An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
Data pipelines are essential for connecting data across systems and platforms. This article provides a deep dive into how data pipelines are implemented, their use cases, and how they're evolving with generative AI.
A guide to tracking in MLOps, covering code, data, and machine learning model tracking
Walkthrough on building a Q and A pipeline using various tools, and distributing it with ModelKits for collaboration.
Kit is a free, open-source MLOps tool that simplifies AI project management by packaging models, datasets, code, and configurations into a standardized, versioned, and tamper-proof ModelKit. It enables collaboration, model traceability, and reproducibility, making it easier to hand off AI projects between data scientists, developers, and DevOps teams.
Explores KitOps, an open source project that bridges the gap between DevOps and machine learning pipelines by allowing you to leverage existing DevOps pipelines for MLOps tasks.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
This article explores the transformer architecture behind Llama 3, a large language model released by Meta, and discusses how to leverage its power for enterprise and grassroots level use. It also delves into the technical details of LlaMA 3 and its prospects for the GenAI ecosystem.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
This article provides an introduction to Mlflow, an open-source platform for end-to-end machine learning lifecycle management. The article focuses on using MLflow as an orchestrator for machine learning pipelines, explaining the importance of managing complex pipelines in machine learning projects.
