Ali Ghodsi

Organizations have insufficient technical capabilities for the speed and scale of their ML model implementations. My colleague Sumit Agarwal just published a very helpful research with a capability view and sample vendors for defining a modular, flexible and scalable MLOps solution. https://lnkd.in/gnB_T9bq #MLOps #LLMOps #AI

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Just having data is not enough in building a AI model, you have to make lot of modifications to the data and change its structure. In this article I discuss about some most fundamental ways of changing data shape which become very necessary when working with tensors. More on this comming soon. #DeepLearning #ML #MachineLearning #Pytorch

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Sharing the First part of my article on Unleashing Discovery-based Insights and Analytics using Vectorized Data Lakes alongside the Traditional Data Lakes. https://lnkd.in/gMCwzJMA The next part will show a more cost-effective and feasible implementation of Leveraging Vectorized Data Lakes for Un-Leashing New Discovery-based Insights & Analytics, apart from the Traditional Insights and Analytics.

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Causal AI is an emerging field within AI, but there is a distinction between causal inference and causal tracking.

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Awesome to see the Meta #Llama 3.2 models on #Azure #AI Model Catalog through managed compute inferencing #MaaS and soon on serverless API deployments while MACC Eligible providing customers with Enhanced Security and Compliance, Content Safety Integration, Simplified Assessment of LLM flows, Client integration, and Simplified Deployment and Inference. https://lnkd.in/g4Zpb3Dm

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Beyond single-score measurement and rankings this article provides more comprehensive approach to LFM (Large Fundamental Model) evaluation from end user perspective. https://lnkd.in/gSZbrqEb

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Very cool article by Christoph Molnar on the notion of inductive bias (here for Random Forests). The concept of inductive bias is often overlooked by practitioners, who assume this is a purely theoretical notion. On the contrary! This is one of the most fundamental aspects to consider when picking a model, interpreting its predictions or troubleshooting an underperforming system. #inductivebias #cart #trees #machinelearning #troubleshooting

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This is a win for #sustainability. In effect, ML applications may better leverage GPU capabilities, saving more time and energy 🚀

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Pushing the edge on how to use #AI and #LLMs with data tables and (numerical) datasets to answer questions and help with more informed decisions. In this technical deep dive I go through how to build a fully scalable and LLM-friendly datastore to use in combination with knowledge systems. The context is #WASH data.

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The current trend in LLMs is small models. This easy-to-understand article discusses the various ways to achieve model compression and provides key references.

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🔍 Key Papers to Demystify How GPT-o1 Works 1) Training Language Models to Self-Correct via Reinforcement Learning: https://lnkd.in/gwgdE5fb 2) STaR: Bootstrapping Reasoning With Reasoning: https://lnkd.in/gRdir7PP 3) Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking: https://lnkd.in/g47fqB7p 4) V-STaR: Training Verifiers for Self-Taught Reasoners https://lnkd.in/gGRRyBrF 5) Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters: https://lnkd.in/g9atmpvC 6) Chain of Thought Empowers Transformers to Solve Inherently Serial Problems: https://lnkd.in/gCyMncsf 7) Large Language Monkeys: Scaling Inference Compute with Repeated Sampling https://lnkd.in/ghy2r5nH 8) An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models: https://lnkd.in/gRHU2xyU 💡 Stay Tuned! I will continually update this post with more insights and a special focus on the "how" aspects of these papers.

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❄ In case you missed it, Snowflake recently released Arctic, a family of performant and compute-efficient Apache 2.0 licensed Large Language Models (LLMs). Curious about how Arctic might fit your needs? Come explore its capabilities at Clarifai alongside the 70+ other LLMs currently available on our platform. Let's build the next groundbreaking GenAI app together! https://lnkd.in/gNz5Babr

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The first step to delivering trusted AI is having the right type of data. Explore the dark age of data W/ @rwang0 on episode 1 of The AI Forecast w/ @xthestreams. Watch it here: https://buff.ly/4fBpC0V @cloudera #Data #AI

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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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Good detailed overview of Retrieval-Augmented Generation (RAG), one of leading techniques to improve accuracy of LLMs. When developing a production-grade LLM-based system, it's essential to understand the advantages and limitations of different techniques (RAG, prompt engineering, fine-tuning etc), across training and serving/inference phases, to improve LLMs for specific applications (see my previous post https://lnkd.in/giuJdJJt) A balanced approach to LLM applications that takes into account accuracy, latency, and cost is crucial for successful productization at scale. #artificialintelligence #largelanguagemodels #GenerativeAI #airevolution

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I had a talk at LDBC's TUC meeting last weekend, the main topic is around our recent studies around GPU vs. CPU on graph algorithm performance, system cost, and energy consumption... there are some "disturbing" findings that CPU-centric architecture can be 10X faster than GPU-centric arch... meanwhile, being 70-75% cheaper and greener... #Ultipa #XAI #GraphAnalytics

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Top 5 LLM Testing and Evaluation Tools: 🔭 Galileo: An end-to-end platform for GenAI evaluation, experimentation, and observability AgentOps.ai: Build reliable AI agents with monitoring, testing, and replay analytics. Toloka: A data-centric environment to support fast and scalable AI development with the help of human insight. Gantry: Powerful observability, analytics, and evaluation for your AI-powered products. RagaAI Inc: Fix AI issues in three easy steps - Detect, Diagnose and Fix. #AI

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