New GPT DP & NavFT blueprints (#435)

use_cases/details/gpt-dp.md

@@ -0,0 +1,7 @@
+![Create free text data with privacy guarantees](https://blueprints.gretel.cloud/use_cases/images/gpt-dp.png "Create free text data with privacy guarantees")
+
+Unlock the potential of your text data while ensuring privacy by applying [differentially private fine-tuning using GPT](https://gretel.ai/blog/generate-differentially-private-synthetic-text-with-gretel-gpt). This method allows you to create a version of your free text data that maintains the integrity of sensitive information while still providing high-quality outputs.
+
+We recommend having a dataset of at least 10,000 samples to ensure reasonable quality. Note that differential privacy requires more epochs, which leads to longer training times compared to running without differential privacy.
+
+Prefer coding? Check out the [SDK notebook](https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/generate_differentially_private_synthetic_text.ipynb) example.

use_cases/details/navigator-ft-simple.md

@@ -0,0 +1,8 @@
+![Generate multi-modal synthetic data with Navigator Fine Tuning](https://blueprints.gretel.cloud/use_cases/images/navigator-ft-hero.png "Generate multi-modal synthetic data with Navigator Fine Tuning")
+
+If you’re new to Gretel, our Navigator Fine-Tuning blueprint is a great place to start. This blueprint automatically selects our comprehensive multi-modal model, a great one-stop shop for most synthetic data generation needs. Just answer a few questions, review the model configuration and hit **Run**. 
+
+Navigator Fine-Tuning supports mutliple tabular modalities of data within a single model, such as numeric, categorical, and free text data.
+
+Prefer coding? Check out the [SDK notebook](https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/demo/navigator-fine-tuning-intro-tutorial.ipynb) example.
+

use_cases/details/navigator-ft.md

@@ -1,6 +1,6 @@
 ![Generate synthetic tabular, text and time series data](https://blueprints.gretel.cloud/use_cases/images/navigator-ft-hero.png "Generate synthetic tabular, text and time series data")
 
-We are excited to announce the public preview of **Navigator Fine Tuning**, the latest advancement in our suite of synthetic data solutions. This new feature builds upon the recent general availability of [Gretel Navigator](https://console.gretel.ai/navigator), enabling you to generate data not only from a prompt, but also from fine-tuning the underlying model on your domain-specific real-world datasets to generate the highest quality synthetic data.
+**Navigator Fine Tuning** is the latest advancement in our suite of synthetic data solutions. It builds upon the recent general availability of [Gretel Navigator](https://console.gretel.ai/navigator), enabling you to generate data not only from a prompt, but also from fine-tuning the underlying model on your domain-specific real-world datasets to generate the highest quality synthetic data.
 
 One of the standout features of Navigator Fine Tuning is its support for multiple tabular data modalities within a single model. This means you can now generate datasets that maintain correlations across:
 - Numeric Data: Continuous or discrete numbers

use_cases/details/synthetic.md

@@ -1,6 +1,6 @@
 ![Generate synthetic tabular data](https://blueprints.gretel.cloud/use_cases/images/synthetic-tabular-generation.png "Generate synthetic tabular data")
 
-If you’re new to Gretel, our synthetic data blueprint is a great place to start. This gentle introduction to synthetic data generation automatically selects our popular [ACTGAN model](https://gretel.ai/blog/scale-synthetic-data-to-millions-of-rows-with-actgan) and provides a sample healthcare dataset. Just answer a few questions, review the model configuration and hit **Run**. 
+The synthetic data blueprint is a great introduction to synthetic data generation using our [ACTGAN model](https://gretel.ai/blog/scale-synthetic-data-to-millions-of-rows-with-actgan) for numeric and categorical data using a sample healthcare dataset. Just answer a few questions, review the model configuration and hit **Run**. 
 
 Prefer coding? Check out the [Gretel 101 notebook](https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/sdk_blueprints/Gretel_101_Blueprint.ipynb) example. Synthesize data in just 4 lines of code! 
 

use_cases/gretel.json

@@ -17,62 +17,22 @@
       }
     },
     {
-      "gtmId": "use-case-synthetic",
-      "title": "Generate synthetic data from complex tabular datasets",
-      "description": "Handle high-dimensional data with thousands of columns and millions of rows.",
-      "cardType": "Console",
-      "icon": "synthetics.png",
-      "detailsFileName": "synthetic.md",
-      "modelType": "synthetics",
-      "modelCategory": "synthetics",
-      "defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml",
-      "sampleDataset": {
-        "fileName": "sample-synthetic-healthcare.csv",
-        "description": "Use this sample electronic health records (EHR) dataset to synthesize an entirely new set of statistically equivalent records.",
-        "records": 9999,
-        "fields": 18,
-        "trainingTime": "6 mins",
-        "bytes": 830021
-      },
-      "button1": {
-        "label": "Gretel 101 Notebook",
-        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/sdk_blueprints/Gretel_101_Blueprint.ipynb"
-      },
-      "button2": {
-        "label": "Advanced Examples Notebook",
-        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/sdk_blueprints/Gretel_Advanced_Tabular_Blueprint.ipynb"
-      }
-    },
-    {
-      "gtmId": "use-case-navigator-ft",
-      "title": "[Public Preview] Generate synthetic tabular, text and time series data with Navigator Fine Tuning ",
-      "description": "Try out our latest synthetic model supporting tabular, text, JSON and time series data in a single dataset.",
+      "gtmId": "use-case-navigator-ft-simple",
+      "title": "Generate multi-modal synthetic data with Navigator Fine Tuning",
+      "description": "Try out our latest synthetic model to combine numeric data, categorical data, free text data, and more in a single dataset.",
       "cardType": "Console",
-      "tag": "Preview",
-      "icon": "navigator-ft.png",
-      "detailsFileName": "navigator-ft.md",
+      "tag": "New",
+      "icon": "navigator-ft-simple.png",
+      "detailsFileName": "navigator-ft-simple.md",
       "modelType": "navigator_ft",
       "modelCategory": "synthetics",
-      "defaultConfig": "config_templates/gretel/synthetics/navigator-ft.yml",
-      "sampleDataset": {
-        "fileName": "sample-patient-events.csv",
-        "description": "This medical dataset contains sequences of annotated events (such as hospital admission, diagnosis, treatment, etc.) for 1,712 synthetic patients.",
-        "records": 7348,
-        "fields": 17,
-        "trainingTime": "25 mins",
-        "bytes": 2386363
-      },
-      "button1": {
-        "label": "SDK Notebook",
-        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/demo/navigator-fine-tuning-intro-tutorial.ipynb"
-      }
+      "defaultConfig": "config_templates/gretel/synthetics/navigator-ft.yml"
     },
     {
       "gtmId": "use-case-redact-pii",
       "title": "Transform unstructured data into AI-ready formats",
       "description": "De-identify, transform, or label text and tabular data for AI.",
       "cardType": "Console",
-      "tag": "New",
       "icon": "transform.png",
       "modelType": "transform_v2",
       "modelCategory": "transform",
@@ -86,6 +46,22 @@
         "bytes": 5647
       }
     },
+    {
+      "gtmId": "use-case-gpt-dp",
+      "title": "Create free text data with privacy guarantees",
+      "description": "Leverage differentially private fine-tuning with GPT to generate a provably-private version of your free text data.",
+      "cardType": "Console",
+      "tag": "New",
+      "icon": "GPTwithDP.png",
+      "detailsFileName": "gpt-dp.md",
+      "modelType": "gpt_x",
+      "modelCategory": "synthetics",
+      "defaultConfig": "config_templates/gretel/synthetics/natural-language-differential-privacy.yml",
+      "button1": {
+        "label": "SDK Notebook",
+        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/generate_differentially_private_synthetic_text.ipynb"
+      }
+    },
     {
       "gtmId": "use-case-gretel_tuner",
       "title": "Optimize your synthetic data",
@@ -102,6 +78,33 @@
         "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/demo/gretel-tuner-advanced-tutorial.ipynb"
       }
     },
+    {
+      "gtmId": "use-case-synthetic",
+      "title": "Generate synthetic data from complex tabular datasets",
+      "description": "Handle high-dimensional data with thousands of columns and millions of rows.",
+      "cardType": "Console",
+      "icon": "synthetics.png",
+      "detailsFileName": "synthetic.md",
+      "modelType": "synthetics",
+      "modelCategory": "synthetics",
+      "defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml",
+      "sampleDataset": {
+        "fileName": "sample-synthetic-healthcare.csv",
+        "description": "Use this sample electronic health records (EHR) dataset to synthesize an entirely new set of statistically equivalent records.",
+        "records": 9999,
+        "fields": 18,
+        "trainingTime": "6 mins",
+        "bytes": 830021
+      },
+      "button1": {
+        "label": "Gretel 101 Notebook",
+        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/sdk_blueprints/Gretel_101_Blueprint.ipynb"
+      },
+      "button2": {
+        "label": "Advanced Examples Notebook",
+        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/sdk_blueprints/Gretel_Advanced_Tabular_Blueprint.ipynb"
+      }
+    },
     {
       "gtmId": "use-case-tabular-dp",
       "title": "Create provably private versions of sensitive data",
@@ -165,6 +168,29 @@
         "bytes": 63000
       }
     },
+    {
+      "gtmId": "use-case-navigator-ft",
+      "title": "Generate synthetic tabular, text and time series data with Navigator Fine Tuning ",
+      "description": "Try out our latest synthetic model supporting tabular, text, JSON and time series data in a single dataset.",
+      "cardType": "Console",
+      "icon": "navigator-ft.png",
+      "detailsFileName": "navigator-ft.md",
+      "modelType": "navigator_ft",
+      "modelCategory": "synthetics",
+      "defaultConfig": "config_templates/gretel/synthetics/navigator-ft.yml",
+      "sampleDataset": {
+        "fileName": "sample-patient-events.csv",
+        "description": "This medical dataset contains sequences of annotated events (such as hospital admission, diagnosis, treatment, etc.) for 1,712 synthetic patients.",
+        "records": 7348,
+        "fields": 17,
+        "trainingTime": "25 mins",
+        "bytes": 2386363
+      },
+      "button1": {
+        "label": "SDK Notebook",
+        "link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/demo/navigator-fine-tuning-intro-tutorial.ipynb"
+      }
+    },
     {
       "gtmId": "use-case-transform-database",
       "title": "Redact PII in a database",