The right place to host, retrain and master Large Language Models
ML Place is a complete solution for managing LLMs that fits securely within your company's internal infrastructure and integrates LLMs into business workflows
Utilise connectors for seamless cloud or self-hosted LLM integration
Benefit from diverse LLM solutions tailored to your needs
Ensure optimal performance and resource use with transparent LLM hosting cost
Cut costs and effort in software development with our ready-to-use services and architecture. ML Place reduces server expenses with auto-scaling and flexible cloud server creation
Enhance your ML operations efficiently with ML Place
With ML Place, you can deploy AI models on-prem to guarantee privacy and minimise latency. Our on-server fine-tuning support ensures flawless LLM local deployment
ML Place is the optimal choice for enhancing your AI development and operational efficiency
Reliable and fast hosting for ML models, scalable to meet business demands
Simple management of an extensive range of models
Efficient resource use through auto-scaling and dynamic server allocation
Cost-saving server hosting diversity across different providers
Reduced workload for DevOps and MLOps, enabling your ML team to manage services independently
Training capabilities for bespoke ML models by business-oriented developers
Enhance your ML operations efficiently while maintaining high performance. ML Place reduces server expenses with auto-scaling and flexible cloud server creation
What are the most popular and powerful LLMs?
Some of the most widely used Large Language Models (LLMs) come from OpenAI, Google, Anthropic, Meta, Mistral, and others. Each has its strengths in different applications, and selecting the right one can make a significant impact. With ML Place, you can host and access top machine learning models to meet your needs.
What is LLM fine-tuning?
Fine-tuning refers to adjusting a pre-trained AI model on a specific dataset to improve its performance on particular tasks or with unique data. For insights into how LLMs function and their specific advantages for your business, contact us at contact@tovie.ai.
Can I deploy your models on my own servers?
Yes, ML Place offers flexible deployment options, including on-premises servers and private cloud environments, for enhanced data security and control. We provide support for setup and configuration. For any support, email us at contact@tovie.ai.
How do you compare to Hugging Face?
ML Place offers a corporate model marketplace, similar to Hugging Face, but for an internal environment. It simplifies managing large language models, delivering cloud and on-premise options through an advanced MLOps platform with accessible APIs, aimed at reducing DevOps workload and easing enterprise AI integration.
What types of models does ML Place support?
A: ML Place supports various large language models including GPT, BERT, T5 and more. It is compatible with models from providers like OpenAI, Anthropic, Google, Meta and others.
How does ML Place help with model evaluation and improvement?
ML Place includes tools to compare model responses easily, review request histories, conduct performance benchmarks, and fine-tune models. This allows for data-driven iteration and optimisation of your AI models over time.
What is the pricing model for ML Place?
ML Place has usage-based pricing, so you only pay for the resources and services you actually use. We offer a free trial to get started. Contact our sales team for high-volume enterprise pricing.
Can ML Place help manage open-source LLM hosting?
Absolutely, ML Place supports a variety of LLM hosting options, including open-source models. Our platform is equipped with connectors for cloud LLMs and supports various integrated LLM options like Llama, Mistral, and Mixtral.
How does ML Place integrate LLMs into business workflows?
ML Place seamlessly integrates LLMs into your business workflows, fitting securely within your company's internal infrastructure. With functionalities like model comparison tools, easy retraining, and the ability to manage a wide range of models, our platform ensures your LLMs contribute effectively to your business operations.
How to prepare data for LLM fine-tuning at ML Place?
To prepare your data for LLM fine-tuning at ML Place, ensure your dataset is clean, relevant to your business needs, and annotated if necessary. ML Place's tools can help you review and tag queries in the request history, aiding in preparing and optimising datasets for effective fine-tuning.
Please tell us about yourself and we’ll get back as soon as we can.
Name
Business email
Company name
Message
Please, fill in the form and we will contact you shortly.
We appreciate you contacting Tovie AI and will get back to you as soon as we can.
Agradecemos o seu contato e entraremos em contato o mais rápido possível.
Our voice assistant will give you a quick call to start the demo.
Please enter the following details and then our AI voice assistant will call you!
We use cookies to provide necessary website functionality, improve your experience and analyze our traffic. By using this website, you agree to our Cookies Policy and Privacy Policy
Accept