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Platform

 * AI PLATFORM
   
   Platform Overview
   The Production AI Platform
   AI Lake
   A single place to track all your AI projects
   
   DATA PREPARATION
   
   Automated data labeling - Scribe
   AI speeds labeling, LabelForce assures quality
   Data search & management - Spacetime
   Patented tech streamlines data management
   
   MODEL BUILDING
   
   Model training & evaluation - Enlight
   Scalable AI training with user-friendly UI

 * MODEL OPERATIONALIZATION
   
   Serverless inference - Armada
   Elastic inference optimizes compute costs
   AI workflows - Mesh
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   Edge version for low-power inference
   Modules - UI Modules
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 * TECHNOLOGY
   
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Platform

AI PLATFORM

Platform Overview
The Production AI Platform
AI Lake
A single place to track all your AI projects

DATA PREPARATION

Automated data labeling - Scribe
AI speeds labeling, LabelForce assures quality
Data search & management - Spacetime
Patented tech streamlines data management

MODEL BUILDING

Model training & evaluation - Enlight
Scalable AI training with user-friendly UI

MODEL OPERATIONALIZATION

Serverless inference - Armada
Elastic inference optimizes compute costs
AI workflows - Mesh
Drag and drop engine links models
Edge AI - Flare
Edge version for low-power inference
Modules - UI Modules
New Make reusable web apps

TECHNOLOGY

Computer Vision
Generative AI
Foundation Models
Natural Language Processing

PROFESSIONAL SERVICES

AI Lab
Generative AI Consulting Services
LabelForce
Data labeling services
ModelForce
Custom model building services
Government
Solutions

BY INDUSTRY

Government
Manufacturing
Media & Entertainment
Retail & E-Commerce
Transportation
See all Industries

BY USE CASE

Content Moderation
Digital Asset Management
Intelligence, Surveillance
Product Discovery
Visual Inspection
See all use cases

Explore Community
Explore
Image Models
Text Models
Audio Models
Workflows

Company
About
Careers
We’re hiring!
Customers
Events
Partners
Press
Tech Awards
Contact Us

Resources
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Blog
Docs
Resource Library
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YouTube
Support

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THE
LLM
COMPUTER VISION
FULL STACK AI
GENERATIVE AI
DEVELOPER PLATFORM

BUILD ON THE FASTEST, PRODUCTION-GRADE DEEP LEARNING PLATFORM FOR DEVELOPERS AND
ML ENGINEERS.

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170+
Countries
250k+
Users
1M+
AI Models
Billions
of predictions served
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Model Predict
url = "https://clarifai.com/quantum-synthetics/completion/models/fine-tuned-llm"
prompt = b"Which was our best quarter in 2022 and why?"
fine_tuned_llm = Model(url)
model_prediction = fine_tuned_llm.predict_by_bytes(prompt, "text")
 
for output in model_prediction.outputs:
print(output.data.text.raw)
 
# Output:
# -------
# > Quantum Synthetics Inc., witnessed unprecedented success in 2022's second
#   quarter primarily due to the release of the groundbreaking product, the
#   EcoSphere Purifier. This innovative device, acclaimed for its capability
#   to efficiently transform atmospheric carbon dioxide into oxygen, has not
#   only revolutionized the field of environmental conservation but also 
#   significantly bolstered the company’s reputation in the green technology
#   sector. The EcoSphere Purifier, with its unique design and superior
#   performance, has resonated profoundly with individuals and organizations
#   passionate about ecological sustainability, driving robust sales and
#   reinforcing your company's market presence.


AWARDS & RECOGNITION

Award winning technology in AI, Machine Learning & Computer  Vision

 


   
 * What’s New
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9.11 Release
Use third-party API keys for OpenAI, Anthropic, and Cohere directly with the
Clarifai.
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Blog
7 Tips For Efficient Data Labeling
Blog
8 Tips To Train And Fine-Tune Machine Learning Models
Blog
Clarifai 9.10: Elevate, Integrate, Innovate


GENERATIVE AI BUILT FOR DEVELOPERS BY DEVELOPERS

WHERE DEVELOPERS BUILD PRODUCTION COMPUTER VISION & LLMS WITH A FULL STACK AI
PLATFORM.

Learn more about platform
   
 * Generate
 * Understand
 * Manage

Learn more about platform


BUILD YOUR NEXT GENERATIVE INNOVATION

Leverage cutting-edge Large Language Models (LLMs) to craft coherent and
contextually rich content, connect image generation models to create detailed
visuals, utilize image captioning models for nuanced, descriptive narratives,
and employ speech generation to render lifelike voice outputs. Harness the
automation of workflows to seamlessly interlink generative models, enabling
effortless innovation and customization.

Docs
Language Image Caption
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Model Predict
llm_url = "https://clarifai.com/clarifai/completion/models/llm"
prompt = b"In 2 lines, summarize why the sky is blue."
llm = Model(llm_url)
model_prediction = llm.predict_by_bytes(prompt, "text")
 
for output in model_prediction.outputs:
print(output.data.text.raw)
 
Output:
-------
> The sky is blue because sunlight hits the atmosphere and the blue
  wavelengths are scattered more than other colors. This scattering
  makes the sky appear blue from the ground
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Model Predict
url="https://clarifai.com/stability-ai//models/stable-diffusion-xl"
prompt = b"A penguin watching a sunset"
image_generator = Model(url)
model_prediction = image_generator.predict_by_bytes(prompt, "text")
 
# Since we have one input, one output will exist here
output = model_prediction.outputs[0].data.image.base64
 
image_filename = f"gen-image.jpg"
with open(image_filename, 'wb') as f:
      f.write(output)
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Caption Model To Use
url = "https://clarifai.com/salesforce/blip/models/image-caption"
caption_model = Model(url)
 
# Image to caption
image_url = "https://samples.clarifai.com/caption-egg-basket.jpg"
model_prediction = caption_model.predict_by_url(url=image_url,
                                    input_type="image")
for output in model_prediction.outputs:
print(output.data.text.raw)
 
# Output:
# -------
# > a photograph of a basket of eggs in a basket on a wooden tablev


INSPECT DATA WITH ADVANCED MODEL ANALYSIS

Use advanced classification models to meticulously categorize and analyze data,
enabling swift and accurate decision-making. Employ detection models to identify
and locate objects, people, and more within images and videos, providing rich,
detailed insights. Harness segmentation models to delineate and differentiate
between various elements within an image, facilitating nuanced understanding and
analysis. If we don’t have a pre-trained model to suit your needs, easily train
another using our many architectures built into the platform.

Docs
Classifier Detector Segmentation
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Classification Model To Use
url = "https://clarifai.com/clarifai/main/general-image-recognition"
 
classify_model = Model(url)
 
# Image to caption
image_url = "https://samples.clarifai.com/metro-north.jpg"
model_prediction = classify_model.predict_by_url(url=image_url,
     input_type="image")
 
for concept in model_prediction.outputs[0].data.concepts:
print(f"{concept.name}: {concept.value}")
 
# Output:
# -------
# train: 0.9996048808097839
# railway: 0.9992978572845459
# subway system: 0.9982557892799377
# station: 0.9980103373527527
# locomotive: 0.9972555041313171
# transportation system: 0.9969767332077026
# travel: 0.9889694452285767
# commuter: 0.9808903932571411
# platform: 0.980640172958374
# light: 0.9741939902305603
# train station: 0.9687928557395935
# blur: 0.9672884345054626
# city: 0.9615078568458557
# road: 0.961391270160675
# urban: 0.960379421710968
# traffic: 0.9599704742431641
# street: 0.9475027918815613
# public: 0.9343006610870361
# tramway: 0.9319851398468018
# business: 0.9295381903648376v
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Classification Model To Use
url = "https://clarifai.com/clarifai/main/general-image-detection"
 
detect_model = Model(url)
 
# Image to caption
image_url = "https://samples.clarifai.com/metro-north.jpg"
model_prediction = detect_model.predict_by_url(url=image_url,
   input_type="image")
 
regions = model_prediction.outputs[0].data.regions
 
for region in regions:
    # Accessing and rounding the bounding box values
    top_row = round(region.region_info.bounding_box.top_row, 3)
    left_col = round(region.region_info.bounding_box.left_col, 3)
    bottom_row = round(region.region_info.bounding_box.bottom_row, 3)
    right_col = round(region.region_info.bounding_box.right_col, 3)
    
    for concept in region.data.concepts:
        # Accessing and rounding the concept value
        name = concept.name
        value = round(concept.value, 4)
 
        print((f"{name}: {value} BBox: {top_row}, {left_col}, "
               f"{bottom_row}, {right_col}"))
 
# Output:
# -------
# Building: 0.9396 BBox: 0.216, 0.002, 0.552, 0.25
# Person: 0.832 BBox: 0.497, 0.647, 0.669, 0.697
# Tree: 0.6977 BBox: 0.392, 0.365, 0.507, 0.511
# Building: 0.6605 BBox: 0.003, 0.305, 0.974, 0.999
# Tree: 0.5274 BBox: 0.378, 0.932, 0.46, 0.998
# Bench: 0.4542 BBox: 0.743, 0.822, 0.987, 0.999
# Land vehicle: 0.4328 BBox: 0.512, 0.61, 0.573, 0.644
# Person: 0.3903 BBox: 0.522, 0.039, 0.586, 0.058
# Train: 0.3745 BBox: 0.471, 0.29, 0.543, 0.472
# Waste container: 0.3713 BBox: 0.539, 0.738, 0.849, 0.893
# Person: 0.3325 BBox: 0.532, 0.072, 0.578, 0.106
# Note: Install clarifai with `pip install -U clarifai`
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model
 
# Classification Model To Use
url = "https://clarifai.com/clarifai/main/image-general-segmentation"

segment_model = Model(url)
 
# Image to caption
image_url = "https://samples.clarifai.com/metro-north.jpg"
model_prediction = segment_model.predict_by_url(url=image_url,
                                                input_type="image")
 
regions = model_prediction.outputs[0].data.regions
 
for region in regions:
    for concept in region.data.concepts:
        # The concept's percentage of image covered
        name = concept.name
        value = round(concept.value, 4)
        print((f"{name}: {value}"))
 
# Output:
# -------
# sky-other: 0.2198
# railroad: 0.1943
# platform: 0.1773
# ceiling-other: 0.1658
# building-other: 0.1185
# train: 0.0939
# tree: 0.0098
# person: 0.008
# unlabeled: 0.0077
# wall-concrete: 0.0047
# fence: 0.0001


ORGANIZE, SHARE, REUSE WITH AI LAKE

Manage your AI applications with Clarifai's intuitive platform. Upload inputs,
be it images, text, or videos, and harness them as the foundation to train
sophisticated models. Structure your uploaded data as datasets, enabling precise
subsets for model training and testing. Define concepts to categorize the
classes within detection, classification, and segmentation models. Employ
versioning to create and compare multiple iterations of your model, fine-tuning
them with varied data to achieve high performance.

Docs
App Management Inputs Datasets
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.user import User
client = User(user_id="user_id")
 
# Get all apps
apps = client.list_apps()
 
# This is how you can create an app and dataset
app = client.create_app(app_id="demo_app",
                    base_workflow="Universal")
 
# This is how you can delete the app 
client.delete_app(app_id="app_id")
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.user import User
app = User(user_id="user_id").app(app_id="app_id")
input_obj = app.inputs()
 
# How to upload an input upload from a URL
url = 'https://samples.clarifai.com/metro-north.jpg'
input_obj.upload_from_url(input_id='demo', 
                      image_url=url)
 
# How to upload an input from a file
input_obj.upload_from_file(input_id='demo',
                       video_file='demo.mp4')
 
# How to upload an input from raw text
input_obj.upload_text(input_id='demo',
                  raw_text='This is a test')
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.dataset import Dataset
 
# Get a dataset
dataset = Dataset(user_id="user_id", app_id="app_id", 
              dataset_id="dataset_id")
 
# How to upload a dataset from a dataset loader
dataset.upload_dataset(task='visual_segmentation', split="train", 
                   dataset_loader='coco_segmentation')
 
# How to upload a dataset from a local folder
dataset.upload_from_folder(folder_path='folder_path', 
                       input_type='text', labels=True)
 
# How to upload a text dataset from a csv file
dataset.upload_from_csv(csv_path='csv_path', labels=True)
Docs
Consult Clarifai's Documentation for clear information, guides, and tutorials on
using our AI models and workflows effectively.
Read more
Community
Access Clarifai's free AI resources. Use our apps, models, and workflows to
enrich your applications.
Start for free
Discord
Have questions or want to know more about Clarifai? Join our Discord! It's the
easiest and fastest way to get support.
Join


DON’T JUST TAKE OUR WORD FOR IT

WHAT DEVELOPERS AND CLIENTS SAY ABOUT US

“Clarifai provides an end-to-end platform with the easiest to use UI and API in
the market. They’ve accelerated our AI development at scale allowing 1,000's of
workers to label data and train 100,000's of AI models with significantly less
development effort, and expedited go-to-market.”

Sr. Director of Catalog Operations
E-commerce Unicorn


“We evaluated the trillion dollar companies and a few niche retail players for
our customer facing visual search use case. Clarifai was much easier to use than
the trillion dollar companies, and their AI significantly outperformed both the
niche players and the big guys in accuracy while having inference speeds 7x
faster. The performance and the flexibility of the Clarifai platform has our
executives exploring numerous other use cases to be powered by the Clarifai
platform.”

Director of Product Management
Fortune 500 Retailer


“A pioneer in deep learning-based computer vision, Clarifai can tackle
near-real-time visual search, facial recognition use cases, and deployment in
the most secure, air-gapped environments that nearly all other vendors can’t
match."

Forrester


"Clarifai is a true leader in AI applications for DAM and serves our users by
improving the searchability and discoverability of their content. Today, Widen
users gain the ability to search by image, which is a game-changer during
time-sensitive projects. By saving countless hours for users, this partnership
with Clarifai improves the Widen experience and delivers true ROI for our
customers."

Jamie Liechty
Acquia DAM


"My organization has worked with Clarifai for almost three years, close to a
daily basis, and the engagements and collaboration are top notch. The Clarifai
platform and capabilities can be adapted, trained, integrated and deployed to a
variety of problem sets with stellar performance. We chose Clarifai over
Microsoft, Google, AWS and IBM because of their production functionality and
performance, product vision and their strong consulting partnership."

Head of Catalog
Fortune 500 Company


"The team was a pleasure to work with. We had a highly dynamic (some would say
chaotic) deployment schedule, but the Clarifai team did their best to be
malleable to our needs. They were professional, engaging and collaborative.
Through constant interaction with us, they iterated on their solution and
developed novel approaches to our problem."

Head of TechOps
Global 2000 Airline


“Clarifai provides an end-to-end platform with the easiest to use UI and API in
the market. They’ve accelerated our AI development at scale allowing 1,000's of
workers to label data and train 100,000's of AI models with significantly less
development effort, and expedited go-to-market.”

Sr. Director of Catalog Operations
E-commerce Unicorn


“We evaluated the trillion dollar companies and a few niche retail players for
our customer facing visual search use case. Clarifai was much easier to use than
the trillion dollar companies, and their AI significantly outperformed both the
niche players and the big guys in accuracy while having inference speeds 7x
faster. The performance and the flexibility of the Clarifai platform has our
executives exploring numerous other use cases to be powered by the Clarifai
platform.”

Director of Product Management
Fortune 500 Retailer


“A pioneer in deep learning-based computer vision, Clarifai can tackle
near-real-time visual search, facial recognition use cases, and deployment in
the most secure, air-gapped environments that nearly all other vendors can’t
match."

Forrester


"Clarifai is a true leader in AI applications for DAM and serves our users by
improving the searchability and discoverability of their content. Today, Widen
users gain the ability to search by image, which is a game-changer during
time-sensitive projects. By saving countless hours for users, this partnership
with Clarifai improves the Widen experience and delivers true ROI for our
customers."

Jamie Liechty
Acquia DAM


"My organization has worked with Clarifai for almost three years, close to a
daily basis, and the engagements and collaboration are top notch. The Clarifai
platform and capabilities can be adapted, trained, integrated and deployed to a
variety of problem sets with stellar performance. We chose Clarifai over
Microsoft, Google, AWS and IBM because of their production functionality and
performance, product vision and their strong consulting partnership."

Head of Catalog
Fortune 500 Company


"The team was a pleasure to work with. We had a highly dynamic (some would say
chaotic) deployment schedule, but the Clarifai team did their best to be
malleable to our needs. They were professional, engaging and collaborative.
Through constant interaction with us, they iterated on their solution and
developed novel approaches to our problem."

Head of TechOps
Global 2000 Airline


“Clarifai's pay-per-usage is an incredible feature.  I would be more than happy
to use clarifai end points than anything else. The fine-tuning functionality is
also makes our life easy.”

Amal Jose
AI engineer


“There's so much there in Clarifai it's sometimes hard to be overwhelmed”

Tonic
CIO at Livedyne


“Got to say I like the environment Clarifai offers to build and prototype on
ideas faster, it was my first time building with Clarifai.”

Bhavish Pahwa
SDE-1 (ML) at Mindtickle


“Clarifai's pay-per-usage is an incredible feature.  I would be more than happy
to use clarifai end points than anything else. The fine-tuning functionality is
also makes our life easy.”

Amal Jose
AI engineer


“There's so much there in Clarifai it's sometimes hard to be overwhelmed”

Tonic
CIO at Livedyne


“Got to say I like the environment Clarifai offers to build and prototype on
ideas faster, it was my first time building with Clarifai.”

Bhavish Pahwa
SDE-1 (ML) at Mindtickle



COMPUTER VISION AND LLM AI LIFECYCLE PLATFORM

THE DEVELOPER PLATFORM FOR ANY DEEP LEARNING USE CASE

   
 * Chat with your data
 * Facial Recognition
 * Sentiment analysis
 * Speech synthesis
 * Summarization
 * Text moderation
 * Translation
 * Visual moderation


CHAT WITH YOUR DATA

Retrieval Augmented Generation (RAG) enables users to interact conversationally
with their own data, using NLP to pull relevant information from datasets. RAG
is a two-step process: first, it retrieves documents that are likely to contain
the answers, then it generates responses based on the retrieved documents. This
creates a chatbot that delivers personalized responses with zero hallucinations.

Use Model



FACIAL RECOGNITION

Clarifai's Facial Recognition technology allows for the accurate identification
and analysis of human faces. This technology is versatile, aiding in
applications such as security, user authentication, and user experience
enhancement by quickly and precisely interpreting facial features. Whether it's
automating access control or personalizing user interactions, Clarifai provides
the tools to integrate facial recognition seamlessly into your applications.

Use Workflow



SENTIMENT ANALYSIS

Textual Sentiment Analysis technology interprets and evaluates the emotions
conveyed within a body of text. This sophisticated tool is instrumental in
understanding user sentiments, allowing for enhanced customer interactions and
feedback analysis. By transforming raw text into insightful data, it aids in
refining product strategies, improving customer relations, and optimizing
overall user experience, helping businesses to respond more effectively to their
audience’s needs and preferences.

Use Model



SPEECH SYNTHESIS 

Speech Synthesis transforms text into natural, lifelike speech, allowing
developers to create applications that talk in a human-like voice. This advanced
technology enhances user engagement by providing auditory interaction, making
information more accessible and interaction more intuitive. Whether it’s for
assistive technologies, entertainment, or customer service applications, Speech
Synthesis brings versatility to voice-enabled experiences, enabling a more
inclusive and interactive future.

Use Model



SUMMARIZATION

Summarization distills lengthy texts down to their essential points, providing
clear, concise summaries. This advanced tool is invaluable for quickly
understanding and conveying key information from extensive documents or content,
aiding in efficient knowledge acquisition and decision-making. Whether used for
academic research, content creation, or business intelligence, our summarization
technology enables users to save time and focus on what truly matters.

Use Model



TEXT MODERATION

Text Moderation identifies and filters inappropriate or harmful text content,
ensuring online spaces maintain a positive and safe environment for users. This
technology is crucial for businesses and developers aiming to uphold community
guidelines and standards across platforms, from social media to forums. By
automating content moderation, it allows for a proactive approach to manage and
mitigate risks associated with user-generated content.

Use Model



TRANSLATION

Translation technology enables the conversion of text from one language to
another with high accuracy, facilitating communication across language barriers.
This solution is essential for developers looking to make their content
accessible to a global audience, enhancing user understanding and interaction.
Whether it’s for customer support, content creation, or multilingual platforms,
our translation tools bridge linguistic gaps, fostering inclusivity and
connection.

Use Model



VISUAL MODERATION

Visual moderation empowers platforms to detect and filter out inappropriate or
harmful visual content, creating a safer online environment. This solution is
key for businesses and developers aiming to maintain a positive user experience
on their platforms, ranging from social media to community forums. By leveraging
image analysis, it proactively moderates content, helping to uphold community
standards and protect user well-being.

Use Model



TRUSTED BY ENTERPRISES. POWERED BY PARTNERS.

ADVANCE AI ADOPTION WITH CLARIFAI’S NETWORK OF PARTNERS





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SOLUTIONS

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