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No-Code/Low-code MLOps: A faster way to build and share datasets, models, and deployments.

Project description

Welcome to is a no/low code MLOps platform aiming to be the simplest way you create, use, and share AI.

You can use without any code to automate the full AI lifecycle of your datasets and models.

The Python SDK gives easy access to all of your datasets, models, jobs, ... on the platform.


$ pip install seeme

Getting started

from seeme import Client

cl = Client()

# -- Registration --

my_username =    # example: "my_username"
my_email =       # example: ""
my_password =    # example: "supersecurepassword"
my_firstname =   # example: "Jan"
my_name =        # example: "Van de Poel"


# -- Log in --

cl.login(username, password)

# -- Log out --



Manage the entire lifecyle of your datasets:

  • create
  • manage
  • version
  • label
  • annotate
  • import/export
from seeme import Dataset, DatasetContentType

# -- Get datasets --
datasets = cl.get_datasets()

my_dataset = Dataset(
    name= "Cloud classifier",
    description= "Classify clouds from pictures",
    default_splits= True, # If `True`, adds 'train', 'valid', and 'test' default_splits
    content_type= DatasetContentType.IMAGES,
    multi_label= False

my_dataset = cl.create_dataset(my_dataset)

Checkout the dataset documentation to see all possibilities and detailed guides.


Manage the entire lifecycle of your AI models:

  • create
  • manage
  • version
  • converst
  • predicti
  • import/export
from seeme import Model, Framework, ApplicationType

# -- Get models --
models = cl.get_models()

# -- Application ID --
application_id = cl.get_application_id(

# -- Create model --
model_name = "Cloud classifier"
description = "Classify clouds from images"

my_model = Model(
    name= model_name,
    description= description,
    application_id= application_id,
    auto_convert= True # Automatically converts your model to ONNX, CoreML, and TensorFlow Lite.

my_model = cl.create_model(my_model)

model_file_location = "my_exported_model.pkl"

cl.upload_model(, model_file_location)

image_location = "my_image.png"

cl.inference(, image_location)

Checkout the [model] documentation]( to see all possibilities and detailed guides.


Schedule training, validation, and model conversion jobs with a simple command:

from seeme import Job, JobItem, JobType, ValueType

jobs = cl.get_jobs()

job = Job(
    name= "v3 image classifier",
    description= "A new dataset for an improved model",
    application_id= application_id,
    job_type= JobType.TRAINING,
    dataset_id= dataset_id,
    dataset_version_id= datset_version_id,
    model_id= model_id,
    model_version_id= model_version_id,
    items= [
        name= "image_size",
        value= "224",
        value_type= ValueType.INT
        name= "arch",
        value= "resnet50",
        value_type= ValueType.TEXT

Applications automates the full lifecycle of data and models for a wide range of AI applications, such as:

  • image classification
  • object detection
  • structured data
  • language models
  • multi lingual text classification
  • object character recognition (OCR)
  • named entity recognition (NER)

for a number of AI frameworks and their versions:

For a full list of frameworks and their versions:

# -- Get applications --
all_applications = cl.get_applications()


SDK Documentation

For more detailed SDK documentation see

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