valiantone

Transitioning from Tag Clouds to Tag Trees

In the packages paradigm - each packaged selection/offering is a bundle of semi-unique characteristics - lets term these as attributes. When a package bundle is selected it instantly informs our controller that certain atrributes are ground truth for this move. The current process introduces assumptions rather than ground truth. For instance let's observe Use Case 1.

Use Case 1: Studio Package Bundle

Facts:
    - Two Bellhops on the move
    - Duration of ~ 2hours
    - 16 Foot Moving truck required

valiantone

Weekly, Monthly Active Emails

WITH
    -- this is your original query, with the ISO week and month number added.
    members_log_aggr(login_date,  year_nbr, iso_week_nbr, month_nbr, email_count) AS
    (
        SELECT
            CAST(ml.login AS Date),
            DATEPART(YEAR, ml.login),
            DATEPART(ISO_WEEK, ml.login),

valiantone

def s3_to_pandas(client, bucket, key, header=None):

    # get key using boto3 client
    obj = client.get_object(Bucket=bucket, Key=key)
    gz = gzip.GzipFile(fileobj=obj['Body'])
    
    # load stream directly to DF
    return pd.read_csv(gz, header=header, dtype=str)

def s3_to_pandas_with_processing(client, bucket, key, header=None):

valiantone

Wavess Guide to Fine-tuning LLMs

Wavess is a play to explore funding opportunities with Astrik in the marketing co-pilot AI B2B service space.

Ahead AI specializes in Machine Learning & AI research and is read by tens of thousands of researchers and practitioners who want to stay ahead in the ever-evolving field.

General Fine-tuning

Resources

Fine-tuning large language models (LLMs) in 2024 Fine-tuning open source large language models (LLMs)