nibble dispatch
By Florent (nibble.ai)
issues
nibble.ai dispatch - Issue #33: Wizards of Oz, MLOps and Lifecycle Management, Netflix open-sources Metaflow, and more...
Hope you liked this issue. If you did, make sure to give it a thumbs up 👍!
nibble.ai dispatch - Issue #32: DevOps principles for AI/ML, Steam powered days of ML, How to recognize AI snake oils, Hacking neural networks, and more...
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nibble.ai weekly - Issue #31: Continuous Delivery for ML, Why is “hers” not recognized as pronoun?, Coding habits for data scientists, Functional programming, and more...
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nibble.ai weekly - Issue #30: investigating OpenAI, the risks of AutoML, Hyperopt on Spark, and more...
If you liked this edition, it's quite likely your colleagues would enjoy it too! You can simply forward this email or Don't forget to share this so they can enjoy it.On a side note, we're organizing events to connect data professionals in Paris. If you're rea…
nibble.ai weekly - Issue #29: How did data get so big? Organizing machine learning projects. Netflix open-sources Polynote, and more...
Call for speakers in Paris 🇫🇷We’re looking for speakers for community events in Paris to share good practices about operationalizing data science.If you’re working on improving the lifecycle of data science project within your organization and want to share y…
nibble.ai weekly - Issue #28: The limits of Deep Learning, The state of ML frameworks in 2019, and more...
Call for speakers in Paris 🇫🇷We're looking for speakers for community events in Paris to share good practices about operationalizing data science.If you're working on improving the lifecycle of data science project within your organization and want to share y…
nibble.ai weekly - Issue #27: lessons learned at Booking.com, which flavors of data professional are you, and more...
If you found something particularly useful, I would love to know, please reach out to me at [email protected] with "nibble.ai weekly" in the subject.Have a great week. Florent
nibble.ai weekly - Issue #26: Data Tooling Market 2019, Data Science is boring, Full stack Deep Learning and more...
Data science is boringA provocative title for an interesting read.Observations, opinions and advices from a Data Science manager who leads teams to deploy ML systems at Fortune-100 enterprises.Many people cherrypick the exciting parts of doing Data Science (o…
nibble.ai weekly - Issue #25: Continuous Delivery for ML, Moonshots vs boring, Reproductibility crisis...
Forget moonshots and think boringIf enterprises ever want to see the benefits of AI, they must embrace the mundane.I’m sure the moonshots are possible if you’re a tech giant and you have billions of dollars to spend on experimenting. However, even Jeff Bezos …
nibble.ai weekly - Issue #24: Machine Learning, faster, How to learn D3.js...
5 Machine Learning Lessons for Product ManagersSome thoughtful advice on how to integrate ML into your product. mindtheproduct.comML takes time and effort to build into your product. You need good people, good data and a good number of iterations to achieve s…
nibble.ai weekly - Issue #23: Making data science more useful, deploying AI without technical debt...
A great model is not enoughDeploying AI without technical debtAn overview of good practices from agile development, DevOps and statistical process control to minimize technical debt, reduces cycle time and improves code and data quality.AI and Data Science ar…
nibble.ai weekly - Issue #22: Insights from Transform 2019, New fast.ai course for NLP, and more...
Don't Bet on AI (yet)Behind the (quite) provocative title, an interesting read that stems from an analysis of 7000 "AI Startups".AI is the new electricity. It will transform industries. But like electricity, it will take decades. Today is 1882 in the world of…
nibble.ai weekly - Issue #21: 2019 review of the data/AI ecosystem, Retraining your entire org for AI & ML, fast.ai Deep Learning Foundations
AI and ML will require retraining your entire organizationTo successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce. oreilly.comAI and ML are going to impact and permeate mo…
nibble.ai weekly - Issue #20: A.I. hype, ML pipelines, history and future of ML...
History and future of Machine Learning 🎙How have we gotten to this point with machine learning? And where are we going?An interview with one of the OG researchers and teachers of machine learning, Professor Tom Mitchell of Carnegie Mellon University. a16z.com
nibble.ai weekly - Issue #19: ML teams people problem, Deep Learning for Life Sciences, a collection of deep learning models...
Deep Learning for the Life SciencesAdapted from the recently released O'Reilly book Deep Learning for the Life Sciences, one of its author (general partner at a16z) shares the three biggest questions the practitioner wanting to use AI in the life sciences sho…
nibble.ai weekly - Issue #18: "One, Two, Automate", TensorFlow 2, Pycon 2019 and much more!!
The role of a Data Science ManagerDriving impact + Building a world class teamWhether, you're a data manager, aspiring to be one or working in a data science team you will want to read this article by the data science team at Sequoia Capital.
nibble.ai weekly #17: The Why behind Data Science, How to deploy ML models, Software development best practices for deep learning, ML/AI cheatsheets, Scala tutorial
Tukey, Design Thinking, and Better QuestionsRoger Peng, the author of this post, reads John Tukey’s paper “The Future of Data Analysis” every year. Though it's a very good read, 62 pages might be a bit too much to pack within your busy day, just get started w…
nibble.ai weekly #16: A framework for understanding the data journey, The evolution of Data Engineering, Maths for ML and DS foundations...
While so many people are trying to enter the field of data science, it's easy to get misguided and focus on the wrong stuff. Peter Scobas shares his experience on how he made the jump from economics to data science: https://peterscobas.com/2019/04/26/how-i-be…
nibble.ai weekly #15: Data Scientist vs Data Engineer. New Udacity Tensorflow 2.0 course...
I recently talked to a few people wondering about the differences between data science and data engineering, these articles should enlighten you:Why a data scientist is not a data engineer (O'Reilly)How to Become a Data Engineer?
nibble.ai weekly #14: decentralized ML, operationalization, Stanford's CS224N winter's videos, O'Reilly's Python books
In this week's edition: decentralized ML, operationalization, Stanford's CS224N winter's videos, O'Reilly's Python books for a bargain, and more!