Top Machine Learning Newsletters to Stay Informed

In today's rapidly evolving technological landscape, machine learning (ML) is no longer a futuristic concept but a present-day reality. Organizations are increasingly relying on ML models to scale their operations, enhance employee productivity, uncover hidden insights from data, and validate underlying assumptions. This surge in adoption has created widespread interest in artificial intelligence (AI) and ML across various business lines and job roles. To make a disruptive organizational impact, AI and ML need to be understood and trusted. Newsletters provide a convenient way to receive regular updates directly in your inbox.

Why Subscribe to Machine Learning Newsletters?

Machine learning (ML) isn’t new. However, the field of big data is revitalizing the subject. Newsletters offer a curated stream of information, saving you time and effort in sifting through the vast amount of content available online. They deliver insights, news, and resources directly to your inbox, ensuring you stay up-to-date with the latest developments in the field.

Subscribing to machine learning newsletters can offer numerous benefits:

Stay informed: Keep abreast of the latest advancements, trends, and breakthroughs in AI and ML.
Save time: Receive curated content, eliminating the need to search for relevant information.
Gain insights: Access expert opinions, analysis, and commentary on industry developments.
Discover resources: Find tutorials, case studies, and tools to enhance your knowledge and skills.
Expand your network: Connect with industry professionals and thought leaders through newsletter communities.

Top Machine Learning Newsletters

Here's a carefully selected list of some of the top Artificial Intelligence and Machine Learning newsletters, considering factors like usefulness, relevance, length, author's experience, and number of subscribers:

1. The Variable

Curated by the team at Towards Data Science, The Variable is a valuable resource for anyone interested in the latest updates in AI and ML. This newsletter covers a wide range of topics, including cutting-edge research papers, industry trends, and insightful commentaries. Whether you’re a seasoned professional or just starting in the field, The Variable ensures you stay at the forefront of the ever-evolving world of AI.

2. Big Tech Digest

Delivered bi-weekly, Big Tech Digest brings you a collection of links to the latest engineering blog posts from over 100 Big Tech companies and startups like Airbnb, Uber, Netflix, or Meta. It is aimed at AI/ML professionals and Software Engineers at any level.

3. Data Science Weekly

True to its name, Data Science Weekly is a must-subscribe for data enthusiasts and AI professionals alike. This newsletter delivers a weekly roundup of the most relevant and intriguing updates in the data science realm. From tutorials and case studies to interviews with industry experts, Data Science Weekly offers a comprehensive overview of the latest happenings, making it an indispensable resource for anyone working in the field.

4. The Batch by DeepMind

For a unique perspective straight from the frontlines of AI research, look no further than The Batch by DeepMind. Authored by Andrew Ng and his team, this newsletter provides exclusive insights into the groundbreaking work happening at one of the leading AI research labs. Expect in-depth articles, research highlights, and a glimpse into the minds of the brilliant researchers shaping the future of AI.

5. Import AI

Import AI, penned by Jack Clark, the Strategy and Communications Director at OpenAI, is a must-read for those seeking a deeper understanding of the AI landscape. This newsletter dives into technical details, policy implications, and the societal impact of AI. Jack’s insightful commentary and well-researched summaries make Import AI an invaluable resource for staying informed on the multifaceted aspects of artificial intelligence.

6. Last Week in AI

Closing our list is Last Week in AI, a succinct and informative newsletter that condenses the week’s most significant AI news into a digestible format.

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Other Valuable Resources

Beyond the newsletters listed above, several other blogs and resources can help you stay informed about machine learning:

OpenAI: This organization, linked to the non-profit research company OpenAI, co-chaired by Elon Musk and Sam Altman, and sponsored by companies such as Amazon Web Services, Microsoft, and Infosys, aims to make AI accessible. Contributors discuss their collective efforts to promote and advance AI technologies through long-term research.
Distill: This platform focuses on making ML and AI more accessible for readers by communicating ML research in appealing, interactive data visualizations. It acts as a neutral platform for multiple authors to publish together, and articles are peer-reviewed, appearing in Google Scholar.
Machine Learning is Fun: This blog offers a valuable, introductory look at the tenets of ML through interactive tutorials and practical examples, making it easier to see the useful applications to different businesses and industries. Author Adam Geitgey is a former software developer who now consults organizations on implementing machine learning.
Machine Learning Mastery: Created by Jason, a machine learning developer with several AI-related degrees, this blog is intended for new developers getting started in the field, imparting lessons learned during his professional journey and sharing the tools that helped him most.
UC Berkeley Artificial Intelligence Research (BAIR) Blog: The artificial intelligence research department at UC Berkeley created this blog to convey research findings and important information about their AI-related work.
FastML: Run by economist Zygmunt Zając, this platform tackles interesting topics in machine learning with entertaining, easy to consume posts. It covers topics like overfitting, pointer networks, and chatbots.
TechTalks.tv: This media channel delivers comprehensive coverage of the latest AI-related technology and business news, designed to keep executives ahead of the curve with artificial intelligence and machine learning.
AWS Machine Learning Blog: Amazon is heavily involved in ML, using algorithms in nearly all areas of its business. The blog features projects and guides that reveal industry strides to readers and covers ML uses in Amazon Web Services technology. Algorithms suggest relevant products for customers in search results, recommend products based on recent purchases, and optimize faster product distribution and shipping from warehouses to customers.
Apple Machine Learning Journal: Apple's advancements in voice recognition, predictive text, and autocorrect leveraged for Siri signal some of its machine learning work. Apple Machine Learning Journal is a helpful look at how ML shapes their different technologies, and Apple engineers give perspective on how their work influences the transformation of ML.

Addressing Common Misconceptions

With greater understanding or appreciation for ML and AI, it’s easier to dispel the myths that may leave doubt about their full potential and to responsibly apply these productive solutions. A Tableau blog post recently explored three common machine learning misconceptions-reviewing them will help you discern fact from fiction in all the industry noise. For anyone ready to embrace these models and put them to work, Andrew Beers, CTO at Tableau Software, wrote about how to effectively and responsibly apply AI techniques taking cues from brands such as Box, eBay, OpenTable, and Slack.

Tools and Frameworks for Machine Learning

The rise of explainable AI is essential for building trust and understanding in machine learning models. Chief Scientist at the Institute for Ethical AI & ML Alejandro Saucedo covers multiple tools available to scale your production machine learning in a short talk at FOSDEM 2019. In this talk, Alejandro covers open source machine learning frameworks in explainability, model & data versioning and orchestration including DVC, Seldon, Pachyderm, CombustML, Algorithmia, SHAP, and more. Great post by the Seldon team on anomaly/outlier detection using machine learning for machine learning models. The post provides background on the different types of outlier detection, as well as the typical approaches used to identify them. Jason from Machine Learning Mastery brings us an excellent tutorial on transfer learning. Fascinating paper by Google on Federated Learning - a method that allows for machine learning models to be trained across multiple edge devices in the network instead on a central server. This means that models can be trained from mobile phones or laptops without requiring the data to be transferred to a central server. This is really exciting as it could have very positive impact on data privacy.

Quantum Machine Learning

For anyone looking to jump on the quantum hype train, the University of Toronto has launched a course on quantum machine learning.

Community and Events

We are excited to see the Awesome MLOps list growing. This week's edition is focused on industrial strength visualisation frameworks which fall on our Responsible ML Principle #5. seaborn - Seaborn is a Python visualization library based on matplotlib. We feature conferences that have core ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up. PyCon Belarus [15/02/2019] - The 5th edition of the Python conference in Minsk, Belarus. Big Data & AI Tech World [12/03/2019] - AI & Big Data Business conference in London, UK.

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