Arize AI

Our founders Jason Lopatecki and Aparna Dhinakaran were brought together through a common frustration: investments in AI are growing rapidly across businesses and organizations of all types, yet it is incredibly difficult to understand why a machine learning model behaves the way it does when deployed into the real world. If a machine learning team doesn't have the tools to answer basic questions about a model's performance metrics, how could they even attempt to answer more complex questions about the impact of these systems on the communities they are meant to serve? From that passion emerged Arize, a machine learning observability platform to help unpack the proverbial AI black box. Our solutions provide ML teams the tools they need to understand whether their models are performing as expected in production and quickly get to the cause behind issues that emerge. Our Mission To make the world's AI work and work for the people. About us About Arize AI Our founders Jason Lopatecki and Aparna Dhinakaran were brought together through a common frustration: investments in AI are growing rapidly across businesses and organizations of all types, yet it is incredibly difficult to understand why a machine learning model behaves the way it does when deployed into the real world. If a machine learning team doesn't have the tools to answer basic questions about a model's performance metrics, how could they even attempt to answer more complex questions about the impact of these systems on the communities they are meant to serve? From that passion emerged Arize, a machine learning observability platform to help unpack the proverbial AI black box. Our solutions provide ML teams the tools they need to understand whether their models are performing as expected in production and quickly get to the cause behind issues that emerge.Recorded sessions from Arize:Observe are now available → Platform Pricing Learn Docs Company Book a Demo Sign In Sign Up The ML Observability Platform for Practitioners Monitor, troubleshoot, and fine tune your models Try now Book a Demo Try Phoenix OSS: ML Observability in a Notebook Top ML companies use Arize Surface. Resolve. Improve. Analytics and workflows to catch model issues, troubleshoot the root cause, and continuously improve performance Monitors Dashboards Performance Tracing Explainability & Fairness Embeddings Analyzer LLM Observability Fine Tune Phoenix OSS Embedding & Cluster Evaluation Detect & Troubleshoot Drift Generative & LLM Observability Faster RCA with Performance Tracing Automated Model Monitoring Easy to Integrate & Deploy Enterprise-Grade Scale & Security Explore in platform Embedding & Cluster Evaluation Monitor embedding drift for NLP, CV, LLM, and generative models alongside tabular data Interactive 2D and 3D UMAP visualizations isolate problematic clusters for fine-tuning Understand Drift Impact Automatically monitor for modal input and output drift Trace which features contribute the most prediction drift impact on your model's performance Generative & LLM Observability Pinpoint clusters of problems in prompt/response pairs, find similar examples, and resolve issues Speed up fine-tuning and prompt engineering with purpose-built workflows. Integrates with common LLM agent tools such as LangChain. ML Performance Tracing Instantly surface up worst-performing slices of predictions with heatmaps Workflows to analyze features or slices of data – and A/B compare model versions, environments, and time periods Automated Model Monitoring Monitor model perfomance with variety of data quality and performance metrics, including custom metrics Zero setup for new model versions and features, with adaptive thresholding based on your model's historical trends Easy Integration & Deployment Log training, validation, and production datasets via SDK, cloud storage object store, data connectors, or local file Automatic model schema detection, import job troubleshooting, delayed actuals support, and API access #install and import dependencies !pip install -q arize import datetime from arize.pandas.logger import Client from arize.utils.types import ModelTypes, Environments, Schema, Metrics import numpy as np import pandas as pd #create Arize client SPACE_KEY = “SPACE_KEY' API_KEY = “API_KEY' arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY) #define schema  schema = Schema( prediction_id_column_name= “prediction_id', timestamp_column_name= “prediction_ts', prediction_label_column_name= “prediction_label', actual_label_column_name='actual_label', feature_column_names=feature_column_names, ) #log data response = arize_client.log(     dataframe=df,     schema=schema, model_id=“binary-classifications-metrics-only-batch-ingestion-tutorial', model_version=“1.0.0',     model_type=ModelTypes.SCORE_CATEGORICAL, metrics_validation=[Metrics.CLASSIFICATION], validate=True,     environment=Environments.PRODUCTION, ) Enterprise-Grade Scale & Security Scalable to billions of fully indexed events, with ability to extend monitors into your data lake or warehouse Securely collaborate across organizations, workspaces and projects with SAML SSO and RBAC controls Connects Your Entire Production ML Ecosystem Arize is designed to work seamlessly with any model framework, from any platform, in any environment. Data Sources Feature Store Model Serving Inference data indexed for real-time metrics monitoring, analysis, and performing tracing Arize SaaS Arize On-Premise Monitoring & Alerting Retraining Fine-tuning & Improvement Our Partners “The strategic importance of ML observability is a lot like unit tests or application performance metrics or logging. We use Arize for observability in part because it allows for this automated setup, has a simple API, and a lightweight package that we are able to easily track into our model-serving API to monitor model performance over time.' Richard Woolston Data Science Manager, America First Credit Union “Arize is a big part of [our project's] success because we can spend our time building and deploying models instead of worrying – at the end of the day, we know that we are going to have confidence when the model goes live and that we can quickly address any issues that may arise.' Alex Post Lead Machine Learning Engineer, Clearcover “Arize was really the first in-market putting the emphasis firmly on ML observability, and I think why I connect so much to Arize's mission is that for me observability is the cornerstone of operational excellence in general and it drives accountability.' Wendy Foster Director of Engineering and Data Science, Shopify “I've never seen a product I want to buy more.' Sr. Manager, Machine Learning Media tech company “Some of the tooling — including Arize — is really starting to mature in helping to deploy models and have confidence that they are doing what they should be doing.' Anthony Goldbloom Co-Founder & CEO, Kaggle “We believe that products like Arize are raising the bar for the industry in terms of ML observability.' Mihail Douhaniaris & Steven Mi Data Scientist & MLOps Engineer, Get Your Guide “It is critical to be proactive in monitoring fairness metrics of machine learning models to ensure safety and inclusion. We look forward to testing Arize's Bias Tracing in those efforts.' Christine Swisher VP of Data Science, Project Ronin Ready to get started? Start now Book demo Product Docs Pricing Learn Course Community Blog LLMOps Machine Learning Observability ML Monitoring Model Monitoring Model Drift KL Divergence Jensen Shannon Divergence Kolmogorov Smirnov Test About Customers Careers Press Security Try now Book a Demo Sign In Contact Privacy Policy Twitter Linkedin Copyright © 2023 Arize AI, IncRecorded sessions from Arize:Observe are now available → Platform Pricing Learn Docs Company Book a Demo Sign In Sign Up Built by ML Practitioners, for ML Practitioners Arize is an ML observability platform designed to help ML engineers and data scientists surface model issues quicker, resolve their root cause, and ultimately, improve model performance. Surface Automatically detect model issues in production Resolve Trace the root cause back to problematic data Improve Fine-tune performance and improve outcomes The ML observability solution for continuous model improvement The ability to surface unknown issues and diagnose the root cause is what differentiates machine learning observability from traditional monitoring tools. By indexing datasets across your training, validation, and production environments in a central inference store, Arize enables ML teams to quickly detect where issues emerge and deeply troubleshoot the reasons behind them. Designed With Your Goals in Mind ML Engineers Deploy & Maintain ML with Confidence Automatically monitor model performance on any dimension Standardize ML observability efforts across ML projects and teams Catch production model issues before they spiral Easy to deploy, easy to integrate Reduce time-to-detection (TTD) and time-to-resolution (TTR) with tracing workflows Data Scientists Keep Up the Model Momentum Exploratory data analysis (EDA) workflows Proactively uncover opportunities for retraining or to expand a model's use case Surface blind spots in the model Dynamic dashboards to track and share model performance ML Business Leader Connect AI ROI to Business Gain a single pane of glass into your production ML Understand how an ML model's performance impacts your product and business lines Align MLOps practices and tooling across engineering and data teams Explore the Platform Monitors Dashboards Performance Tracing Explainability & Fairness Embeddings Analyzer LLM Observability Fine Tune Phoenix OSS Recommended resources Whitepapers The Definitive Machine Learning Observability Checklist Read more→ Resource A Guide To Automated Model Retraining Read more→ ML Observability: A Guide for Central ML Teams Read more→ Getting Started With Arize Data Visualization for ML Observability in 3 Easy Steps Read more→ Webinar Advanced Metrics Workshop Series Read more→ Webinar Automatically Surface Issues with Unstructured Data Read more→ Webinar Improve Ranking Model Performance With ML Observability Read more→ Webinar Productionizing Machine Learning with Observability, Quality and Flexibility at Scale Read more→ Get ML observability in minutes. Sign up for free Product Docs Pricing Learn Course Community Blog LLMOps Machine Learning Observability ML Monitoring Model Monitoring Model Drift KL Divergence Jensen Shannon Divergence Kolmogorov Smirnov Test About Customers Careers Press Security Try now Book a Demo Sign In Contact Privacy Policy Twitter Linkedin Copyright © 2023 Arize AI, Inc