# Where to go next

Deep Learning has a strong open-source culture. Many great learning resources exist on blogs, lectures, tutorials, newsletters, course websites, and code repositories.

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This section is mostly up to you! [Submit a pull request](https://github.com/full-stack-deep-learning/course-gitbook) to add helpful resources.
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## Conferences

* [ScaledML](https://info.matroid.com/scaledml-media-archive-2020) (by Matroid)
* [MLOps Conference](https://www.youtube.com/playlist?list=PLH8M0UOY0uy6d_n3vEQe6J_gRBUrISF9m) (by Iguazio)
* [Spark + AI Summit](https://databricks.com/sparkaisummit/north-america-2020/agenda) (by Databricks)

## Newsletters

* [The Batch](https://www.deeplearning.ai/thebatch/) (by deeplearning.ai)
* [Machine Learning In Production](https://mlinproduction.com/machine-learning-newsletter/) (by Luigi Patruno)
* [Import AI](https://jack-clark.net/about/) (by Jack Clark)
* [The Machine Learning Engineer Newslette](https://ethical.institute/mle.html)r (by The Institute for Ethical AI & ML)
* [Projects To Know](https://projectstoknow.amplifypartners.com/ml-and-data) (by Amplify Partners)

## Personal Blogs

* [Locally Optimistic](https://locallyoptimistic.com/) (Data Leaders in NYC)
* [MLOps Tooling Landscape](https://huyenchip.com/2020/06/22/mlops.html) (by Chip Huyen)
* [Three Risks in Building Machine Learning Systems](https://insights.sei.cmu.edu/sei_blog/2020/05/three-risks-in-building-machine-learning-systems.html) (by Benjamin Cohen)
* [How To Serve Models](http://bugra.github.io/posts/2020/5/25/how-to-serve-model/) (by Bugra Akyildiz)
* [Nitpicking ML Technical Debt](https://matthewmcateer.me/blog/machine-learning-technical-debt/) (by Matthew McAteer)
* [Monitoring ML Models in Production](https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/) (by Christopher Samiullah)
* [Models for integrating data science teams within organizations](https://medium.com/@djpardis/models-for-integrating-data-science-teams-within-organizations-7c5afa032ebd) (Pardis Noorzad)
* [Data-as-a-Product vs Data-as-a-Service](https://medium.com/@itunpredictable/data-as-a-product-vs-data-as-a-service-d9f7e622dc55) (Justin Gage)

## Corporate Blogs

* [The New Business of AI](https://a16z.com/2020/02/16/the-new-business-of-ai-and-how-its-different-from-traditional-software/) (by a16z)
* [Long-Tailed AI Problems](https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/) (by a16z)
* [Rules of ML](https://developers.google.com/machine-learning/guides/rules-of-ml) (by Google)
* [Continuous delivery and automation pipelines in machine learning](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) (by Google)
* [Tecton: The Data Platform for Machine Learning](https://www.tecton.ai/blog/data-platform-ml/) (by Tecton)
* [Why We Need DevOps for ML Data](https://www.tecton.ai/blog/devops-ml-data/) (by Tecton)
* [Continuous Delivery for Machine Learning](https://martinfowler.com/articles/cd4ml.html) (by ThoughtWorks)
* [Dagster: The Data Orchestrator](https://medium.com/dagster-io/dagster-the-data-orchestrator-5fe5cadb0dfb) (by Elementl)
* [State of Machine Learning Model Servers In Production](https://anyscale.com/blog/heres-what-you-need-to-look-for-in-a-model-server-to-build-ml-powered-services/) (by Anyscale)

## Repositories

* [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning) (by The Institute for Ethical AI & Machine Learning)
* [MLOps References](https://ml-ops.org/content/references.html) (by InnoQ)
* [ML Applied in Production](https://github.com/eugeneyan/applied-ml) (by Eugene Yan)
* [Feature Stores for ML](http://featurestore.org/) (by KTH Royal Institute of Technology)
* [Feature Store: The Missing Data Layer in ML Pipelines?](https://www.logicalclocks.com/blog/feature-store-the-missing-data-layer-in-ml-pipelines) (by Logical Clocks)

## Tutorials

* [Deep Learning for OCR, Document Analysis, Text Recognition, and Language Modeling](https://github.com/tmbdev-tutorials/icdar2019-tutorial) (ICDAR 2019)
* [Image Retrieval in the Wild](https://matsui528.github.io/cvpr2020_tutorial_retrieval/) (CVPR 2020)
* [Handwritten Text Recognition (OCR) with MXNet Gluon](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet) (AWS Labs)