Auto-classification Neural Network Software Engineer 
Unite.AI
JUNE 24, 2024
MLOps , or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. What is MLOps? We also save the trained model as an artifact using wandb.save().
AWS Machine Learning Blog
MAY 31, 2023
PyTorch supports dynamic computational graphs, enabling network behavior to be changed at runtime. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime. Be sure to try it out!
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Mlearning.ai
JUNE 22, 2023
Understanding the biggest neural network in Deep Learning Join 34K+ People and get the most important ideas in AI and Machine Learning delivered to your inbox for free here Deep learning with transformers has revolutionized the field of machine learning, offering various models with distinct features and capabilities.
Snorkel AI
FEBRUARY 2, 2023
If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs. Streamlined tagging workflows.
Snorkel AI
FEBRUARY 2, 2023
If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs. Streamlined tagging workflows.
The MLOps Blog
JUNE 27, 2023
Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. The platform provides a comprehensive set of annotation tools, including object detection, segmentation, and classification.
The Stanford AI Lab Blog
MARCH 28, 2022
What sets this challenge apart from any other reinforcement learning problems is the fact that a classification needs to be made at the end of this agent’s interaction with this MDP — the decision of whether the MDP is the same as the reference MDP or not. Figure 7 : Performance of different bug classification models with different RL agents.
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