Paulo Menon’s Post

Every company I've spoken to that is building GenAI Applications today is writing custom code to load the data to their AI systems. That creates a lot of enterprise risk. "Instead of crafting custom code for new data engineering applications, users can opt for established data engineering platforms and to collaborate with vendors that can inventory important risks. At Datavolo, our team has been assisting data engineers in solving complex problems within the Apache NiFi community for almost a decade. We’ve curated a set of patterns and best practices, offering them as processors and templates to help data teams achieve their goals. For building multimodal data pipelines, we provide engineers with over 300 processors for extracting, chunking, transforming, and loading multimodal data for AI use cases. Alongside being secure, scalable, and user-friendly, Datavolo offers flexibility to seamlessly swap APIs and modify transformations, sources, destinations, and models. Datavolo users can efficiently reuse modular code, fostering collaboration and preventing redundant effort."

The Eyer machine learning is proprietary. The data pipeline is built on common technologies such as Spark, Deltalake, Influx, Kafka ++ This choice was deliberate and was made to enable Eyer to scale autonomous and automated monitoring for millions of performance metrics with a cost and performance advantage far beyond alternatives. Why? Because this will allow for aggressive scalability and flexibility in GTM models.

I have completed modern MLOps framework, including the lifecycle and deployment of machine learning models. I learned to write ML code that minimizes technical debt, discover the tools need to deploy and monitor your models, and examine the different types of environments and analytics use cases Happy learning 😀

Learned some new concept related to mlops.

In pursuing knowledge to build production-level ML systems, I am thrilled to announce that I have completed a comprehensive course on MLOps. 🚀

Woohoo! I completed an introductory course in #MLOps concepts!

Thanks DataCamp for this insightful track about the core principles of MLOps techniques and good practices. #ai #ml #devops #mlops

25 Top MLOps Tools You Need to Know in 2024 Discover top MLOps tools for experiment tracking, model metadata management, workflow orchestration, data and pipeline versioning, model deployment and serving, and model monitoring in production. 🧠 Large Language Models (LLMs) Framework 1. Qdrant 2. LangChain 📊 Experiment Tracking and Model Metadata Management Tools 3. MLFlow 4. Comet ML 5. Weights & Biases 🔄 Orchestration and Workflow Pipelines MLOps Tools 6. Prefect 7. Metaflow 8. Kedro 🗄️ Data and Pipeline Versioning Tools 9. Pachyderm 10. Data Version Control (DVC) 11. LakeFS 🛒 Feature Stores 12. Feast 13. Featureform 🧪 Model Testing 14. Deepchecks ML Models Testing 15. TruEra 🚀 Model Deployment and Serving Tools 16. Kubeflow 17. BentoML 18. Hugging Face Inference Endpoints 📈 Model Monitoring in Production ML Ops Tools 19. Evidently 20. Fiddler ⚙️ Runtime Engines 21. Ray 22. Nuclio 🔄 End-to-End MLOps Platforms 23. AWS SageMaker 24. DagsHub 25. Iguazio MLOps Platform #datascience #datascientist #mlops

Learn the basics of tracking in the context of MLOps by following along Chayma Zatout's handy guide, which covers the entire ML pipeline and the tracking that each stage calls for.

Intrested about implimenting MLOps & LLMOps in Databricks? This Data + AI Summit session is for you ☝ Background: This session offers a detailed look at the architectures involved in Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps). You will learn : - The core architectures used in MLOps and LLMOps. - The various elements involved in these operations, such as data management, model training, deployment, and monitoring. - Strategies for implementing effective MLOps and LLMOps in your projects, with a focus on improving efficiency and model performance. Link to session: https://lnkd.in/gZAnrMxH