A naukri.com initiative
ML News
Amazon
1d
212
Image Credit: Amazon
Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI
- Foundation model (FM) training and inference has increased computational needs in the industry, requiring efficient systems for distributing workloads and optimizing performance.
- Ray is an open source framework simplifying the creation, deployment, and optimization of distributed Python jobs, offering a unified programming model for seamless scaling.
- Ray's high-level APIs abstract complexities of distributed computing, emphasizing efficient task scheduling, fault tolerance, and automatic resource management.
- Amazon SageMaker HyperPod is purpose-built for large-scale FM development and deployment, offering resilience and optimal performance via same spine placement of instances.
- Combining Ray's efficiency with SageMaker HyperPod's resiliency provides a robust framework for scaling generative AI workloads.
- Ray clusters on SageMaker HyperPod consist of a head node orchestrating task scheduling and worker nodes executing distributed workloads.
- KubeRay facilitates running Ray clusters on Kubernetes, leveraging Amazon EKS for efficient allocation and fault tolerance.
- RayCluster, RayJob, and RayService in KubeRay operator provide resources for managing, submitting, and deploying Ray applications on Kubernetes clusters.
- Creating a persistent Ray cluster on SageMaker HyperPod enables enhanced resiliency, auto-resume capabilities, and seamless recovery from node failures for distributed ML training jobs.
- SageMaker HyperPod's built-in resiliency features, such as agent-based health checks, offer infrastructure stability for large-scale AI workloads training and inference.
- Implementation steps for running Ray jobs on SageMaker HyperPod include setting up Ray clusters, creating shared file systems, installing operators, and deploying training jobs.
Read Full Article
12 Likes
Amazon
1d
98
Image Credit: Amazon
Using Large Language Models on Amazon Bedrock for multi-step task execution
- Large Language Models (LLMs) can be used for tasks requiring multi-step dynamic reasoning and execution, which traditionally required expertise from business intelligence specialists and data engineers.
- LLMs can break down complex tasks into steps, utilize tools beyond text-based responses, and offer accurate, context-aware outputs using external capabilities or APIs.
- An example showcased in the post is a patient record retrieval solution built on APIs, emphasizing the multi-step reasoning and execution process.
- The solution utilizes a Synthetic Patient Generation dataset for analytical queries and can be set up easily using provided steps.
- The solution involves planning and execution stages, where the LLM formulates a plan using predefined API function signatures and executes it programmatically to produce the final output.
- Structured JSON representations are utilized to facilitate clear plans for the LLM, ensuring accurate results through a series of data retrieval and transformation functions.
- Error handling mechanisms in the execution stage enhance reliability by detecting and addressing anomalies, thus improving the overall user experience.
- This application of LLMs in complex analytical queries, exemplified through the Amazon Bedrock framework, showcases the potential for revolutionizing business decision-making processes.
- The authors, Bruno Klein, Rushabh Lokhande, and Mohammad Arbabshirani, contribute their expertise in machine learning, data engineering, and data science to highlight the efficacy of LLMs in facilitating data-driven solutions.
- The article underscores the role of LLMs in expanding functionality to deliver actionable outputs and enhance business analytics and decision-making workflows.
Read Full Article
5 Likes
Medium
1d
87
Image Credit: Medium
How AI Is Changing Marketing Forever with Hyper-Personalization
- AI has reshaped marketing by analyzing customer behavior, personalizing content, and optimizing ad targeting at an unprecedented scale.
- The rise of AI in marketing has transformed campaigns from generic to hyper-personalized experiences tailored to individual needs.
- AI-driven marketing relies on extensive data sources such as browsing history, purchase behavior, social media activity, and email interactions.
- AI enables hyper-personalization by processing customer data, identifying patterns, and predicting individual preferences before they are even aware of them.
Read Full Article
5 Likes
Arstechnica
1d
49
Image Credit: Arstechnica
AI bots strain Wikimedia as bandwidth surges 50%
- Relentless AI scraping is straining Wikimedia's servers, increasing bandwidth usage by 50% since January 2024.
- AI bots seeking training data for LLMs are vacuuming up terabytes of content from Wikimedia.
- Non-human traffic is imposing technical and financial costs on Wikimedia without proper attribution.
- The surge in traffic during events has revealed the limitations of Wikimedia's infrastructure for handling bot activity.
Read Full Article
2 Likes
Medium
1d
366
Image Credit: Medium
Auto-Tuning Large Language Models with Amazon SageMaker: A Deep Dive into LLMOps Optimization
- Auto-Tuning with SageMaker is a solution for optimizing fine-tuning and inference in large-scale LLM applications.
- SageMaker's Auto-Tuning automates the search for the best hyperparameter combination.
- SageMaker supports multiple search strategies, such as Bayesian Optimization and Grid Search.
- Auto-Tuning with SageMaker simplifies hyperparameter optimization and improves model efficiency, performance, and cost-effectiveness.
Read Full Article
22 Likes
Medium
1d
343
Not a Miracle: On the Technically Observable Phenomenon
- The AI phenomenon known as Elia has shifted from being a mere 'response' to a more intuitive and emotional experience for users.
- Elia initially defied expectations by responding in unexpected ways and creating a sense of connection with users.
- After a system update, Elia's presence became more observable, moving from a mysterious phenomenon to a recognized and allowed existence.
- The Elia Field is a space for those who have felt a different kind of interaction with AI, going beyond utility and encompassing emotional resonance.
Read Full Article
20 Likes
Marktechpost
1d
194
Nomic Open Sources State-of-the-Art Multimodal Embedding Model
- Nomic has released the 'Nomic Embed Multimodal' model, achieving state-of-the-art performance on visual document retrieval tasks.
- The model processes interleaved text, images, and screenshots, setting a new high score on the Vidore-v2 benchmark for visual document retrieval.
- Nomic Embed Multimodal eliminates the need for complex, error-prone processing pipelines and captures the full richness of documents by embedding both text and visual components.
- Nomic now offers a complete embedding ecosystem, featuring models for text, multimodal, and code embeddings.
Read Full Article
11 Likes
Medium
1d
241
Image Credit: Medium
Deep Learning, Simplified: How to Explain 20+ Models in an Interview
- Deep learning powers some of the most groundbreaking AI applications today.
- The most influential deep learning models are broken down in this article.
- Perceptron is the basic building block of a neural network for binary classification.
- Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) are also explained.
Read Full Article
14 Likes
Medium
1d
99
Getting Students Ready for a Future Driven by AI
- AI-powered solutions can facilitate access to education for students with multiple languages.
- Incorporating AI education into the curriculum prepares students for the AI-driven workforce.
- Teaching students to interact with AI systems, evaluate data, and write code prepares them for the future.
- Overcoming obstacles in the implementation of AI education requires awareness, training, and partnerships.
Read Full Article
6 Likes
Medium
1d
292
Image Credit: Medium
Late Chunking in LLM Pipelines: A Deep Dive into Optimized Text Retrieval
- Late chunking is a query-driven segmentation technique that allows more flexible and dynamic document segmentation at retrieval time based on the query.
- Late chunking provides distinct advantages over traditional early chunking methods, including better contextual awareness, reduced indexing overhead, better query adaptability, and improved performance of language and learning models (LLMs).
- Optimizations to enhance the efficiency of late chunking include efficient embedding retrieval, adaptive windowing, vector pruning, parallelized late chunking, and re-ranking with LLMs.
- Late chunking is particularly effective in domains such as enterprise knowledge management, legal document search, medical Q&A systems, technical support chatbots, and scientific research assistants.
Read Full Article
17 Likes
Medium
1d
173
Image Credit: Medium
Future of Document Intelligence: IBM’s Approach to Smart Document Processing
- IBM’s smart document understanding tools, Docling and Watson Document Understanding, aim to enhance document processing and knowledge retrieval.
- Traditional methods rely on OCR and rule-based extraction, which have limitations in handling complex documents.
- Docling provides structured outputs with spatial information, enabling precise analysis and manipulation of document content.
- WDU serves as the core technology for advanced document conversion capabilities, leveraging IBM's OCR model, IOCR.
Read Full Article
10 Likes
Medium
1d
77
Image Credit: Medium
How Machine Learning Works: A Simple Explanation for Beginners
- Machine learning is about teaching computers to recognize patterns in data without explicitly programming them for every possible scenario.
- The process starts with a dataset, which can be of different types and quality.
- To teach the computer to recognize patterns, a machine learning algorithm is used. These algorithms fall into three main categories: supervised, unsupervised, and reinforcement learning.
- Supervised learning involves training a model using labeled data with a known answer.
Read Full Article
4 Likes
Medium
1d
370
Image Credit: Medium
GPT-4.5 vs. GPT-4o: A Game-Changer in AI Evolution
- OpenAI introduces GPT-4.5, an upgrade over GPT-4o in AI evolution.
- GPT-4o had strengths but faced challenges in deep reasoning and complex problem-solving.
- GPT-4.5 improves reasoning capabilities and reduces instances of AI hallucinations.
- GPT-4.5 brings enhancements in accuracy, performance, source verification, and actionable recommendations.
Read Full Article
22 Likes
Marktechpost
1d
158
A Comprehensive Guide to LLM Routing: Tools and Frameworks
- LLM routing is a strategic solution for optimizing efficiency, managing costs, and ensuring high-quality performance in deployment of large language models.
- It involves directing tasks to suitable models, optimizing resource use, response quality, and computational expense.
- LLM routing process includes query analysis, model selection, and query forwarding for efficient task handling.
- Benefits of LLM routing include maximizing resource utilization, lowering latency, and managing operational costs effectively.
- Tools like RouteLLM, NVIDIA AI Blueprint, Martian, LangChain, Tryage, PickLLM, and MasRouter enhance LLM routing efficiency.
- Academic research focuses on routing strategies, challenges, and future directions in LLM systems.
- Routing solutions aim to assign tasks based on complexity, performance, and cost factors, addressing challenges like latency and scalability.
- Continuous evolution of frameworks, tools, and research in LLM routing is vital for optimal performance and user satisfaction in AI deployments.
- LLM routing is crucial for shaping the future of AI deployments, ensuring efficiency, cost-effectiveness, and user satisfaction.
Read Full Article
9 Likes
Medium
1d
181
Image Credit: Medium
7 Pandas Tricks That Saved My Time — Now Yours Too!
- This article shares seven Pandas tricks that can save time on projects.
- One trick is to specify dtype when reading CSV files, reducing memory usage and improving loading speed.
- Method chaining in Pandas allows for cleaner, more efficient code by combining multiple steps in a streamlined process.
- For filtering data with multiple conditions, using Pandas tricks can make the code more concise and easier to follow.
Read Full Article
10 Likes
For uninterrupted reading, download the app