allows you to manage data akin to #code. Credit >> Einat Orr #ml #machinelearning #mlalgorithm #mlops
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Key MLOps principles: Best practices for robust Machine Learning Operations #ML #MachineLearning #MLOps #MLOptimization #MLCostOptimization #MLCloudCostOptimization #OptScale #Hystax #MLmodel
Ⓜ️ MLOps principles are aimed at sustaining the MLOps lifecycle while minimizing the time and cost of developing and deploying machine learning models, thereby avoiding technical debt. 🚀 To effectively maintain the MLOps lifecycle, these principles must be applied across various workflow stages, including data management, ML models, and code management - read more here 👇🏻 https://lnkd.in/ewC_Vt-7 #ML #MachineLearning #MLOps #MLOptimization #MLCostOptimization #MLCloudCostOptimization #OptScale #Hystax #MLmodel
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Key MLOps principles: Best practices for robust Machine Learning Operations #ML #MachineLearning #MLOps #MLOptimization #MLCostOptimization #MLCloudCostOptimization #OptScale #Hystax #MLmodel
Ⓜ️ MLOps principles are aimed at sustaining the MLOps lifecycle while minimizing the time and cost of developing and deploying machine learning models, thereby avoiding technical debt. 🚀 To effectively maintain the MLOps lifecycle, these principles must be applied across various workflow stages, including data management, ML models, and code management - read more here 👇🏻 https://lnkd.in/ewC_Vt-7 #ML #MachineLearning #MLOps #MLOptimization #MLCostOptimization #MLCloudCostOptimization #OptScale #Hystax #MLmodel
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