01.24.2023
New Releases, Enhancements, Changes + Arize in the News!
Upload a local file directly through the UI on the 'Upload Data' page. Use Local Upload to verify the schema for a sample of your data and quickly onboard a model. This feature supports CSV, Parquet, and Avro files. Learn how to upload a file here.
Note: Local File Upload is limited to one 30MB file at a time.

(Optional) Provide the
metrics_validation
field to validate your schema and specify the desired metric group to be visualized in the Arize UI. Learn more about the metrics validator here. response = arize_client.log(
model_id='sample-model-1',
model_version='v1',
model_type=ModelTypes.BINARY_CLASSIFICATION, # new model type name
metrics_validation=[Metrics.CLASSIFICATION], # new metrics validator
environment=Environments.PRODUCTION,
dataframe=example_dataframe,
schema=schema
)
embedding_column_names
is now a dictionary object (previously a list). Use this dictionary to name embeddings represented in the UI for enhanced platform flexibility. Learn more here. # before (embedding_column_names as a list)
embedding_column_names = [
EmbeddingColumnNames(
vector_column_name="vector",
data_column_name="text",
link_to_data_column_name="image_link",
)
]
# after (embedding_column_names as a dict)
embedding_column_names = {
"embedding_display_name": EmbeddingColumnNames(
vector_column_name="vector",
data_column_name="text",
link_to_data_column_name="image_link",
)
}
Many executives wonder how generative AI will impact their business – particularly in a turbulent economic environment where growing productivity takes on an elevated importance. Here are a few tips for developing a generative AI strategy.

Key predictions for the state of AI in 2023:
- 1.Generative AI will go mainstream (but we need all hands on deck)
- 2.AI will likely take on elevated importance as the economy pressures companies to deliver greater efficiency and productivity.
- 3.The days of central ML teams taking months or years to build and maintain proprietary feature stores or monitoring tools in-house are numbered.
- 4.ML platforms that aren't built to handle unstructured use cases risk irrelevance.

Last modified 8mo ago