NLP inside your database - Query OpenAI’s GPT-3 from directly inside your database

@Adam Carrigan and @Rashid Azarang Esfandiari, I am a fan of Airtable as well. Our docs provide a comprehensive list of tutorials and examples for NLP-based text tasks, including NLP strategies. In addition, we offer other strategies such as regression, time series, and classification. I encourage you to proceed with scheduling the discovery call. We look forward to hearing about your use case.

@adam_carrigan @rashid_azarang_esfandiari if you join our community we can help you there, but please check the integration docs here: https://medium.com/@prathikkshet...

@rashid_azarang_esfandiari MindsDB's integration with Airtable allows you to use the power of AI to automate a wide range of tasks, such as: 1. Predictive modeling: You can use MindsDB to train models that make predictions based on your Airtable data. For example, you could train a model to predict customer churn, sales forecast, or lead conversion rate. 2. Data classification: You can use MindsDB to classify your Airtable data into different categories based on specific criteria. For example, you could use MindsDB to classify customer feedback into positive, neutral, or negative categories. 3. Data imputation: You can use MindsDB to fill in missing values in your Airtable data. This is useful when you have incomplete data that you need to complete for analysis or reporting purposes. 4. Anomaly detection: You can use MindsDB to identify outliers and anomalies in your Airtable data. This is useful for detecting potential fraud, identifying data quality issues, or detecting unusual behavior in your data. 5. Time series forecasting: You can use MindsDB to make predictions about future values in time series data stored in Airtable. For example, you could use MindsDB to forecast sales for the next quarter based on historical data. 6. Recommendation systems: You can use MindsDB to build recommendation systems that suggest items or actions based on your Airtable data. For example, you could use MindsDB to recommend products to customers based on their purchase history. Overall, MindsDB's integration with Airtable provides a powerful tool for automating a wide range of tasks using AI. Whether you're working with time series data, customer data, or any other type of data, MindsDB can help you make more informed decisions, save time, and improve the accuracy of your predictions.

@chandre31 Great. Thanks for the clarification 👌🏿

@ed_forson Thank you

@ed_forson absolutely! please join our community on slack, Patricio Cerda from our team, has implemented the ability to finetune based on your data.

@ed_forson Yes, MindsDB allows users to train models against existing data in their database. The training process involves feeding the data into the MindsDB engine, which uses machine learning algorithms to learn the relationships between the input features and the target variables. During the training process, users have the option to modify various parameters, such as the learning rate, the number of trees, the maximum depth of the trees, and more, to optimize the performance of the model. In addition, MindsDB provides a user-friendly interface that makes it easy for users to train, test, and deploy their models. The interface allows users to visualize their data, set their target variables, and perform feature engineering tasks like normalizing and transforming their data. Once the model is trained, users can evaluate its performance using metrics like accuracy, precision, recall, and F1 score, and make further adjustments as needed. Overall, MindsDB is designed to be a flexible and user-friendly tool for building and deploying AI models and allows users to train models against their own data and adjust various parameters to optimize performance.

@davidhere_ We have support for AWS ! Moving fast to expand on the rest

@davidhere_ what cloud platform do you use? happy to help: https://docs.mindsdb.com/setup/s...

@davidhere_ Yes, MindsDB currently supports the major cloud platforms. The platform provides integration with popular cloud data storage services, including Amazon S3, Google Cloud Storage, and Microsoft Azure. This makes it easy for you to store and access your data in the cloud, regardless of which cloud provider you use. Additionally, MindsDB can be deployed on popular cloud infrastructure platforms such as AWS EC2, Google Cloud Platform (GCP), and Microsoft Azure, giving you the ability to take advantage of the scalability and reliability of the cloud.

@charlielito This is a great question, the answer is YES! , Well more like very soon, check the open PR here: https://github.com/mindsdb/minds... We believe this will be a more comfortable way of fine-tuning than the one provided by OpenAI themselves

@charlielito @ricardo_r_f_ Indeed, this will likely be merged and released sometime next week. Our roadmap also includes support for inserts and edits in the short term.

@charlielito Yes, MindsDB supports fine-tuning GPT-3 for specific tasks and data. Fine-tuning involves taking a pre-trained model like GPT-3 and adapting it to a new task or dataset by training it further on a smaller, task-specific dataset. MindsDB provides a user-friendly interface that makes it easy to fine-tune GPT-3 for a wide range of NLP tasks, such as text classification, named entity recognition, and sentiment analysis. You can use MindsDB to load your task-specific data and fine-tune GPT-3 to improve its performance for your particular use case. In addition, MindsDB provides a range of tools and features to help you fine-tune GPT-3 more effectively, such as the ability to adjust the fine-tuning parameters, evaluate the model's performance using various metrics, and make adjustments as needed. Overall, MindsDB provides a flexible and user-friendly platform for fine-tuning GPT-3 and other pre-trained models to improve their performance for specific tasks and data. Whether you're working on a small project or a large-scale fine-tuning effort, MindsDB can help streamline the process and improve the quality of your models.

@ricardo_r_f_ @chandre31 especially this community is awesome!

@ricardo_r_f_ cool!

@ricardo_r_f_ nice!

@ricardo_r_f_ Thanks for sharing this.

@tomhuds We're close to shipping support for fine-tuned OpenAI completion models! After that, we'll look into doing the same for HuggingFace. As an aside, our MLOps features (particularly, model versioning and projects) enable you to effectively store, navigate and use all previous training runs for ML integrations that support the "retrain" and "adjust" commands with locally-stored models, which is quite convenient in use cases with long model lifecycles.

@tomhuds great question!

@tomhuds Stay tuned for further updates. We have much more exciting NLP stuff coming up pretty soon.

@martyna_slaw Thank you!

@martyna_slaw thanks

@martyna_slaw Thank you for all the love and support.

@david_fortunato1 wow thanks !

@david_fortunato1 Cool, thank you!

@david_fortunato1 Keep loving and supporting us.

@henryrearden I'd love to learn a bit more about your use case you can reach out to me on ac (at) mindsdb.com

@adam_carrigan @henryrearden looking forward to learn more about your use case, lets chat on our slack chat

@henryrearden Thanks for the appreciation. Feel free to join the community slack here- https://mindsdbcommunity.slack.com/ to discuss further.

@jorge_torres2 @adam_carrigan @alejandro_villegas Congrats!

@jorge_torres2 @adam_carrigan @alejandro_villegas thanks

@alejandro_villegas Thanks for the appreciation.