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MLWorks
India
Приєднався 18 лют 2011
Hello Everyone, Welcome to MLWorks. I am Mayur, a Machine Learning Engineer with a decade of experience. I make content on Python, ML, DL, and many more. The key objective of the channel is to explore all the latest developement in the field of AI and Python. If you're interested in these topics, you can follow this channel.
MLWorks: mayurji.github.io/
Github: github.com/Mayurji
Medium: mayur-ds.medium.com/
MLWorks: mayurji.github.io/
Github: github.com/Mayurji
Medium: mayur-ds.medium.com/
Master MLOps: Harnessing Ray on Minikube for Powerful Distributed Computing!
Welcome to our in-depth tutorial on mastering MLOps! In this video, we'll guide you through the process of harnessing Ray on Minikube to achieve powerful distributed computing for your machine learning projects.
🚀 What You'll Learn:
- Setting up Minikube for local Kubernetes development (Watch Previous Video)
- Installing and configuring Ray for distributed processing
Whether you're just starting out in MLOps or looking to refine your skills, this video is packed with practical insights and hands-on examples. Join us as we unlock the potential of distributed computing with Ray!
👉 Don’t forget to like, subscribe, and hit the notification bell for more tutorials on MLOps and cutting-edge technologies!
🔗 Resources & Links:
- Ray Documentation: docs.ray.io
- Minikube Setup Guide: minikube.sigs.k8s.io/docs/start/
Let’s get started on your journey to mastering MLOps!
🚀 What You'll Learn:
- Setting up Minikube for local Kubernetes development (Watch Previous Video)
- Installing and configuring Ray for distributed processing
Whether you're just starting out in MLOps or looking to refine your skills, this video is packed with practical insights and hands-on examples. Join us as we unlock the potential of distributed computing with Ray!
👉 Don’t forget to like, subscribe, and hit the notification bell for more tutorials on MLOps and cutting-edge technologies!
🔗 Resources & Links:
- Ray Documentation: docs.ray.io
- Minikube Setup Guide: minikube.sigs.k8s.io/docs/start/
Let’s get started on your journey to mastering MLOps!
Переглядів: 24
Відео
Master MLOps: Harnessing MLFlow & Optuna for Optimal Machine Learning!
Переглядів 4514 днів тому
Unlock the power of MLOps in this comprehensive tutorial! 🌟 In "Master MLOps: MLFlow with Optuna," we'll dive deep into the world of machine learning operations, exploring how to effectively manage your ML lifecycle with MLFlow and optimize your models using Optuna. What you'll learn: - The fundamentals of MLOps and why it's essential for scalable ML projects - Step-by-step guidance on setting ...
Master MLOps: Deploy ML Models on Kubernetes with KServe, MLServer & MLFlow!
Переглядів 19221 день тому
Welcome to our comprehensive tutorial on deploying machine learning models in a Kubernetes environment! In this video, we'll guide you through the entire MLOps process, focusing on powerful tools like KServe, MLServer, and MLFlow. 🚀 What You'll Learn: - Introduction to MLOps and its importance (Watch Previous Videos) - Setting up your Kubernetes cluster for ML deployment (Watch Previous Videos)...
Master MLOps: MLOps with Kubernetes, Setting Up Kubernetes using Minikube!
Переглядів 9821 день тому
In this video, we dive into the world of MLOps and how Kubernetes can revolutionize your machine learning workflows! 🌟 Join us as we walk through the step-by-step process of setting up a local Kubernetes environment using Minikube. Whether you’re a beginner or looking to refine your skills, we’ll cover everything from installation to deploying your first ML model. You’ll learn: - What MLOps is ...
Polars Concatenation: Efficiently Combining DataFrames in Python | Episode 5
Переглядів 17Місяць тому
In this episode of our Polars series, we dive into the power of Concatenation and how it can help you combine DataFrames with ease. Whether you're working with rows or columns, Polars provides a fast and memory-efficient way to handle large datasets. We'll walk through practical examples and show you the differences between vertical and horizontal concatenation. By the end, you'll have a solid ...
Practical Tutorial: Mastering MLFlow, Model Serving with MLServer & Flask | Step-by-Step Guide
Переглядів 116Місяць тому
In this video, we dive deep into MLFlow model serving using MLServer and Flask. Learn how to deploy your machine learning models efficiently with MLFlow's powerful tracking and deployment capabilities. We walk you through the entire process, from setting up MLFlow, integrating MLServer, to serving models using Flask. Whether you're a beginner or looking to enhance your MLOps skills, this tutori...
Polars Series: Mastering Joins | Episode 4
Переглядів 24Місяць тому
Welcome to Episode 4 of our Polars Series! In this video, we dive deep into Joins in Polars, an efficient DataFrame library for Python and Rust. Learn how to perform different types of joins, including inner, outer, left, and right joins, to combine your data effectively. Whether you're a beginner or an experienced data professional, this episode will help you master Polars joins and optimize y...
Practical Tutorial: MLOps with MLFlow, Essential Tools for Machine Learning!
Переглядів 982 місяці тому
Unlock the power of MLFlow in your MLOps journey! In this video, we'll explore how MLFlow simplifies machine learning workflows, from tracking experiments to managing models in production. Whether you're new to MLOps or looking to scale your machine learning pipelines, this tutorial will guide you through the essential features of MLFlow. Learn how to streamline your ML processes and bring your...
Polars Series: GroupBy and Data Analysis | Episode 3
Переглядів 242 місяці тому
🚀 Welcome back to the Polars Series! In this episode, we take a deep dive into one of the most powerful features of Polars-GroupBy. Whether you're crunching numbers, summarizing data, or performing complex aggregations, mastering GroupBy is essential for effective data analysis. In this video, we'll cover: How to use GroupBy in Polars for efficient data grouping and aggregation. Advanced data a...
Polars Series: Everything to know about Drop and Null | Episode 2
Переглядів 212 місяці тому
🚀 Welcome to the Polars Series! In this episode, we dive into handling missing data with Polars-a powerful DataFrame library that's blazing fast and easy to use. In this video, we'll explore: - How to drop null values from your data. - Techniques to manage null entries, ensuring your dataset remains clean and efficient. - Practical examples and code walkthroughs to help you master data manipula...
Polars Series: Getting Started with the Basics | Episode 1
Переглядів 402 місяці тому
Welcome to Episode 1 of our Polars Python Library series! 🌟 In this introductory video, we’ll guide you through the basics of Polars, including how to set up your environment, create and manipulate dataframes, and perform essential operations. Perfect for beginners and those new to Polars, this episode will lay the groundwork for more advanced topics in upcoming videos. Remember to subscribe an...
Floating Point Numbers 101: Basics, Normalization, and FP32 Explained
Переглядів 922 місяці тому
Ever wondered how computers handle decimal numbers? This video covers the basics of floating point numbers, including normalization and the FP32 format. We'll break down what floating point numbers are, how they’re represented in FP32, and why normalization is crucial for accurate calculations. Perfect for beginners and those looking to refresh their knowledge! Watch now to get a solid foundati...
When to Use TF-IDF vs BM25: A General Guide
Переглядів 2202 місяці тому
Confused about when to use TF-IDF and BM25 for your text search or information retrieval project? This video breaks down the key differences between these two ranking algorithms and provides practical examples of when to use each. Learning about parameters helps decide BM25 or TFIDF with relevancy scoring, precision, and Document lengths. #datascience #tfidf #bm25 #machinelearning #naturallangu...
BM25 Algorithm: Overcoming the Limitations of TF-IDF
Переглядів 5042 місяці тому
In this video, we dive deep into the world of information retrieval, comparing and contrasting two powerful ranking algorithms: TF-IDF and BM25. While TF-IDF has been a cornerstone in text search, it faces challenges in handling long documents and query length variations. Discover how BM25 addresses these limitations and offers superior performance in ranking relevant documents. We'll explore: ...
TF-IDF Explained Simply: Understanding Text Analysis | Understanding Tf-Idf
Переглядів 533 місяці тому
Ever wondered how search engines like Google find the most relevant results for your query? It's all thanks to TF-IDF! In this video, we break down this complex concept into easy-to-understand terms. Learn what TF-IDF is, how it works, and why it's essential for text mining and information retrieval. From understanding term frequency to inverse document frequency, we've got you covered. Whether...
DSPy | Programming On Foundation Models | RAG | MultiHop-Search | CoT
Переглядів 2993 місяці тому
DSPy | Programming On Foundation Models | RAG | MultiHop-Search | CoT
System Design Basics: Back Of The Envelope Estimation
Переглядів 744 місяці тому
System Design Basics: Back Of The Envelope Estimation
System Design Basics: Logging, Metrics, and Automation
Переглядів 154 місяці тому
System Design Basics: Logging, Metrics, and Automation
System Design Basics: Database Scaling
Переглядів 354 місяці тому
System Design Basics: Database Scaling
System Design Basics: Stateless vs Stateful Applications
Переглядів 194 місяці тому
System Design Basics: Stateless vs Stateful Applications
System Design Basics: Content Delivery Network
Переглядів 264 місяці тому
System Design Basics: Content Delivery Network
System Design Basics: Load Balancing & Database Replication
Переглядів 704 місяці тому
System Design Basics: Load Balancing & Database Replication
System Design Basics: Database, Server, and Scaling
Переглядів 114 місяці тому
System Design Basics: Database, Server, and Scaling
RAG: Retrieval Augmented Generation | Things to know while building RAG systems
Переглядів 505 місяців тому
RAG: Retrieval Augmented Generation | Things to know while building RAG systems
Embedding Quantization Using Sentence Transformers: Speed Up Retrievel & Reduce Latency and Cost.
Переглядів 1065 місяців тому
Embedding Quantization Using Sentence Transformers: Speed Up Retrievel & Reduce Latency and Cost.
Fine-Tuning Wav2Vec2 using HuggingFace | Audio Classification
Переглядів 9585 місяців тому
Fine-Tuning Wav2Vec2 using HuggingFace | Audio Classification
Agents With Multiple Tools | Cohere and Langchain
Переглядів 1,6 тис.7 місяців тому
Agents With Multiple Tools | Cohere and Langchain