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rel="noopener noreferrer" href="/blog/star-history-open-source-2022-open-source-alternatives"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">2022 OSS Alternatives</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/star-history-yearly-pick-2022-frontend"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">2022 Front-end</span></a></li></ul></div><div><div class="w-full flex flex-row justify-between items-center my-2"><h3 class="text-sm font-medium text-gray-400 leading-6">Starlet List</h3></div><ul class="list-disc list-inside"><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/list-your-open-source-project"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">🎁 Prompt yours for FREE</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/trench"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #28 - Trench</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/langfuse"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #27 - langfuse</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/thepipe"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #26 - thepi.pe</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/taipy"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #25 - Taipy</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/superlinked"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #24 - Superlinked</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/tea-tasting"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #23 - tea-tasting</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/giskard"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #22 - Giskard</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/khoj"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #21 - Khoj</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/paradedb"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #20 - ParadeDB</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/skyvern"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #19 - Skyvern</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/prisma"><span class="inline -ml-2 text-sm 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text-sm text-blue-700 hover:underline">Issue #13 - Earthly</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/wasp"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #12 - Wasp</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/libsql"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #11 - libSQL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/postgresml"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #10 - PostgresML</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/electricsql"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #9 - ElectricSQL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/prompt-flow"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #8 - Prompt flow</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/clipboard"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #7 - Clipboard</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/hoppscotch"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #6 - Hoppscotch</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/metisfl"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #5 - MetisFL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/chatgpt-js"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #4 - chatgpt.js</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/mockoon"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #3 - Mockoon</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/dlta-ai"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #2 - DLTA-AI</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/sniffnet"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #1 - Sniffnet</span></a></li></ul></div></div></div><div class="w-full flex flex-col justify-start items-center"><div class="w-full p-4 md:p-0 mt-6 md:w-5/6 lg:max-w-6xl h-full flex flex-col justify-start items-center self-center"><img class="hidden md:block w-auto max-w-full object-scale-down" src="/assets/blog/postgresml/banner.webp" alt=""/><div class="w-auto max-w-6xl mt-4 md:mt-12 prose prose-indigo prose-xl md:prose-2xl flex flex-col justify-center items-center"><h1 class="leading-16">Starlet #10 PostgresML: The GPU-powered AI application database</h1></div><div class="w-full mt-8 mb-2 max-w-6xl px-2 flex flex-row items-center justify-center text-sm text-gray-900 font-semibold trackingwide uppercase"><div class="flex space-x-1 text-gray-500"><span class="text-gray-900">Montana Low</span><span aria-hidden="true"> · </span><time dateTime="2023-10-19T00:00:00.000Z">Oct 19, 2023</time><span aria-hidden="true"> · </span><span> 4 min read </span></div></div><div class="mt-8 w-full max-w-5xl prose prose-indigo prose-xl md:prose-2xl"><p><em>This is the tenth issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our <a href="/blog/list-your-open-source-project">announcement</a>.</em></p> <hr> <p><a href="https://postgresml.org/">PostgresML</a> cuts the complexity from AI so you can get your app to market faster. It’s the first GPU-powered AI application database – enabling users to build chatbots, search engines, forecasting apps and more with the latest NLP, LLM and ML models using the simplicity and power of SQL.</p> <h2>Problem</h2> <p>PostgresML provides all the functionality I wish I had working as a machine learning engineer at Instacart and scaling the company's data infrastructure. We needed a platform to quickly and reliably implement our online systems like our site search engine and real time fraud detection - crucial functions for ecommerce.</p> <p>Today’s machine learning apps and their practitioners are encountering the same problems with complex and data hungry ML & AI apps. A leading cause of slowdowns is network latency due to microservices architecture. Several roundtrip network calls must be made across multiple services in the MLOPs ecosystem to return an accurate response. It’s slower for users, and it’s more complexity for engineers and product teams to build and manage.</p> <p>Who wants to wait more than a few seconds for search results or a response from a chatbot? And what engineer wants to manage more complexity?</p> <h2>Solution</h2> <p>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.</p> <p>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. Here’s what a typical chatbot build looks like with and without PostgresML:</p> <p><img src="/assets/blog/postgresml/diagram.webp" alt="diagram"></p> <p>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.</p> <h3>➡️ Scalability</h3> <p>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.</p> <h3>➡️ Efficiency</h3> <p>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 <strong>40x faster</strong> improvement over Python microservices.</p> <h3>➡️ All the latest LLMs, ML + AI algorithms</h3> <ul> <li>Torch</li> <li>Tensorflow</li> <li>SCikit Learn</li> <li>XGBoost</li> <li>LightGBM</li> <li>Pre-trained deep learning models from Hugging Face</li> <li>LLama 2</li> <li>Falcon LLM</li> <li>OpenAI</li> </ul> <h3>➡️ Open-source</h3> <p>We’re a totally open-source project. That includes various ML libraries and the vast Postgres ecosystem.</p> <h2>Examples</h2> <h3>Train</h3> <pre><code class="language-sql">SELECT pgml.train( 'Sales Forecasting', task => 'regression', relation_name => 'historical_sales', y_column_name => 'next_week_sales', algorithm => 'xgboost' ); </code></pre> <h3>Deploy</h3> <pre><code class="language-sql">SELECT pgml.deploy( 'Sales Forecasting', strategy => 'best_score', algorithm => 'xgboost' ); </code></pre> <h3>Predict</h3> <pre><code class="language-sql">SELECT pgml.predict( 'Sales Forecasting', ARRAY[ last_week_sales, week_of_year ] ) AS prediction ORDER BY prediction DESC; </code></pre> <h2>Future Development</h2> <p><a href="https://postgresml.org/">PostgresML</a> 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.</p> <p>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 <a href="https://github.com/postgresml/postgresml">Github</a> or chat with me personally anytime on our <a href="https://discord.com/invite/DmyJP3qJ7U">Discord</a>.</p> </div></div><div class="mt-12"><iframe src="https://embeds.beehiiv.com/2803dbaa-d8dd-4486-8880-4b843f3a7da6?slim=true" data-test-id="beehiiv-embed" height="52" frameBorder="0" scrolling="no" style="margin:0;border-radius:0px !important;background-color:transparent"></iframe></div></div><div class="w-full hidden lg:block"></div></div><footer class="relative w-full shrink-0 h-auto mt-6 flex flex-col justify-end items-center"><div class="w-full py-2 px-3 md:w-5/6 lg:max-w-7xl flex flex-row flex-wrap justify-between items-center text-neutral-700 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crossorigin="">{"props":{"pageProps":{"blog":{"title":"Starlet #10 PostgresML: The GPU-powered AI application database","slug":"postgresml","author":"Montana Low","featured":true,"featureImage":"/assets/blog/postgresml/banner.webp","publishedDate":"2023-10-19T00:00:00.000Z","excerpt":"PostgresML is the first GPU-powered AI application database - build chatbots, search engines, forecasting apps and more with the latest NLP, LLM and ML models using the simplicity and power of SQL.","readingTime":4},"parsedBlogHTML":"\u003cp\u003e\u003cem\u003eThis is the tenth issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our \u003ca href=\"/blog/list-your-open-source-project\"\u003eannouncement\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://postgresml.org/\"\u003ePostgresML\u003c/a\u003e cuts the complexity from AI so you can get your app to market faster. It’s the first GPU-powered AI application database – enabling users to build chatbots, search engines, forecasting apps and more with the latest NLP, LLM and ML models using the simplicity and power of SQL.\u003c/p\u003e\n\u003ch2\u003eProblem\u003c/h2\u003e\n\u003cp\u003ePostgresML provides all the functionality I wish I had working as a machine learning engineer at Instacart and scaling the company\u0026#39;s data infrastructure. We needed a platform to quickly and reliably implement our online systems like our site search engine and real time fraud detection - crucial functions for ecommerce.\u003c/p\u003e\n\u003cp\u003eToday’s machine learning apps and their practitioners are encountering the same problems with complex and data hungry ML \u0026amp; AI apps. A leading cause of slowdowns is network latency due to microservices architecture. Several roundtrip network calls must be made across multiple services in the MLOPs ecosystem to return an accurate response. It’s slower for users, and it’s more complexity for engineers and product teams to build and manage.\u003c/p\u003e\n\u003cp\u003eWho wants to wait more than a few seconds for search results or a response from a chatbot? And what engineer wants to manage more complexity?\u003c/p\u003e\n\u003ch2\u003eSolution\u003c/h2\u003e\n\u003cp\u003ePostgresML 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.\u003c/p\u003e\n\u003cp\u003eInstead 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. Here’s what a typical chatbot build looks like with and without PostgresML:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/postgresml/diagram.webp\" alt=\"diagram\"\u003e\u003c/p\u003e\n\u003cp\u003eWhy 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.\u003c/p\u003e\n\u003ch3\u003e➡️ Scalability\u003c/h3\u003e\n\u003cp\u003eIn 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.\u003c/p\u003e\n\u003ch3\u003e➡️ Efficiency\u003c/h3\u003e\n\u003cp\u003eIn 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 \u003cstrong\u003e40x faster\u003c/strong\u003e improvement over Python microservices.\u003c/p\u003e\n\u003ch3\u003e➡️ All the latest LLMs, ML + AI algorithms\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eTorch\u003c/li\u003e\n\u003cli\u003eTensorflow\u003c/li\u003e\n\u003cli\u003eSCikit Learn\u003c/li\u003e\n\u003cli\u003eXGBoost\u003c/li\u003e\n\u003cli\u003eLightGBM\u003c/li\u003e\n\u003cli\u003ePre-trained deep learning models from Hugging Face\u003c/li\u003e\n\u003cli\u003eLLama 2\u003c/li\u003e\n\u003cli\u003eFalcon LLM\u003c/li\u003e\n\u003cli\u003eOpenAI\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e➡️ Open-source\u003c/h3\u003e\n\u003cp\u003eWe’re a totally open-source project. That includes various ML libraries and the vast Postgres ecosystem.\u003c/p\u003e\n\u003ch2\u003eExamples\u003c/h2\u003e\n\u003ch3\u003eTrain\u003c/h3\u003e\n\u003cpre\u003e\u003ccode class=\"language-sql\"\u003eSELECT pgml.train(\n \u0026#39;Sales Forecasting\u0026#39;,\n task =\u0026gt; \u0026#39;regression\u0026#39;,\n relation_name =\u0026gt; \u0026#39;historical_sales\u0026#39;,\n y_column_name =\u0026gt; \u0026#39;next_week_sales\u0026#39;,\n algorithm =\u0026gt; \u0026#39;xgboost\u0026#39;\n);\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003eDeploy\u003c/h3\u003e\n\u003cpre\u003e\u003ccode class=\"language-sql\"\u003eSELECT pgml.deploy(\n \u0026#39;Sales Forecasting\u0026#39;,\n strategy =\u0026gt; \u0026#39;best_score\u0026#39;,\n algorithm =\u0026gt; \u0026#39;xgboost\u0026#39;\n);\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003ePredict\u003c/h3\u003e\n\u003cpre\u003e\u003ccode class=\"language-sql\"\u003eSELECT pgml.predict(\n \u0026#39;Sales Forecasting\u0026#39;,\n ARRAY[\n last_week_sales,\n week_of_year\n ]\n) AS prediction\nORDER BY prediction DESC;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003eFuture Development\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://postgresml.org/\"\u003ePostgresML\u003c/a\u003e 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.\u003c/p\u003e\n\u003cp\u003eIf 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 \u003ca href=\"https://github.com/postgresml/postgresml\"\u003eGithub\u003c/a\u003e or chat with me personally anytime on our \u003ca href=\"https://discord.com/invite/DmyJP3qJ7U\"\u003eDiscord\u003c/a\u003e.\u003c/p\u003e\n"},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"postgresml"},"buildId":"xKX4ZiOi_N7h3OBOEsSZu","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>