06.05.2023
New Releases, Enhancements, Changes + Arize in the News!
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New Releases, Enhancements, Changes + Arize in the News!
Last updated
Was this helpful?
Automatic baselines continually update your model's reference point based on the most recently uploaded training/validation dataset. We recommend you enable auto baselines if you frequently retrain your model to streamline your monitoring workflows.
Learn more about baselines and how to enable automatic baselines .
Dynamically track each individual data point within your monitor to account for seasonal or slow-moving data and identify local anomalies. This is automatically enabled for all new monitors.
Autothresholds are calculated based on a statistical analysis of data over 14 days. Each day, a data point is collected, and after 14 days, the average (mean) and standard deviation of these data points are computed. The threshold is then set by adding or subtracting the standard deviation from the average.
Sync model data stored in Snowflake tables with Arize to automatically stream, monitor, and analyze new model data for real-time ML observability.
Prediction ID: Now optional and supports up to 128 characters. If a prediction is sent without a prediction_id, Arize will generate a prediction_id for you
Timestamp: The valid timestamp window has been extended from 1 year in the past to 2 years before your most recently uploaded timestamp
Binary Classification, Multiclass, NLP, CV Model Type: You can now log data for this model type with a score only; a label is no longer required
Mixed Nulls: Label and Score dataframe columns with null values can now be logged
Arize's newest course covers core concepts and emerging best practices for large language model operations (LLMOps). Modules include:
Other new learning-focused content:
If you're currently using the previous version of auto thresholds with existing monitors, we strongly recommend refreshing your thresholds. Learn more about thresholds .
Learn how to connect Snowflake with Arize.
Easily share data when you discover interesting insights so your data science team can perform further investigation or kickoff retraining workflows. Learn how to export data .
After choosing your desired cluster metric (e.g. euclidean distance, accuracy, custom metric, etc.), Arize automatically surfaces the clusters you should focus on for model improvement / troubleshooting so you can quickly find the root cause. Learn more .
Now in early release! Monitor and troubleshoot your Object Detection models in Arize alongside your CV, NLP, LLM, and tabular models. for early access.
🏗️ : the significance and impact of unified language models — and what lies ahead
💬 : emerging techniques for assessing large language models using their peers
: unlock the potential of OpenAI Evals for comprehensive and reliable assessment of LLMs
: prompt engineering and management, LLM agents, and LLM observability
: what you need to know, from the basics to LLMs
⚖️ : tools for understanding and mitigating algorithmic bias
🌍 : the basics of AI ethics and how to mitigate bias in ML systems
Join us every week in the Arize Community for analysis of the latest . Here are the first few we covered:
: interactive point-based manipulation on the generative image manifold
: diving into the efficiency and effectiveness of large language models
In focused on building AI community, the founder of Cerebral Valley discusses the current moment in AI, the state of LLMOps, the mission of Cerebral Valley, and some of the exciting things community members are working on and building.
