Building an LLMOps Stack for Large Language Models
Explore LLMOps architecture: MLFlow for LLMs, Vector Databases, RAG strategies, and prompt evaluation. Gain insights into optimizing and deploying large language models effectively.
ML and MLOps with Databricks: LLMOps, MLFlow, Transformers, and Databricks
Explore LLMOps, MLFlow, and Databricks for advanced machine learning operations. Master transformers and enhance your data science skills.
MLOps MLFlow: Deployment Server - Managing and Using Multiple LLMs
Install and utilize MLflow Deployment Server to manage multiple LLMs, including OpenAI, Anthropic, and Langchain. Enhance your MLOps skills for efficient model deployment.
MLOps MLFlow: MLflow Projects with PySpark Docker Environments
Explore MLflow Projects with PySpark in Docker, utilizing Databricks for artifact storage and tracking in MLOps workflows.
MLFlow Projects: Dockerized Environments in MLflow
Explore MLflow projects using Docker environments and Databricks for artifact repository and tracking in MLOps workflows.
MLOps: Databricks MLFlow and Optuna Hyper-parameter Tuning
Optimize XGBoost models using Databricks, MLflow, and Optuna for efficient hyperparameter tuning and improved machine learning workflows.
MLOps: Databricks and MLFlow Hyper-parameter Tuning for XGBoost Models
Optimize machine learning models using Databricks and MLflow for hyperparameter tuning with XGBoost and HyperOpt.
MLOps MLFlow: Building Pipelines with MLflow Projects
Build MLflow project pipelines for efficient machine learning workflows. Learn to create and execute data science pipelines using MLflow Projects.
Serving LLMs with MLflow - Phi-3-mini-4k-instruct Model
Aprende a crear, guardar y cargar modelos LLM con MLflow, utilizando Phi-3-mini-4k-instruct para inferencia local en tu computadora.
MLOps: Crafting Custom Models with Python Model and PyFunc in MLFlow
Craft custom models using MLFlow's PythonModel and pyfunc, enhancing your MLOps workflow with flexible and reusable machine learning components.
LLMOps with Langchain, OpenAI, and Faiss on Databricks - Creating a Retrieval QA Tool
Develop a Retrieval QA tool using Langchain, OpenAI, and Faiss. Learn to save components in Databricks' MLFLOW, work with Model Governance, and perform inference after chain retrieval.
ML and MLOps with Databricks: LLMOps, MLFlow, OpenAI, and Databricks
Build a code reviewer using GPT-4, encapsulate it in MLflow, and deploy for inference in Databricks pipelines. Enhance your ML workflow with LLMOps techniques.
Fine-tuning Flan-T5 for Sequence-to-Sequence Classification with MLFlow
Fine-tune Flan-T5 for sequence-to-sequence classification using MLflow. Learn advanced MLOps techniques for optimizing language models in data science projects.
Fine-tuning Flan-T5 for Text Classification with MLFlow
Aprende a personalizar el modelo T5 base para clasificación de secuencias usando tu propio conjunto de datos y MLflow para MLOps.
MLOps with MLflow: Fine-Tuning LLMs - Tracking Experiments and Logging Models
Fine-tune LLMs, track experiments, and log models using MLflow for efficient MLOps practices in data science and machine learning projects.
Bridging the Chasm Between Research and Software Development - A Story From a Fertility App
Explore strategies to bridge the gap between research and software development in data science projects, focusing on MLflow for managing machine learning lifecycles.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions with Apache Spark. Explore distributed computing, MLflow, feature engineering, and integrating Spark with deep learning frameworks like TensorFlow and PyTorch.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.
The Model Review - Improving Transparency, Reproducibility, and Knowledge Sharing
Explore implementing a Model Review process for ML models using MLflow, enhancing transparency, reproducibility, and knowledge sharing in machine learning development and deployment.
Managing Complex Data Science Experiment Configurations with Hydra
Learn to manage complex data science experiments efficiently using Hydra, an open-source framework that simplifies configuration handling, enhances traceability, and improves reproducibility with minimal boilerplate code.
A Story of Unification from Apache Spark to MLflow
Explore the journey of unification from Apache Spark to MLflow, highlighting key developments and innovations in big data processing and machine learning workflows.