Jan2020 Feb Opinions
All (74) | Events (7) | News, Education (4) | Opinions (15) | Top Stories, Tweets (9) | Tutorials, Overviews (39)
- Learning from 3 big Data Science career mistakes
- Feb 25, 2020.
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.
- Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms
- Feb 24, 2020.
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
- 7 Data Trends for 2020 (and one non-trend)
- Feb 24, 2020.
This article discusses trends that will (and won't) take shape in 2020.
- Prepare for a Long Battle against Deepfakes
- Feb 21, 2020.
While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.
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The Death of Data Scientists – will AutoML replace them?
- Feb 20, 2020.
Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us? - In Loving Memory of Strictly-Typed Schemas
- Feb 20, 2020.
This article addresses one very peculiar manifestation of marketing propaganda in the big data industry that has crippled data engineers across the board — a resolute and methodical undermining of the sanctity of strictly-typed schemas.
- Hand labeling is the past. The future is #NoLabel AI
- Feb 19, 2020.
Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?
- Scaling the Wall Between Data Scientist and Data Engineer
- Feb 17, 2020.
The educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.
- What Does it Mean to Deploy a Machine Learning Model?
- Feb 14, 2020.
You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.
- Why Did I Reject a Data Scientist Job?
- Feb 12, 2020.
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
- AI and Machine Learning In Our Every Day Life
- Feb 7, 2020.
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
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The Data Science Puzzle — 2020 Edition
- Feb 7, 2020.
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here. - The Future of Machine Learning Will Include a Lot Less Engineering
- Feb 6, 2020.
Despite getting less attention, the systems-level design and engineering challenges in ML are still very important — creating something useful requires more than building good models, it requires building good systems.
- Top 5 Data Science Trends for 2020
- Feb 4, 2020.
As Data Science continues to expand into the next decade, this article features five important trends in the field that are expected in 2020. Leverage these trends to help improve your business processes for maximizing growth.
- Why are Machine Learning Projects so Hard to Manage?
- Feb 3, 2020.
What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.
