Mlpack

Data mining and machine learning software | Free statistical software | Free mathematics software

mlpack is a machine learning software library for C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. Its intended target users are scientists and engineers. It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world. (Wikipedia).

MLOps: MLflow Hands On, Session 2, part 2

About MLflow Code example Package model & environment Configurations Model flavours App example Better way: mlflow.pyfunc Model wrapper Packaging

From playlist ML Ops (hands-on)

MLOps: Inference model serving overview, Session 2, part 9

Iris classification example walkthrough Serializing artifacts MLflow packaging Model wrapper Model and preproccesing definitions Artifacts Conda environment Meta data

From playlist ML Ops (hands-on)

Streamlit for ML #1 - Installation and API

▶️ Streamlit for ML Part 2: https://www.youtube.com/watch?v=U0EoaFFGyTg&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=2 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in pr

From playlist Streamlit for ML

Streamlit for ML #2 - ML Models and APIs

▶️ Streamlit for ML Part 3: https://www.youtube.com/watch?v=lYDiSCDcxmc&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=3 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in pr

From playlist Streamlit for ML

MLOps: Push an Image to DockerHub, Session 2, part 6

Tagging Pushing Pulling Webhooks

From playlist ML Ops (hands-on)

What is MLOps and how to get started? | MLOps series

❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨‍👩‍👧‍👦 Join our Discord community 👨‍👩‍👧‍👦 https://discord.gg/peBrCpheKE Disclaimer: this channel is separate from my work at Google DeepMind and is my attempt to educate the broader community of future ML enginee

From playlist MLOps

MLOps: Flask Iris Model Serving, Session 2, part 1

Flask web application Loading requirements Route definitions Running server Predict endpoint

From playlist ML Ops (hands-on)

What Is MLOps | Introduction to MLOps | MLOps Tutorial | DevOps Tutorial For Beginners | Edureka

🔥Edureka DevOps Training: https://www.edureka.co/devops-certification-training/ This Edureka video explains "what is MLOps" in a simple, comprehensive fashion. MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage produc

From playlist DevOps Training Videos

Streamlit for ML #4 - Adding Bootstrap Components

▶️ Streamlit for ML Part 5.1: https://www.youtube.com/watch?v=SGazDb8o-to&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=5 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in

From playlist Streamlit for ML

ML Ops: What is it REALLY?

ML Ops was very confusing to me at first. People were telling me it was useful, but would never tell me what it was. In this video I explain what ML Operations is (ML Ops), where it came from, and why learning it could be useful for your career. Be sure to check out the @KNNPodcast for s

From playlist Data Science Jobs

Mlpack

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