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Machine Learning

Deploying Machine Learning Models for Real-Time Predictions Checklist

Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies. In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process.

Use AI to train AI: prompt learning using OpenAI API and ClearML

Making a Question Answering (QA) bot that can cite your own documentation and help channels is now possible thanks to chatGPT and Langchain, an open-source tool that cleverly uses chatGPT but doesn’t require retraining it. But it’s a far cry from “out of the box.” One example is that you have to get the prompt just right. To get an LLM (large language model) to do exactly what you want, your instructions will have to be very clear, so what if we automate that too?

Say Hello to ClearML's Machine Learning-Powered Sarcasm Detector: How to Train a Language Classifier using ClearML

by Victor Sonck, Developer Advocate, ClearML Sarcasm can be difficult to detect in text, especially for machines. However, with the power of large language models, it’s possible to create a tool that can identify sarcastic comments with high accuracy. That’s exactly what the ClearML team did with their latest project: a sarcasm detector that combines various ClearML tools to showcase the capabilities of MLOps.

Tzag Elita and ClearML: Powering the Future of AI Workflows

The world of artificial intelligence and machine learning is constantly evolving, with new challenges and innovations emerging every day. Tzag Elita is a leading provider of HPC and AI solutions in Israel, and understands the importance of staying ahead of the curve and delivering cutting-edge solutions to their customers. That’s why ClearML is excited to announce our partnership with Tzag Elita.

Solving key challenges in the ML lifecycle with Unravel and Databricks Model Serving

Machine learning (ML) enables organizations to extract more value from their data than ever before. Companies who successfully deploy ML models into production are able to leverage that data value at a faster pace than ever before. But deploying ML models requires a number of key steps, each fraught with challenges.