neuralmagic · robertgshaw2-redhat · Dec 9, 2022 · Nov 10, 2022 · Nov 10, 2022 · Nov 15, 2022
diff --git a/src/content/details.mdx b/src/content/details.mdx
@@ -6,4 +6,4 @@ index: 5000
 skipToChild: True
 ---
 
-# Details
+# Details
diff --git a/src/content/details/faqs.mdx b/src/content/details/faqs.mdx
@@ -1,8 +1,8 @@
 ---
 title: "FAQs"
 metaTitle: "FAQs"
-metaDescription: "FAQs for the DeepSparse product from Neural Magic"
-index: 2000
+metaDescription: "FAQs for the Neural Magic Platform"
+index: 4000
 ---
 
 # FAQs
@@ -11,22 +11,22 @@ index: 2000
 
 **What is Neural Magic?**
 
-Founded by a team of award-winning MIT computer scientists and funded by Amdocs, Andreessen Horowitz, Comcast Ventures, NEA, Pillar VC, and
-Ridgeline Partners, Neural Magic is the creator and maintainer of the Deep Sparse Platform. It has several components, including the
-[DeepSparse Engine,](/products/deepsparse) a CPU runtime that runs sparse models at GPU speeds. To enable companies the ability to use
-ubiquitous and unconstrained CPU resources, Neural Magic includes [SparseML](/products/sparseml) and the [SparseZoo,](/products/sparsezoo)
-open-sourced model optimization technologies that allow users to achieve performance breakthroughs, at scale, with all the flexibility of software.
+Neural Magic was founded by a team of award-winning MIT computer scientists and is funded by Amdocs, Andreessen Horowitz, Comcast Ventures, NEA, Pillar 
+VC, and Ridgeline Partners. The Neural Magic Platform includes several components, including [DeepSparse,](/products/deepsparse), [SparseML]
+(/products/sparseml), and [SparseZoo[(/products/sparsezoo). DeepSparse is an inference runtime offering GPU-class performance on CPUs and tooling to 
+integrate ML into your application. [SparseML](/products/sparseml) and [SparseZoo,](/products/sparsezoo) are and open-source tooling and model repository 
+combination that enable you to create an inference-optimized sparse-model for deployment with DeepSparse.
 
-**What is the DeepSparse Engine?**
+Together, these components remove the tradeoff between performance and the simplicity and scalability of software-delivered deployments.
 
-The DeepSparse Engine, created by Neural Magic, is a general purpose engine for machine learning, enabling machine learning to be practically
-run in new places, on new kinds of workloads. It delivers state of art, GPU-class performance for the deep learning applications running on x86
-CPUs. The DeepSparse Engine achieves its performance using breakthrough algorithms that reduce the computation needed for neural network execution
-and accelerate the resulting memory-bound computation.
+**What is DeepSparse?**
+
+DeepSparse, created by Neural Magic, is an inference runtime for deep learning models. It delivers state of art, GPU-class performance on commodity CPUs
+as well as tooling for integrating a model into an application and monitoring models in production.
 
 **Why Neural Magic?**
 
-Learn more about Neural Magic and the DeepSparse Engine (formerly known as the Neural Magic Inference Engine).
+Learn more about Neural Magic and DeepSparse (formerly known as the Neural Magic Inference Engine).
 [Watch the Why Neural Magic video](https://youtu.be/zJy_8uPZd0o)
 
 **How does Neural Magic make it work?**
@@ -44,8 +44,8 @@ for our end users to train and infer on for their deep learning needs, and have
 Our inference engine supports all versions of TensorFlow <= 2.0; support for the Keras API is through TensorFlow 2.0.
 
 **Do you run on AMD hardware?**
-
-The DeepSparse Engine is validated to work on x86 Intel (Haswell generation and later) and AMD CPUs running Linux, with
+ 
+DeepSparse is validated to work on x86 Intel (Haswell generation and later) and AMD CPUs running Linux, with
 support for AVX2, AVX-512, and VNNI instruction sets. Specific support details for some algorithms over different microarchitectures
 [is available.](/user-guide/deepsparse-engine/hardware-support)
 
@@ -57,7 +57,7 @@ market adoption and deep learning use cases.
 We are actively working on ARM support and it’s slated for release late-2022. We would like to hear your use cases and keep you in the
 loop! [Contact us to continue the conversation.](https://neuralmagic.com/contact/)
 
-**To what use cases is the Deep Sparse Platform best suited?**
+**To what use cases is the Neural Magic Platform best suited?**
 
 We focus on the models and use cases related to computer vision and NLP due to cost sensitivity and both real time and throughput constraints.
 The belief now is GPUs are required for deployment.
@@ -98,18 +98,18 @@ ___
 
 **Which instruction sets are supported and do we have to enable certain settings?**
 
-AVX2, AVX-512, and VNNI. The DeepSparse Engine will automatically utilize the most effective available
+AVX2, AVX-512, and VNNI. DeepSparse will automatically utilize the most effective available
 instructions for the task. Depending on your goals and hardware priorities, optimal performance can be found.
 Neural Magic is happy to discuss your use cases and offer recommendations.
 
 **Are you suitable for edge deployments (i.e., in-store devices, cameras)?**
 
 Yes, absolutely. We can run anywhere you have a CPU with x86 instructions, including on bare metal, in the cloud,
 on-prem, or at the edge. Additionally, our model optimization tools are able to reduce the footprint of models
-across all architectures. We only guarantee performance in the DeepSparse Engine.
+across all architectures. We only guarantee performance in DeepSparse.
 
 We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases
-or how others are leveraging the Deep Sparse Platform, [please contact us.](https://neuralmagic.com/contact/)
+or how others are leveraging the Neural Magic Platform, [please contact us.](https://neuralmagic.com/contact/)
 Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools.
 
 **Do you have available solutions or applications on the Microsoft/Azure platform?**
@@ -119,10 +119,10 @@ We deploy extremely easily. We are completely infrastructure-agnostic. As long a
 
 **Can the inference engine run on Kubernetes? How do you containerize and take advantage of underlying infrastructure?**
 
-The DeepSparse Engine becomes a component of your model serving solution. As a result, it can
+DeepSparse becomes a component of your model serving solution. As a result, it can
 simply plug into an existing CI/CD deployment pipeline. How you deploy, where you deploy, and what you deploy on
-becomes abstracted to the DeepSparse Engine so you  can tailor your experiences. For example, you can run the
-DeepSparse Engine on a CPU VM environment, deployed via a Docker file and managed through a Kubernetes environment.
+becomes abstracted to DeepSparse so you can tailor your experiences. For example, you can run the
+DeepSparse on a CPU VM environment, deployed via a Docker file and managed through a Kubernetes environment.
 
 ___
 
@@ -141,7 +141,7 @@ Neural Magic, _[WoodFisher: Efficient Second-Order Approximation for Neural Netw
 
 **When does sparsification actually happen?**
 
-In a scenario in which you want to sparsify and then run your own model in the DeepSparse Engine, you would first
+In a scenario in which you want to sparsify and then run your own model with DeepSparse, you would first
 sparsify your model to achieve the desired level of performance and accuracy using Neural Magic’s [SparseML](/products/sparseml) tooling.
 
 **What does the sparsification process look like?**
@@ -166,9 +166,9 @@ hyperparameters are fully under your control and allow you the flexibility to ea
 
 **Do you support INT8 and INT16 (quantized) operations?**
 
-The DeepSparse Engine runs at FP32 and has support for INT8.  With Intel Cascade Lake generation chips and later,
+DeepSparse runs at FP32 and has support for INT8.  With Intel Cascade Lake generation chips and later,
 Intel CPUs include VNNI instructions and support both INT8 and INT16 operations. On these machines, performance improvements
-from quantization will be greater. The  DeepSparse Engine has INT8 support for the ONNX operators QLinearConv, QuantizeLinear,
+from quantization will be greater. DeepSparse has INT8 support for the ONNX operators QLinearConv, QuantizeLinear,
 DequantizeLinear, QLinearMatMul, and MatMulInteger. Our engine also supports 8-bit QLinearAdd, an ONNX Runtime custom operator.
 
 **Do you support FP16 (half precision) and BF16 operations?**
@@ -179,12 +179,12 @@ ___
 
 ## Runtime FAQs
 
-**Do users have to do any model conversion before using the DeepSparse Engine?**
+**Do users have to do any model conversion before using DeepSparse?**
 
-DeepSparse Engine executes on an ONNX (Open Neural Network Exchange) representation of a deep learning model.
+DeepSparse executes on an ONNX (Open Neural Network Exchange) representation of a deep learning model.
 Our software allows you to produce an ONNX representation. If working with PyTorch, we use the built-in ONNX
 export and for TensorFlow, we convert from a standard exported protobuf file to ONNX. Outside of those frameworks,
-you would need to convert your model to ONNX first before passing it to the DeepSparse Engine.
+you would need to convert your model to ONNX first before passing it to DeepSparse.
 
 **Why is ONNX the file format used by Neural Magic?**
 
@@ -212,6 +212,6 @@ Specifically for sparsification, our software keeps the architecture intact and
 
 **For a CPU are you using all the cores?**
 
-The DeepSparse Engine optimizes _how_ the model is run on the infrastructure resources applied to it. But, the Neural
+DeepSparse optimizes _how_ the model is run on the infrastructure resources applied to it. But, Neural
 Magic does not optimize for the number of cores. You are in control to specify how much of the system Neural Magic will use and run on.
 Depending on your goals (latency, throughput, and cost constraints), you can optimize your pipeline for maximum efficiency.
diff --git a/src/content/details/glossary.mdx b/src/content/details/glossary.mdx
@@ -124,7 +124,7 @@ The machine learning community includes a vast array of terminology that can hav
   </tr>
   <tr>
     <td>Unstructured pruning</td>
-    <td>A method for compressing a neural network. Unstructured pruning removes individual weight connections from a trained network. Software like Neural Magic's DeepSparse Engine runs these pruned networks faster.</td>
+    <td>A method for compressing a neural network. Unstructured pruning removes individual weight connections from a trained network. Software like Neural Magic's DeepSparse runs these pruned networks faster.</td>
   </tr>
   <tr>
     <td>VNNI</td>

diff --git a/src/content/get-started.mdx b/src/content/get-started.mdx
@@ -1,7 +1,7 @@
 ---
 title: "Get Started"
 metaTitle: "Get Started"
-metaDescription: "Getting started with the Neural Magic DeepSparse Platform"
+metaDescription: "Getting started with the Neural Magic Platform"
 index: 1000
 skipToChild: True
 ---

diff --git a/src/content/get-started/deploy-a-model.mdx b/src/content/get-started/deploy-a-model.mdx
@@ -1,13 +1,13 @@
 ---
 title: "Deploy a Model"
 metaTitle: "Deploy a Model"
-metaDescription: "Deploy a model with the DeepSparse server for easy and performant ML deployments"
+metaDescription: "Deploy a model with DeepSparse Server for easy and performant ML deployments"
 index: 5000
 ---
 
 # Deploy a Model
 
-The DeepSparse package comes pre-installed with a server to enable easy and performant model deployments.
+DeepSparse comes pre-installed with a server to enable easy and performant model deployments.
 The server provides an HTTP interface to communicate and run inferences on the deployed model rather than the Python APIs or CLIs.
 It is a production-ready model serving solution built on Neural Magic's sparsification solutions resulting in faster and cheaper deployments.
 

diff --git a/src/content/get-started/deploy-a-model/cv-object-detection.mdx b/src/content/get-started/deploy-a-model/cv-object-detection.mdx
@@ -9,9 +9,9 @@ index: 2000
 
 This page walks through an example of deploying an object detection model with DeepSparse Server.
 
-The DeepSparse Server is a server wrapper around `Pipelines`, including the object detection pipeline. As such,
+DeepSparse Server is a server wrapper around `Pipelines`, including the object detection pipeline. As such,
 the server provides and HTTP interface that accepts images and image files as inputs and outputs the labeled predictions.
-With all of this built on top of the DeepSparse Engine, the simplicity of servable pipelines is combined with GPU-class performance on CPUs for sparse models.
+In this way, DeepSparse combines the simplicity of servable pipelines with GPU-class performance on CPUs for sparse models.
 
 ## Install Requirements
 
@@ -20,12 +20,12 @@ This example requires [DeepSparse Server+YOLO Install](/get-started/install/deep
 ## Start the Server
 
 Before starting the server, the model must be set up in the format expected for DeepSparse `Pipelines`.
-See an example of how to setup `Pipelines` in the [Try a Model](../../try-a-model) section.
+See an example of how to setup `Pipelines` in the [Use a Model](../../use-a-model) section.
 
 Once the `Pipelines` are set up, the `deepsparse.server` command launches a server with the model at `--model_path` inside. The `model_path` can either
 be a SparseZoo stub or a path to a local `model.onnx` file.
 
-The command below shows how to start up the DeepSparse Server for a sparsified YOLOv5l model trained on the COCO dataset from the SparseZoo.
+The command below shows how to start up DeepSparse Server for a sparsified YOLOv5l model trained on the COCO dataset from the SparseZoo.
 The output confirms the server was started on port `:5543` with a `/docs` route for general info and a `/predict/from_files` route for inference.
 
 ```bash

diff --git a/src/content/get-started/deploy-a-model/nlp-text-classification.mdx b/src/content/get-started/deploy-a-model/nlp-text-classification.mdx
@@ -9,9 +9,9 @@ index: 1000
 
 This page walks through an example of deploying a text-classification model with DeepSparse Server.
 
-The DeepSparse Server is a server wrapper around `Pipelines`, including the sentiment analysis pipeline. As such,
+DeepSparse Server is a server wrapper around `Pipelines`, including the sentiment analysis pipeline. As such,
 the server provides an HTTP interface that accepts raw text sequences as inputs and responds with the labeled predictions.
-With all of this built on top of the DeepSparse Engine, the simplicity of servable pipelines is combined with GPU-class performance on CPUs for sparse models.
+In this way, DeepSparse combines the simplicity of servable pipelines with GPU-class performance on CPUs for sparse models.
 
 ## Install Requirements
 
@@ -20,12 +20,12 @@ This example requires [DeepSparse Server Install](/get-started/install/deepspars
 ## Start the Server
 
 Before starting the server, the model must be set up in the format expected for DeepSparse `Pipelines`.
-See an example of how to set up `Pipelines` in the [Try a Model](../../try-a-model) section.
+See an example of how to set up `Pipelines` in the [Use a Model](../../use-a-model) section.
 
 Once the `Pipelines` are set up, the `deepsparse.server` command launches a server with the model at `--model_path` inside. The `model_path` can either
 be a SparseZoo stub or a local model path.
 
-The command below starts up the DeepSparse Server for a sparsified DistilBERT model (from the SparseZoo) trained on the SST2 dataset for sentiment analysis.
+The command below starts up DeepSparse Server for a sparsified DistilBERT model (from the SparseZoo) trained on the SST2 dataset for sentiment analysis.
 The output confirms the server was started on port `:5543` with a `/docs` route for general info and a `/predict` route for inference.
 
 ```bash

diff --git a/src/content/get-started/install.mdx b/src/content/get-started/install.mdx
@@ -1,15 +1,16 @@
 ---
 title: "Installation"
-metaTitle: "Install Deep Sparse Platform"
-metaDescription: "Installation instructions for the Deep Sparse Platform including DeepSparse Engine, SparseML, SparseZoo"
+metaTitle: "Install Neural Magic Platform"
+metaDescription: "Installation instructions for the Neural Magic Platform including DeepSparse, SparseML, SparseZoo"
 index: 0
 ---
 
 # Installation
 
-The Neural Magic Platform is made up of core libraries that are available as Python APIs and CLIs.
-All Python APIs and CLIs are installed through pip utilizing [PyPI](https://pypi.org/user/neuralmagic/).
-We recommend you install in a [virtual environment](https://docs.python.org/3/library/venv.html) to encapsulate your local environment.
+The Neural Magic Platform contains several products: DeepSparse (available in two editions, Community and Enterprise), SparseML, and SparseZoo.
+
+Each package is installed with [PyPI](https://pypi.org/user/neuralmagic/). It is recommended to install in 
+a [virtual environment](https://docs.python.org/3/library/venv.html) to encapsulate your local environment.
 
 ## Installing the Neural Magic Platform
 
@@ -24,12 +25,12 @@ Now, you are ready to install one of the Neural Magic products.
 ## Installing Products
 
 <LinkCards>
-  <LinkCard href="./deepsparse" heading="DeepSparse">
-    Install the DeepSparse Community Edition for performant inference on CPUs.
+  <LinkCard href="./deepsparse" heading="DeepSparse Community">
+    Install DeepSparse Community for performant inference on CPUs in dev or testing environments.
   </LinkCard>
 
   <LinkCard href="./deepsparse-ent" heading="DeepSparse Enterprise">
-    Install the DeepSparse Enterprise Edition for performant inference on CPUs in production deployments.
+    Install DeepSparse Enterprise for performant inference on CPUs in production deployments.
   </LinkCard>
 
   <LinkCard href="./sparseml" heading="SparseML">

diff --git a/src/content/get-started/install/deepsparse-ent.mdx b/src/content/get-started/install/deepsparse-ent.mdx
@@ -1,17 +1,21 @@
 ---
 title: "DeepSparse Enterprise"
 metaTitle: "DeepSparse Enterprise Installation"
-metaDescription: "Installation instructions for the DeepSparse Engine enabling performant neural network deployments"
+metaDescription: "Installation instructions for DeepSparse enabling performant neural network deployments"
 index: 2000
 ---
 
-# DeepSparse Enterprise Edition Installation
+# DeepSparse Enterprise Installation
 
-The [DeepSparse Engine](/products/deepsparse-ent) enables GPU-class performance on CPUs, leveraging sparsity within models to reduce FLOPs and the unique cache hierarchy on CPUs to reduce memory movement.
-The engine accepts models in the open-source [ONNX format](https://onnx.ai/), which are easily created from PyTorch and TensorFlow models.
+[DeepSparse Enterprise](/products/deepsparse-ent) enables GPU-class performance on CPUs.
 
-Currently, DeepSparse is tested on Python 3.7-3.10, ONNX 1.5.0-1.10.1, ONNX opset version 11+ and is [manylinux compliant](https://peps.python.org/pep-0513/).
-It is limited to Linux systems running on x86 CPU architectures.
+Currently, DeepSparse is tested on Python 3.7-3.10, ONNX 1.5.0-1.10.1, ONNX opset version 11+, and [manylinux compliant systems](https://peps.python.org/pep-0513/).
+
+We currently support x86 CPU architectures.
+
+DeepSparse is available in two versions:
+1. [**DeepSparse Community**](/products/deepsparse) is free for evaluation, research, and non-production use with our [DeepSparse Community License](https://neuralmagic.com/legal/engine-license-agreement/).
+2. [**DeepSparse Enterprise**](/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications.
 
 ## Installing DeepSparse Enterprise
 
@@ -23,7 +27,7 @@ pip install deepsparse-ent
 
 ## Installing the Server
 
-The [DeepSparse Server](/use-cases/deploying-deepsparse/deepsparse-server) allows you to serve models and pipelines through an HTTP interface using the deepsparse.server CLI.
+[DeepSparse Server](/user-guide/deploying-deepsparse/deepsparse-server) allows you to serve models and pipelines through an HTTP interface using the deepsparse.server CLI.
 To install, use the following extra option:
 
 ```bash
@@ -37,6 +41,6 @@ To use YOLO models, install with the following extra option:
 
 ```bash
 pip install deepsparse-ent[yolo]         # just yolo requirements
-pip install deepsparse-ent[yolo,server] # both yolo + server requirements
+pip install deepsparse-ent[yolo,server]  # both yolo + server requirements
 ```