AI Cloud Features

A Platform-as-a-Service, Planck AI Cloud 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.

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API Calls:

• 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 Hosting:

• 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.