MLtraq - Track Your ML/AI Experiments at Hyperspeed
Explore MLtraq's superior speed and capabilities for tracking ML/AI experiments. Compare leading tools, understand performance factors, and learn efficient Python object serialization techniques.
How to Improve LLM Factual Accuracy and Reliability
Explore techniques for improving LLM factual accuracy, including DSP framework and MLflow LLMOps tools, to create reliable AI applications for production use.
MLOps with MLflow - Creating Execution Pipelines Using Projects and Databricks
Explore MLflow Projects to create execution pipelines locally and leverage Databricks for tracking and artifact storage in machine learning workflows.
MLOps: Logging and Loading Microsoft Phi3 Mini 128k in GGUF with MLflow
Learn to log and load quantized LLama.cpp models in MLflow, create Python classes, and perform inference using Microsoft Phi3 mini 128k in GGUF format.
MLOps: Fine-tuning Mistral 7B with PEFT, QLora, and MLFlow
Fine-tune Mistral 7B using PEFT, QLora, and MLFlow techniques. Master advanced LLM optimization for improved performance and efficiency.
Tackling Operational Time-to-Market Decelerators in AI/ML Projects
Explore strategies to accelerate AI/ML project deployment by addressing operational challenges, focusing on MSP collaboration and open-source tool management in hybrid and multicloud environments.
Trust Fall: Three Hidden Gems in MLFlow
Discover three lesser-known MLFlow features to enhance AI research collaboration, increase transparency, and streamline metric tracking for improved trust and reproducibility in machine learning projects.
Simulating the Superbowl: Real-Time ML to Predict the NFL
Explore DraftKings' real-time ML system for NFL predictions, featuring Monte Carlo simulations and ensemble models. Learn about their MLOps pipelines and how Databricks accelerates development.
Advanced Experiment Tracking for LLM-Powered Applications with Customized Open-Source MLFlow
Discover Intuit's use of open-source MLFlow for enhancing ML models and LLM applications through experiment tracking, reproducibility, model lineage, and streamlined review workflows.
AI-Powered FinTech - AR Forecasting with Databricks and MLFlow
Automate AR forecasting with CICD pipelines, Azure cloud, and MLflow. Learn to deploy infrastructure, ML notebooks, and monitoring dashboards for efficient financial predictions without prior cloud experience.
Trust Fall: Three Hidden Gems in MLFlow
Discover three lesser-known MLFlow features to enhance AI research collaboration, increase transparency, and streamline metric tracking for improved trust and reproducibility in machine learning projects.
MLflow - Platform for Complete Machine Learning Lifecycle
Discover how MLflow simplifies machine learning development by tracking experiments, registering projects, and streamlining model deployment across various platforms.
Building Reproducible ML Processes with an Open Source Stack
Discover how to build truly reproducible machine learning experiments by integrating MLFlow, Git, lakeFS, and infrastructure-as-code for consistent data, code, and environment management.
Overview of the Databricks Platform and Data Lakehouse Architecture
Discover how Databricks revolutionizes data processing by combining data warehouse and lake architectures through Delta Lake, Apache Spark, MLFlow, and Koalas frameworks for scalable data engineering and analytics.
Using MLflow and Databricks to Deploy ML Models in Production
Explore MLflow and Databricks for production ML model deployment. Learn end-to-end management, model tracking, and deployment strategies, plus Databricks' Feature Store and AutoML.
Managing Machine Learning Experiments with MLflow
Learn to manage ML experiments, reproduce pipelines, and deploy models using MLflow, an open-source tool for streamlining the machine learning lifecycle.
The Birth and Growth of Spark: An Open Source Success Story
Explore a insightful conversation with Matei Zaharia, creator of Apache Spark, on open-source success, MLflow development, and the future of AI applications combining LLMs with other systems.
The Emerging Toolkit for Reliable, High-quality LLM Applications
Explore emerging techniques in LLMOps for building, tuning, and maintaining high-quality LLM applications. Learn about testing tools, programming frameworks, and methods to control LLM outputs for desired goals.
MLOps MLflow: Installing a MLflow Tracking Server in Docker Containers
Install and configure MLflow Tracking Server with MariaDB backend using Docker containers for efficient MLOps workflows.
MLOps MLflow: Time Series Forecasting with SkTime and Prophet
Explore time series forecasting using SkTime and Prophet libraries in MLflow, enhancing your MLOps skills with practical implementation.
MLOps MLflow: Deep Learning Frameworks - PyTorch and TensorFlow Integration
Explore integrating deep learning frameworks Pytorch and Tensorflow with MLflow for enhanced model management and tracking.