Nov
2020 Dec Opinions
All (82) | News, Education (12) | Opinions (15) | Top Stories, Tweets (8) | Tutorials, Overviews (47)
- Data Catalogs Are Dead; Long Live Data Discovery, by Debashis Saha & Barr Moses - Dec 28, 2020.
Why data catalogs aren’t meeting the needs of the modern data stack, and how a new approach – data discovery – is needed to better facilitate metadata management and data reliability.
- How to easily check if your Machine Learning model is fair?, by Jakub Wisniewski - Dec 24, 2020.
Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.
- Can you trust AutoML?, by Ioannis Tsamardinos - Dec 23, 2020.
Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.
- 5 strategies for enterprise machine learning for 2021, by Leah Kolben - Dec 22, 2020.
While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.
- MLOps Is Changing How Machine Learning Models Are Developed, by Henrik Skogstrom - Dec 21, 2020.
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.
- MLOps – “Why is it required?” and “What it is”?, by Bose & Aggarwal - Dec 18, 2020.
Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.
- Navigate the road to Responsible AI, by Ben Lorica - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
- Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning, by Gregory Piatetsky - Dec 16, 2020.
We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?
- Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds, by Ramesh & Teki - Dec 15, 2020.
Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.
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State of Data Science and Machine Learning 2020: 3 Key Findings, by Matthew Mayo - Dec 15, 2020.
Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here. - 6 Things About Data Science that Employers Don’t Want You to Know, by Terence Shin - Dec 14, 2020.
As is the potential for any "trending hot" career, the reality of a position in the field may not be all that you initially expected. Data Science is no exception, and being still a young field, its evolving definition can offer some surprises that you should know about before accepting that dream offer.
- A Journey from Software to Machine Learning Engineer, by Guillermo Carrasco - Dec 10, 2020.
In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.
- Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology, by Gregory Piatetsky - Dec 9, 2020.
Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
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Why the Future of ETL Is Not ELT, But EL(T), by John Lafleur - Dec 4, 2020.
The well-established technologies and tools around ETL (Extract, Transform, Load) are undergoing a potential paradigm shift with new approaches to data storage and expanding cloud-based compute. Decoupling the EL from T could reconcile analytics and operational data management use cases, in a new landscape where data warehouses and data lakes are merging. -
AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021, by Matthew Mayo - Dec 3, 2020.
2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
