Alternatives and similar repositories for Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda

Users that are interested in Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda are comparing it to the libraries listed below

• bodywork-ml / ml-pipeline-engineering
  Best practices for engineering ML pipelines.
  ☆35Updated 2 years ago
• ds-muzalevskiy / sagemaker-docker-deploy
  ☆18Updated 3 years ago
• sejalv / serverless-ml-workshop
  Deployment of ML models with Serverless APIs (AWS Lambda) and Docker
  ☆24Updated 4 years ago
• PacktPublishing / -Practical-Deep-Learning-on-the-Cloud
  Practical Deep Learning on the Cloud, published by Packt
  ☆41Updated 2 years ago
• Klalena / AWS-Serverless-Data-Engineering-Pipeline
  This is a repository for the Duke University Cloud Computing course project on Serveless Data Engineering Pipeline. For this project, I r…
  ☆19Updated 4 years ago
• noahgift / or
  Operations Research Algorithms
  ☆17Updated last year
• saturncloud / saturn-cloud-examples
  ☆19Updated 4 years ago
• Spratiher9 / Sparkora
  Powerful rapid automatic EDA and feature engineering library with a very easy to use API 🌟
  ☆53Updated 3 years ago
• maxhumber / MVML
  ⭕️ Minimum Viable Machine Learning
  ☆33Updated 4 years ago
• alexeygrigorev / deep-learning-keras-aws-lambda
  ☆11Updated 2 years ago
• PacktPublishing / Hands-On-Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda
  Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda, published by Packt
  ☆14Updated 2 years ago
• hanneshapke / Deploying-Deep-Learning-Models
  Strategies to deploy deep learning models
  ☆27Updated 6 years ago
• PipelineAI / katacoda-notebooks
  O'Reilly Katacoda
  ☆56Updated 2 years ago
• OElesin / sagemaker-autopilot-step-functions
  CD4AutoML: Continuous Delivery for AutoML with Amazon SageMaker Autopilot and Amazon Step Functions
  ☆13Updated 4 years ago
• eugeneyan / poc-docker-template
  Simple template showing how to set up docker for reproducible data science with Jupyter notebooks.
  ☆23Updated 11 months ago
• dominodatalab / domino-research
  Projects developed by Domino's R&D team
  ☆76Updated 3 years ago
• AlexIoannides / notes-and-demos
  Study notes and demos.
  ☆12Updated last year
• elleobrien / automatic_testing
  ☆21Updated last year
• dmatrix / mlflow-workshop-part-3
  Partly lecture and partly a hands-on tutorial and workshop, this is a three part series on how to get started with MLflow. In this four p…
  ☆35Updated 4 years ago
• tecton-ai / apply-workshop-2022
  ☆17Updated 2 years ago
• raghavbali / appliedml_workshop_dhs_av_2019
  Content for Applied ML Workshop @ DataHack Summit 2019
  ☆25Updated 5 years ago
• Spratiher9 / SparkDataset
  Instant search for and access to many datasets in Pyspark.
  ☆34Updated 2 years ago
• YisongZou / Flask-Salary-Predictor-with-Random-Forest-Algorithm
  In this project, we are going to use a random forest algorithm (or any other preferred algorithm) from scikit-learn library to help predi…
  ☆9Updated 3 years ago
• algorithmiaio / model-deployment
  Examples showcasing model deployment
  ☆18Updated last year
• parulnith / Explainable-AI-Workshop
  ☆17Updated last year
• lumiata / tech_blog
  Follow the Lumiata Tech Blog on Medium!
  ☆21Updated 2 years ago
• PacktPublishing / Amazon-SageMaker-Best-Practices
  Amazon SageMaker Best Practices, published by Packt
  ☆29Updated 2 years ago
• jacopotagliabue / no-ops-machine-learning
  A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.
  ☆37Updated 4 years ago
• PacktPublishing / Machine-Learning-with-Amazon-SageMaker-Cookbook
  Machine Learning with Amazon SageMaker Cookbook, published by Packt
  ☆54Updated 2 years ago
• datastacktv / kubeflow-introduction
  Code examples for the Introduction to Kubeflow course
  ☆14Updated 4 years ago