Ml Engineering Github Emperinter Info

TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation. The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more! An AI-powered data science team of agents to help you perform common data science tasks 10X faster. Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng Ultimate AI research and engineering course

This machine learning course is created with Jupyter notebooks that allow you to interact with all the machine learning concepts and algorithms to understand them better. At the same time, you’ll learn how to control these algorithms and use them in practice. Lectures can be viewed online as notebooks, as slides (online or PDF), or as videos (hosted on YouTube). They all have the same content. Upon opening the notebooks, you can launch them in Google Colab (or Binder), or run them locally. 1 These lectures (slides and video recordings) will be slightly updated.

2 The order of the slides in the video is slightly different. Retrieve all materials by cloning the GitHub repo. To run the notebooks locally, see the prerequisites. If you notice any issue, or have suggestions or requests, please go the issue tracker or directly click on the icon on top of the page and then ‘open issue`. We also welcome pull requests :). Superlinear is a Belgium-based Machine Learning company.

We invent, design and develop AI-powered software. Together with our clients, we identify which problems within organizations can be solved with AI, demonstrating the value of Artificial Intelligence for each problem. Our team is constantly looking for novel and better-performing solutions and we challenge each other to come up with the best ideas for our clients and our company. Here are some examples of what we do with Machine Learning, the technology behind AI: We work hard and we have fun together. We foster a culture of collaboration, where each team member feels supported when taking on a challenge, and trusted when taking on responsibility.

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular. 😎 A curated list of awesome MLOps tools 🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴 Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng Frouros: an open-source Python library for drift detection in machine learning systems.

Hi, I'm emperinter, an independent developer! Dedicated to crafting products that enrich lives and deliver meaningful value for others. emperinter is derived from the French word empreinte, which started as a favorite poem "Traces". I translated it, and it gradually evolved into emperinter! Initially focused on Android development using Java (6 projects), but later switched to iOS and Mac projects using Swift due to platform and revenue constraints (7 projects). Total downloads from the App Store exceed 800.

Started with a rented Linux server and gradually shifted towards building personal blogs and websites. This PHP project was created as a personal test after learning some web development. It was built using PHP and MySQL, leveraging an existing server setup. Selected quality blog posts that meet WeChat Public Account regulations and shared them on the public account, which also supports keyword searches and is updated periodically. Hands-On Guides, Tools, and Frameworks to Fast-Track Your AI Journey Whether you’re just getting started with Machine Learning and Artificial Intelligence or are looking to take your skills to the next level, GitHub is a goldmine of resources.

From in-depth tutorials to real-world examples, these repositories can dramatically boost your knowledge and hands-on skills in ML and AI. Here are seven outstanding GitHub repositories that every aspiring or professional ML/AI engineer should take a look at. The FastAI “fastbook” is the collection of Jupyter Notebooks that will introduce the reader to the world of deep learning. Jeremy Howard and the FastAI Team created this repository, giving it a mix of both theory and hands-on practice-very important ML concepts which can be covered using FastAI, built on PyTorch. Topics would range from foundational deep learning principles to advanced techniques. Each notebook is well designed with a beginner approach so that complex ideas become understandable.

For those interested in deep learning, fastbook would be indispensable. Students used GitHub Copilot to decode ancient texts buried in Mount Vesuvius, achieving a groundbreaking historical breakthrough. This is their journey, the technology behind it, and the power of collaboration. Learn how we’re experimenting with open source AI models to systematically incorporate customer feedback to supercharge our product roadmaps. This post features a guest interview with Diego M. Oppenheimer, CEO at Algorithmia Over the past few years, machine learning has grown in adoption within the enterprise.

More organizations are… To make language detection more robust and maintainable in the long run, we developed a machine learning classifier named OctoLingua based on an Artificial Neural Network (ANN) architecture which can handle language predictions in... Background Machine Learning Operations (or MLOps) enables Data Scientists to work in a more collaborative fashion, by providing testing, lineage, versioning, and historical information in an automated way. Because the… Best Data Science, Data Analytics, AI, and SDE roadmaps. This repository is continually updated based on the top job postings on LinkedIn and Indeed in the data science and AI domain.

Machine Learning Project Template - Ready to production Data Mining in Industrial Processes: Evaluation of different machine learning models for product quality prediction. Evaluated model types are Random Forest, Naive Gaussian Bayes, Logistic Regression, K Nearest Neighbour and Support Vector Machine. Comparision of non time based state based approach with time series based approach. Final result i… This repository serves as my personal portfolio, showcasing my projects, skills, and contributions.

Explore my work in JavaScript, Python, Web Development, and more. Online Portfolio of Arunkumar Venkataramanan A few useful things to know about machine learning Automatic translation (e.g. Google Translate) Progress in all sciences: Genetics, astronomy, chemistry, neurology, physics,…

Learn to perform a task, based on experience (examples) \(X\), minimizing error \(\mathcal{E}\) E.g. recognizing a person in an image as accurately as possible 📰 News: Updated in 2025: I have added a new repo for Agentic AI Systems, including the latest trends in AI engineering and agentic systems design and development, for those who are interested. You can find a variety of resources, system design summaries, and hands-on coding examples, projects, and more. This repo aims to serve as a guide to prepare for Machine Learning (AI) Engineering interviews for relevant roles at big tech companies (in particular FAANG).

It has compiled based on the author's personal experience and notes from his own interview preparation, when he received offers from Meta (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and... The following components are the most commonly used interview modules for technical ML roles at different companies. We will go through them one by one and share how one can prepare: At the time I'm putting these notes together, machine learning interviews at different companies do not follow a unique structure unlike software engineering interviews. However, I found some of the components very similar to each other, although under different naming. The guide here is mostly focused on Machine Learning Engineer (and Applied Scientist) roles at big companies.

Although relevant roles such as "Data Science" or "ML research scientist" have different structures in interviews, some of the modules reviewed here can be still useful.

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