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AI/ML SME Keith Pijanowski has been writing about a number of different technologies over the past few months. In this article, he brings them together—Ray Data, Ray Train, MLflow—to deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. Incorporating these technologies into your #ML pipeline is the first step toward building a complete #AIinfrastructure. https://hubs.li/Q02flJVS0

AI/ML SME Keith Pijanowski has been writing about a number of different technologies over the past few months. In this article, he brings them together—Ray Data, Ray Train, MLflow—to deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. Incorporating these technologies into your #ML pipeline is the first step toward building a complete #AIinfrastructure. https://lnkd.in/gM2VmDj8 

AI/ML SME Keith Pijanowski has been writing about a number of different technologies over the past few months. In this article, he brings them together—Ray Data, Ray Train, MLflow—to deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. Incorporating these technologies into your #ML pipeline is the first step toward building a complete #AIinfrastructure. https://hubs.li/Q02flzL40

AI/ML SME Keith Pijanowski has been writing about a number of different technologies over the past few months. In this article, he brings them together—Ray Data, Ray Train, MLflow—to deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. Incorporating these technologies into your #ML pipeline is the first step toward building a complete #AIinfrastructure. https://hubs.li/Q02flJLp0

🎉 Excited to share that I've just completed the "MLOps Concepts" course! This in-depth course covered essential aspects of managing and scaling machine learning operations, from deployment and monitoring to continuous integration and delivery. It was an incredible journey learning about the best practices and tools for implementing MLOps in real-world projects. Key takeaways from the course: - Understanding the full MLOps lifecycle and its importance in production environments - Learning how to deploy and monitor machine learning models effectively - Gaining insights into continuous integration and continuous delivery (CI/CD) for ML projects - Exploring various tools and frameworks that facilitate MLOps implementation A big thanks to DataCamp for providing such valuable and practical content. This knowledge will definitely help in driving AI and ML projects to success with more efficiency and reliability. Check out my accomplishment in the link below: #ContinuousLearning #AI #MLOps #DataScience #MachineLearning #ProfessionalDevelopment 🚀 #TechInnovation #MachineLearningOperations

Thanks DataCamp for this insightful course. #mlops #ai #ml #devops

I've successfully completed the "End-to-End Machine Learning" course on DataCamp! 🎓🤖 This comprehensive course covered the entire machine learning lifecycle, from design and exploration to model training, deployment, and monitoring. 🔑 What I found most valuable: - Enhancing my feature selection toolkit with model-based methods - Experimenting with MLFlow in a practical setting - Gaining a holistic view of the ML workflow, including feature stores and model registries I'm eager to continue expanding my skills, particularly in Docker and CI/CD, and apply these concepts to real-world projects. This course has provided me with a robust foundation to build and maintain high-performing ML models that deliver actionable insights. 🚀 Looking forward to new opportunities in the machine learning space! #MachineLearning #DataScience #AI #EndToEndML #MLFlow #FeatureEngineering #ModelDeployment #CICD #Docker #DataDrift #MLMonitoring #GoogleColab #DataCamp

A few days ago after work, I had an opportunity to attend #FullStackDataScience meet-up to learn what other full stack developers in the data science field are interested in. I was glad to hear that most data scientists are using Notebook as a scratchboard rather than a development environment or deploying in a production environment. With great presentations, Mary Gibbs, @WilliamAngel, and Abigail Haddad explained Docker development/deployment, how to utilize templates in VSCode to flow into CI/CD processes for a more packaged product of Data Science development in the output of APIs or even more reporting types of output. Now, the next step is how to enrich and model the data and make your Data Science product in rich Data sets, Metadata, GraphDB, and Business data views for simple queries, searches, and then Visualization. It'd be great to productize all the Data Science work in your enterprises and agencies for not only predictive analysis, or API calls, but also, a visualized dashboard of data science products. Let's prototype a user view with a framework like #streamlit or #react, the curated/modeled data in a multi-model database like #marklogic, and add them to an NLP tool like #semaphore for #GenAI. A solution for the adv. generative AI with your custom data from my colleague -

#AI #DataScience #MachineLearning 😊🙃😀🙃😊 The MLOps Playbook by Equal Experts! " .An ML solution depends on both the algorithm - which is code - and the data used to develop and train that algorithm. For this reason, developing and operating solutions that use ML components is different to standard software development. .This playbook brings together our experiences working with algorithm developers to make machine learning a normal part of operations. It won’t cover the algorithm development itself - that is the work of the data scientists. Instead it covers what you need to consider when providing the architecture, tools and infrastructure to support their work and integrate their outputs into the business. .It is a common mistake to focus on algorithms - after all they are very clever, require deep expertise and insight and in some cases seem to perform miracles. But in our experience, obtaining business value from algorithms requires engineering to support the algorithm development part of the process alongside integrating the machine learning solution into your daily operations. To unlock this value you need to: ▶ Collect the data that drives machine learning and make it available to the data scientists who develop machine learning algorithms ▶ Integrate these algorithms into your everyday business ▶ Configuration control, deploy and monitor the deployed algorithms ▶ Create fast feedback loops to algorithm developers" .Enjoy! T. Scott Clendaniel

Sometimes, managing machine learning projects can be difficult while dealing with multiple models, frameworks, and tools. And this is where MLflow comes into play.  MLflow is an open-source platform that manages the lifecycle of Machine Learning and provides a unified interface to handle the different aspects of an ML project. Want to dive into MLflow more, then read our complete blog today: https://lnkd.in/gf3xTPBH #aitobi #AITOBItech #InnovateWithAITOBI #TechAdvancements #BusinessConsulting #DigitalTransformation #PrecisionInTech #AITOBIclients #FutureTechSolutions #InnovativeSolutions #BusinessSolutions #MLflow #MachineLearning #ModelManagement #ModelDeployment #MachineLearningLifecycle