GITBOOK-76: Cassandra updated overview page: Please review!

Cassandra Stumer · gitbook-bot · commit 785815d47698 · 2023-11-06T18:56:48.000Z
diff --git a/pgml-docs/docs/guides/README.md b/pgml-docs/docs/guides/README.md
@@ -1,11 +1,70 @@
+---
+description: What is PostgresML?
+coverY: 0
+layout:
+  cover:
+    visible: false
+    size: full
+  title:
+    visible: true
+  description:
+    visible: true
+  tableOfContents:
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+  outline:
+    visible: true
+  pagination:
+    visible: true
+---
+
 # Overview
 
-PostgresML supercharges your Postgres database into an end-to-end MLOps platform, seamlessly integrating the key components of the machine learning workflow. Without moving data outside your database, PostgresML allows Postgres to function as a feature store, model store, training engine, and inference service all in one place. This consolidation streamlines building and deploying performant, real-time AI applications for developers.
+[PostgresML](https://postgresml.org/) is a PostgreSQL extension that enables users to build fast, simple and powerful models right inside your database. Users can build chatbots, search engines, forecasting apps and more with the latest NLP, LLM and ML models using the simplicity and power of SQL (and our SDKs).&#x20;
+
+### Problem <a href="#problem" id="problem"></a>
+
+Today's engineers and product teams are increasingly burdened with data hungry ML & AI apps and complex architecture. Several roundtrip network calls must be made across a web of microservices to return an accurate response. This approach to MLops is slower for users, and it’s more complex to build, manage and scale. &#x20;
+
+### Solution <a href="#solution" id="solution"></a>
+
+PostgresML collocates data and compute, so you can save models and index data right in your Postgres database. When you need to predict or train using machine learning, you don’t have to make requests over the internet or even take your data out of your database - which is both slower and a data safety risk. We’ve even added GPUs to our databases to fully leverage the latest technology and algorithms. You can avoid the complexity and latency of microservices when you have a complete MLops platform right in your database.
+
+Instead of patching together MongoDB, Databricks, Pinecone, Huggingface, LangChain and more to get the latest algorithms and LLMs in your app – you can add one extension to your Postgres database and get to market quickly.&#x20;
+
+
+
+![Typical chatbot build without and with PostgresML](https://star-history.com/blog/assets/postgresml/diagram.webp)
+
+
+
+Why Postgres? If it’s not broke, don’t fix it. Postgres is old reliable for a reason. It’s highly efficient, scalable and open-source (Just like all of PostgresML). Plus, you can easily build your AI app using simple SQL.
+
+#### ➡️ Scalability <a href="#scalability" id="scalability"></a>
+
+In ML applications, new data is constantly being generated by users and needs to be stored somewhere (feature store). For example, there are long term historical features, short term session level, and real time request level. Postgres can handle both long term with partitioning, table spaces and indexing as well as short term storage, and accept real-time features as parameters in queries, or as additional session level storage already written during the request.
+
+#### ➡️ Efficiency <a href="#efficiency" id="efficiency"></a>
+
+In database-ML minimizes latency and computational cost. Postgres can also handle all types of data efficiently; including vectors, geospatial, JSON, timeseries, tabular and text. Our benchmarks show up to **40x faster** improvement over Python microservices.
+
+#### ➡️ All the latest LLMs, ML + AI algorithms <a href="#all-the-latest-llms-ml-ai-algorithms" id="all-the-latest-llms-ml-ai-algorithms"></a>
+
+* Torch
+* Tensorflow
+* SCikit Learn
+* XGBoost
+* LightGBM
+* Pre-trained deep learning models from Hugging Face
+* LLama 2
+* Falcon LLM
+* OpenAI
+
+#### ➡️ Open-source <a href="#open-source" id="open-source"></a>
 
-With PostgresML, your database becomes a full-fledged ML workbench. It supports supervised and unsupervised algorithms like regression, clustering, deep neural networks, and more. You can build models using SQL on data inside Postgres. Models are stored back into Postgres for low-latency inferences later.
+We’re a totally open-source project. That includes various ML libraries and the vast Postgres ecosystem.
 
-PostgresML also unlocked the power of large language models like GPT-3 for your database. With just a few lines of SQL, you can leverage state-of-the-art NLP to build semantic search, analyze text, extract insights, summarize documents, translate text, and more. The possibilities are endless.
+### Future Development <a href="#future-development" id="future-development"></a>
 
-PostgresML is open source but also offered as a fully-managed cloud service. In addition to the SQL API, it provides Javascript, Python, and Rust SDKs to quickly build vector search, chatbots, and other ML apps in just a few lines of code.
+[PostgresML](https://postgresml.org/) is in early stages of development - but we are moving quickly. It’s our vision to help as many app developers as possible build and scale AI apps without all of the complexity.
 
-To scale horizontally, PostgresML utilizes PgCat, an advanced PostgreSQL proxy and load balancer. PgCat enables sharding, load balancing, failover, and mirroring to achieve extremely high throughput and low latency. By keeping the entire machine learning workflow within Postgres, PostgresML avoids expensive network calls between disparate systems. This allows PostgresML to handle millions of requests per second at up to 40x the speed of other platforms. PgCat and Postgres replication deliver seamless scaling while retaining transactional integrity.
+If you’d like to stay up-to-date with our progress or contribute to the project (feedback, questions and comments are all welcome) you can check us out on [Github](https://github.com/postgresml/postgresml) or chat with us anytime on our [Discord](https://discord.com/invite/DmyJP3qJ7U).
diff --git a/pgml-docs/docs/guides/deploying-postgresml/self-hosting/pooler.md b/pgml-docs/docs/guides/deploying-postgresml/self-hosting/pooler.md
@@ -115,6 +115,6 @@ Type "help" for help.
 postgresml=> SELECT pgml.version();
  version
 ---------
- 2.7.12
+ 2.7.9
 (1 row)
 ```
diff --git a/pgml-docs/docs/guides/developer-docs/contributing.md b/pgml-docs/docs/guides/developer-docs/contributing.md
@@ -126,7 +126,7 @@ SELECT pgml.version();
 postgres=# select pgml.version();
       version
 -------------------
- 2.7.12
+ 2.7.4
 (1 row)
 ```
 
diff --git a/pgml-docs/docs/guides/developer-docs/installation.md b/pgml-docs/docs/guides/developer-docs/installation.md
@@ -133,7 +133,7 @@ CREATE EXTENSION
 pgml_test=# SELECT pgml.version();
  version 
 ---------
- 2.7.12
+ 2.7.4
 (1 row)
 ```
 
diff --git a/pgml-docs/docs/guides/developer-docs/quick-start-with-docker.md b/pgml-docs/docs/guides/developer-docs/quick-start-with-docker.md
@@ -18,7 +18,7 @@ docker run \
     -v postgresml_data:/var/lib/postgresql \
     -p 5433:5432 \
     -p 8000:8000 \
-    ghcr.io/postgresml/postgresml:2.7.12 \
+    ghcr.io/postgresml/postgresml:2.7.3 \
     sudo -u postgresml psql -d postgresml
 ```
 
@@ -43,7 +43,7 @@ docker run \
     --gpus all \
     -p 5433:5432 \
     -p 8000:8000 \
-    ghcr.io/postgresml/postgresml:2.7.12 \
+    ghcr.io/postgresml/postgresml:2.7.3 \
     sudo -u postgresml psql -d postgresml
 ```
 
@@ -80,7 +80,7 @@ Time: 41.520 ms
 postgresml=# SELECT pgml.version();
  version 
 ---------
- 2.7.12
+ 2.7.3
 (1 row)
 ```
 

-Original file line number
+Diff line change
 postgresml=> SELECT pgml.version();
  version
 ---------
 - 2.7.12
 + 2.7.9
 (1 row)
 ```