AI Studio
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A Platform-as-a-Service, Planck AI Studio offers its clients AI model deployment, inferencing, fine-tuning, and training services. To help any business implement custom models, we at Planck also offer advisory services for creating a custom MLOps pipeline.
Customize, test, and deploy all major open-source models from Google, Mistral, Meta, and more. Use our full-stack platform to build, test, and deploy enterprise-ready AI apps, customized with your own data, with any model, on our cloud. Compared to AWS or Azure, the costs associated are 60% lower on Planck, with pay-per-usage and no up-front fees.
Foundational Model APIs:
Build AI apps with foundational models like Llama-3 and other major open-source models.
Access a vast library of pre-trained AI models, covering a wide range of tasks such as natural language processing, image recognition, and more.
Easily integrate these models into your applications through simple API calls, without the need for deep machine learning expertise.
Example: A developer building a chatbot can use the Llama-3 API to provide the chatbot with advanced language understanding and generation capabilities.
Use case: A content creation platform can leverage the API to generate personalized product descriptions or blog post summaries based on user preferences.
AI Inference:
Deploy trained AI models to make predictions and inferences in real-time applications.
Once you've trained or fine-tuned a model, deploy it to our cloud platform for efficient inference.
Receive real-time predictions and insights from your models, enabling you to build responsive and intelligent applications.
Example: A fraud detection system can use a deployed AI model to analyze transaction data and identify suspicious activity in real time.
Use case: A customer support chatbot can leverage a deployed model to understand customer inquiries and provide accurate and timely responses.
AI Training:
Train custom AI models from scratch using large datasets and our powerful infrastructure.
Build highly tailored AI models that meet your specific needs and requirements.
Utilize our scalable cloud infrastructure to train models on massive datasets, accelerating the training process.
Example: A medical researcher can train a custom AI model to analyze medical images and diagnose diseases with high accuracy.
Use case: An e-commerce company can train a model to predict customer preferences and recommend relevant products.
AI Fine-Tuning:
Adapt pre-trained models to specific tasks and domains for improved performance.
Start with a pre-trained model as a foundation and fine-tune it on your own data to specialize it for your use case.
This process allows you to achieve better results with less training data and time.
Example: A language translation service can fine-tune a pre-trained language model on a large dataset of parallel texts to improve the accuracy of translations.
Use case: A social media platform can fine-tune a sentiment analysis model on its user-generated content to better understand user opinions and engagement.
AI Model Deployement:
Deploy and manage AI models on our scalable cloud platform for easy access and use.
Easily deploy your trained or fine-tuned models to our cloud platform for seamless integration into your applications.
Benefit from our scalable infrastructure to handle varying inference loads and ensure high availability.
Example: A mobile app developer can deploy an AI model to the cloud to enable real-time image recognition features on the app.
Use case: A financial institution can host a risk assessment model on the cloud to provide automated credit scoring for loan applications.
