jesussantana / DeepLearning.AI-Introduction-to-Machine-Learning-in-ProductionLinks
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype…
☆10Updated 4 years ago
Alternatives and similar repositories for DeepLearning.AI-Introduction-to-Machine-Learning-in-Production
Users that are interested in DeepLearning.AI-Introduction-to-Machine-Learning-in-Production are comparing it to the libraries listed below
Sorting:
- Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.☆65Updated last year
- Watson OpenScale tutorials including sample models, notebooks and applications☆22Updated 3 years ago
- This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management☆15Updated 6 years ago
- Build tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Created by Ram Seshadri. Collaborators welcome…☆121Updated last year
- This is code depository for my upcoming session. Will update details post the session☆40Updated 3 years ago
- Build Deep Neural Network model in Keras and deploy a REST API to production with Flask on Google App Engine☆33Updated 2 years ago
- Writing Primer for Data Scientists☆18Updated 5 years ago
- ☆32Updated 6 years ago
- ☆13Updated 5 years ago
- Best practices for engineering ML pipelines.☆36Updated 3 years ago
- Streamlit example showing Scikit Learn & Pyspark ML over Healthcare data ! Its simple !!☆31Updated 5 years ago
- ☆23Updated 3 years ago
- A pipeline to detect data drift and retrain the model when there is drift☆24Updated 2 years ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.☆40Updated last year
- A hands-on introduction to Plotly and the Dash framework for creating analytical dashboards in Python.☆21Updated 6 years ago
- This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your ow…☆66Updated 2 years ago
- Boston Consulting Group Data Science & Advanced Analytics Virtual Experience Program☆11Updated 5 years ago
- 🦖 Streamlined Recommender Systems with TensorFlow and KubeFlow☆17Updated 2 years ago
- ☆14Updated 6 years ago
- Demo on how to use Prefect with Docker☆27Updated 3 years ago
- Pyspark in Google Colab: A simple machine learning (Linear Regression) model☆38Updated 6 years ago
- Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications☆105Updated 2 years ago
- This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behavio…☆79Updated 6 years ago
- This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of tra…☆27Updated 3 years ago
- FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)☆72Updated 4 years ago
- MLFlow End to End Workshop at Chandigarh University☆11Updated 3 years ago
- Streamlit-based Web App for Ai Text Generation based on GPT-2 Models from HuggingFace Model Hub using Python library aitextgen☆27Updated 5 years ago
- Machine Learning and Reinforcement Learning in Finance Specialization (MOOC) Assignments☆12Updated 4 years ago
- Advanced Business Analytics and Mathematics with Python (by @firmai)☆136Updated 4 years ago
- An End-to-End Implementation of AutoML with H2O, MLflow, FastAPI, and Streamlit for Insurance Cross-Sell☆82Updated 3 years ago