MLOps: Comparing Microsoft Phi3 Mini 128k in GGUF, MLFlow, and ONNX Formats
Explore Microsoft Phi3 Mini 128k model: compare inference time and precision across MLFlow, GGUF, and ONNX formats. Learn quantization techniques and performance optimization for various platforms.
Open Source Powered Real-Time Fraud Analysis in a Hybrid Cloud Environment
Explore real-time fraud analysis using open-source and closed-source solutions in a hybrid cloud environment, focusing on credit card fraud detection, AI model training, and deployment strategies.
Bringing AI to the Heterogeneous Edge with WebAssembly and ONNX
Explore deploying AI workloads to edge devices using WebAssembly and ONNX, addressing challenges of heterogeneous environments, security, and resource constraints in a multiplatform development approach.
A Deep Dive into Machine Learning and MLOps Pipeline with Model Registry and Feature Store
Explore key components of modern ML pipelines: AutoML, observability, model registry, transformation, and feature store. Learn to build scalable, reliable MLOps systems for enterprise-level machine learning.
Improving the Life of Data Scientists - Automating ML Lifecycle through MLflow
Explore Flock, an end-to-end platform leveraging MLflow to automate and simplify enterprise-grade machine learning, enhancing data scientist productivity and addressing regulatory challenges in ML adoption.
How to Convert Almost Any PyTorch Model to ONNX and Serve It Using Flask
Learn to convert PyTorch models to ONNX format and deploy them using Flask, focusing on a BERT sentiment model. Gain practical skills in model conversion and API development.
Machine Learning in Fastly's Compute@Edge
Explore WebAssembly modules for efficient ML model execution in Fastly's Compute@Edge, covering wasi-nn extensions, host API revisions, and KServe protocol integration for enhanced performance.
Compare, Contrast, Contribute: Model Deployment Standards and How You Can Help
Explore ML model deployment standards like PMML, PFA, and ONNX. Learn their features, use cases, and how to contribute to open-source initiatives advancing AI/ML interoperability.
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
Convert a fine-tuned ViT Transformer to ONNX format and perform CPU inference, enhancing model portability and efficiency for custom image classification tasks.
MLOps: C# Image Recognition with ResNet50v2 Model
Implement image recognition using ResNet50v2 in C# with ONNX and SixLabors.ImageSharp for efficient MLOps workflows.
Converting BERT to ONNX and Inference with BERTTokenizer for C# - Machine Learning and Data Science
Convert Bert model to ONNX format and create a C# app using BERTTokenizer for inference with ONNX transformer models.
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
Explore model conversion to ONNX, OpenVino, and Tensor-RT formats for optimized CPU and GPU inference, comparing performance against native PyTorch.
Object Detection with Faster R-CNN in C# Using ONNX on CPU
Implement object detection using Faster R-CNN in C# with ONNX format on CPU, leveraging machine learning for efficient image analysis.
Microsoft AI: Semantic Kernel C# SDK Chatting with Phi3 4k ONNX CPU - Machine Learning and Data Science
Develop a ChatBot using Phi3 Mini 4k Instruct ONNX model with Semantic Kernel C# SDK, integrating custom services for machine learning applications.
LLMOPs: Multimodal Prompting and Inference with Phi-3 Vision 128K Instruct on CPU - ONNX 4-Bit Quantization in C#
Explore multimodal prompting and CPU inference using Phi 3 Vision 128K Instruct, quantized to 4 bits in ONNX format, with C# implementation for efficient machine learning operations.
LLMOPs - Inference in CPU with Phi3 4k Instruct ONNX 4-bit Model Using C#
Explore CPU-based inference using 4-bit quantized Phi 3 4K Instruct in ONNX format with C#. Learn practical implementation for efficient machine learning deployment.
LLMOps: Converting Video Classifier (ViViT) to ONNX and Inference on CPU
Convert a Video Vision Transformer to ONNX format and perform CPU inference, enhancing model portability and deployment flexibility for video classification tasks.
Fine-Tuning ViT Classifier with Retina Images and Converting to ONNX - Video 3
Fine-tune a Vision Transformer for diabetic retinopathy detection using EyeQ dataset and convert the model to ONNX format for efficient deployment.
Secure AI Orchestration: Mitigating Model-centric Attacks with Flyte
Discover how to protect AI systems from model-centric attacks, focusing on code injection and LLM vulnerabilities, while implementing security measures using Flyte orchestration and open-source tools.
LLMOps: Inference of Fine-Tuned ViT Classifier on CPU with C#
Perform CPU inference on a fine-tuned Vision Transformer using C# and ONNX format for efficient image classification in machine learning projects.
Microsoft AI: Visual Studio Code AI Toolkit Introduction for Data Science and Machine Learning
Explore Visual Studio Code AI Toolkit with hands-on examples in Python and C#, focusing on CPU inference using the Phi 3 4K Instruct model in ONNX format.