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 MLflow Model Monitoring OverviewThis tutorial project implements a model monitoring and experiment tracking system using MLflow. The system enables data scientists and ML engineers to track, compare, and deploy machine learning models with full versioning and performance monitoring capabilities. FeaturesExperiment Tracking: Record and compare different model runs with detailed metrics and parameters Model Versioning: Maintain a complete history of model iterations with MLflow's Model Registry Performance Monitoring: Track key metrics like accuracy, response time, and similarity scores Parameter Logging: Automatically record all model parameters and configurations AI Studio with MLflow Benefits for ML ProjectsExperiment Organization: Structured tracking of all model iterations and experiments Reproducibility: Complete environment and parameter tracking ensures reproducible results Collaboration: Team members can easily share and compare experiments Model Lifecycle Management: Streamlin

AI Studio Troubleshooting Guide Getting Started Issues Issue: Unable to create an account Solution:  Check if you're using a supported email domain  Clear browser cache and cookies  Try a different browser  Ensure you have a stable internet connection  If corporate email, check with IT about firewall restrictions  Issue: Can't access dashboard after login Solution:  Verify you're using the correct login credentials  Check if your account has been properly activated  Clear browser cache and try incognito mode  Disable browser extensions that might interfere  Contact support if issue persists   Connection Problems Issue: Frequent disconnections during sessions Solution:  Check your internet connection stability  Close unused browser tabs to free up resources  Try a wired connection instead of Wi-Fi if possible  Clear browser cache and restart  Check if your VPN is causing interference  Issue: API endpoints unreachable Solution:  Verify the correct endpoint URLs in your reques

  This week’s Office Hours was hosted by Sothan Thach and supported by Christian Charles and Suresh Ramanan from HP. It provided live technical help and guidance for participants in the DevPost Hackathon, particularly those using HP AI Studio.Summary  AI Studio Compatibility & Setup AI Studio currently supports Windows 10/11 and Ubuntu 22.04 LTS only. Running on Ubuntu 24.04 or VMs (VirtualBox, WSL, etc.) may cause issues; such configurations are not officially supported. Using GitHub Sample Projects Suresh gave a full walkthrough on how to clone and load projects from GitHub into AI Studio. He highlighted the value of the vacation-recommendation sample and showed how to run notebooks inside the correct AI Studio kernel. Hardware Requirements A GPU is needed; using platforms like Google Colab is not supported. Minimum specs: ~16GB RAM, compatible NVIDIA GPU. RAM needs may vary depending on model size (e.g., 1B parameters ≈ 1GB GPU memory). Gett

 AI Studio 101 (Watch the Recording)What is AI Studio? - Discover key features and capabilities to power your AI projects Getting Started on AI Studio - Follow along with our download experience, first sign-on process, and platform orientation Setting up your first project - Learn practical tips, tricks, and what to anticipate as you begin building 

Ready to dive into the HP AI Studio & NVIDIA Developer Challenge? The process is straightforward: after registering on Devpost, you'll receive an activation email from AI Studio with download links. Simply click the activation link, sign in with your HPID (or create one), download the installer for your platform, and follow the setup wizard. The first team member to sign in receives admin privileges and can invite others through the team settings.Once set up, you can create projects, define workspaces using NVIDIA NGC GPU-optimized pre-trained models, and start coding in notebooks. AI Studio brings together everything you need—data, people, and compute—in one powerful environment optimized for AI development. Be sure to check out the base images and GPU resources available to maximize your project's performance during the challenge! Have questions? Drop a comment below!Learn more about AI Studio here and see how Z by HP powers Data Science & AI Solutions.

You can find the code for the project and README in this public repository: https://github.com/HPInc/aistudio-samples/tree/main/deep-learning-in-ais/image_generation_deepseek_janus_pro

 AI Studio Platform Walkthrough: From Project Creation to Model Deployment This video covers essential features of AI Studio:Project Creation: Learn how to set up new projects with customized descriptions, tags, and privacy settings. We'll show you how to seamlessly connect to GitHub repositories for efficient code management.Asset Management: Discover how to upload, organize, and leverage different assets within your projects, making data management intuitive and efficient.NVIDIA NGC Model Integration: Explore the NGC catalog and see how easily you can search for and incorporate NVIDIA's GPU-optimized pre-trained models into your workflow, accelerating your development process.Workspace Configuration: Master the creation of custom workspaces tailored to your project's computational needs, ensuring you have the right resources for your specific tasks.Notebook Development: Experience the powerful coding environment with Jupyter notebooks, complete with pre-configured dependencies and se

AI Studio is a secure, containerized development environment that enables easy team collaboration.  This document outlines the requirements for the AI Studio platform.   

AI Studio workspaces streamline the entire AI/ML development lifecycle, from initial experimentation to final deployment:• Quickly spin up an environment with the necessary libraries• Collaborate effortlessly with team members, sharing notebooks and resources in real time• Utilize the containerized environment to develop locally and maintain control of sensitive dataThis document walks through the process of creating AI Studio Workspaces.

Dear Community Members,   We are delighted to invite you to an insightful talk presented by our talented student team.How will Neural Processing Units (NPUs) redefine the landscape of local AI? Our student team has delved into this question by conducting comprehensive performance comparisons of CPUs, GPUs, and NPUs on Z by HP mobile workstations. Their research focuses on a practical application: leveraging Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) for efficient document interaction.During this session, you will gain valuable insights into:- Performance Comparisons: Detailed findings on processing speed across CPUs, GPUs, and NPUs.- Energy Efficiency: Real-world data on the energy consumption of these processing units.- Local AI Capabilities: A practical demonstration of LLMs and RAG for document interaction on mobile workstations.- Future of Computing: An engaging discussion on the potential of NPUs and the future of local AI. Event Details:Date: April 4th,

 A recent video post by Akash James of SparkCognition shows how he was able to get the GPU he needed to work on his multi-modal LLM called VERsatile ONline Intelligent Cognitive Agent (VERONICA) by accessing compute he owned in India from the Himalayas. Join the Z by HP Boost webinar Oct 4th @ 9 am PST to hear how Akash is accessing his GPU remotely to create AI.  

I am looking to run some testing on a local ollama model in AI Studio. However, every time I open AI Studio, I have to redownload ollama and pull llama3 to use from my notebook. Any suggestion for how to save that download (done from terminal) so that it is already loaded when I open AI Studio?

I'm a Researcher, We want to work on Artificial intelligence, Deep learning and Machine Learning algorithms on healthcare databases. We have a budget of about 30,00,000/- Lakhs Indian rupees to spend. Please suggest a Workstation and an NVIDIA GPU under this budget. 

HP & NVIDIA AI Influencer Event at NVIDIA GTC 2025When: March 19, 2025 | 7:30 PM – 9:30 PM PDTWhere: Loft Bar & Bistro - Dining Room, San Jose, CAEngage with top AI influencers Discuss cutting-edge AI trends Expand your industry connectionsSecure your spot! https://register.nvidia.com/flow/nvidia/gtcs25/se73345/form/rsvp

 AI is evolving fast, and with all the hype, it’s easy to lose sight of what really matters. In a recent conversation, Thomas H. Davenport and Randy Bean broke down five key AI trends that will shape the year ahead—helping leaders cut through the noise and focus on what’s actually changing the game.n this discussion, they cover:Agentic AI: Hype vs. Reality – How to navigate the excitement and actual impact. AI ROI Matters – Why businesses need to track real value, not just trends. Data-Driven Culture Struggles – The persistent roadblocks to making data central to decision-making. Unstructured Data Challenges – Why tackling messy data is more important than ever. Evolving AI Leadership – How roles and reporting structures are shifting in the AI era.Drawing from their latest MIT Sloan Management Review article, these experts dive into how agentic AI and large language models are reshaping business in 2025. They explore how data-driven decision-making is changing the way companies operate

Imagine teaching a robot to boil water. It might first search for a pot, fill it with water, then place it on the stove. But what if it mistakenly turns on the microwave instead of the stove? Traditional AI agents often struggle to recover from such errors, leading to cascading failures. This is the problem Agent-R—a novel framework for training self-reflective language model agents—aims to solve.In this article, we’ll unpack the theoretical backbone of Agent-R by focusing on how this framework redefines error recovery in AI.Why Error Recovery Matters in Interactive EnvironmentsMost AI agents learn by mimicking expert trajectories (e.g., perfect step-by-step guides). But real-world tasks are messy. Errors are inevitable, and waiting until the end of a task to correct them is like letting a typo in a sentence propagate into a garbled paragraph.The Core Challenge: Timely InterventionProblem: In multi-step tasks (e.g., crafting items in Minecraft or navigating a virtual lab), errors early

Multi-agent systems have become increasingly important in solving complex problems through distributed intelligence and collaboration. However, coordinating multiple agents effectively remains a significant challenge, particularly in the field of reinforcement learning. In this article, we'll explore a novel approach called Shared Recurrent Memory Transformer (SRMT) that aims to enhance coordination in decentralized multi-agent systems.The Challenge of Multi-Agent CoordinationImagine a group of robots trying to navigate through a crowded warehouse. Each robot has its own goal and can only see what's immediately around it. How can they work together efficiently without bumping into each other or getting stuck? This is the essence of the multi-agent pathfinding problem.Traditional approaches often struggle with this task, especially when the environment is complex or the number of agents is large. They may rely on centralized control, which doesn't scale well, or on explicit communicatio

 A research paper by Ali Behrouz, Peilin Zhong and Vahab Mirrokni from Google Research introduce a groundbreaking neural network module that addresses the limitations of current models in handling long term dependencies. This work is a significant leap forward in the design of memory augmented architectures, offering theoretical and practical advancements in sequence modeling. Below we explore the theoretical underpinnings, technical innovations and experimental results of this advanced neural network module.The Challenge of Long-Term DependenciesModern neural architectures like Transformers and recurrent neural networks (RNNs) have revolutionized sequence modeling. However, they face some challenges:Transformers: While effective at capturing short term dependencies through attention mechanisms, they are computationally expensive for long sequences due to quadratic complexity in context length. Their context window limits their ability to model long term dependencies Recurrent Models

 Image generated using CopilotArtificial intelligence (AI) systems are becoming increasingly sophisticated, capable of generalizing across tasks and domains. However, this complexity makes them harder to understand, raising concerns about their trustworthiness and safety. Mechanistic interpretability offers a promising solution by reverse-engineering neural networks to uncover how they process information internally. Just as neuroscience advanced our understanding of the brain by studying internal cognitive processes, mechanistic interpretability seeks to move beyond black-box methods to provide precise, causal explanations of AI behavior.This article explores the principles, methods, and implications of mechanistic interpretability. By examining its core concepts, such as features, circuits, and hypotheses like superposition and linear representation, we aim to shed light on how this field can contribute to AI safety while addressing the challenges it faces.Mechanistic interpretabilit