Neil Ferguson, a popular epidemiologist from the imperial college, London, whose model forecasted the possible deaths due to COVID-19 with and without lockdown, has turned to be #inaccurate and the events followed has even led to his resignation.
An ex-Google software developer who goes by the pseudonym Sue Denim elaborated what is wrong with this model in his blog.
#AI #ML #DataScience #Analytics
#machine-learning #big-data #data-science #coronavirus
The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.
Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.
Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.
In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.
#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop
At SqlDBM BI database modeling tool help organizations to improve their decision and Analyze billions of records in seconds. Currently " Data Warehouse” is currently trending topic in the data area. We will covering what a Data Warehouse is and how it is created from a SQL script. Visit us to get know more about BI modeling Tools and how it work with SQL.
#export data model #SQL Server BI Modeling #BI modeling Tools #SQL Server Business Intelligence Modeling Tool
NLP Models have shown tremendous advancements in syntactic, semantic and linguistic knowledge for downstream tasks. However, that raises an interesting research question — is it possible for them to go beyond pattern recognition and apply common sense for word-sense disambiguation?
Thus, to identify if BERT, a large pre-trained NLP model developed by Google, can solve common sense tasks, researchers took a closer look. The researchers from Westlake University and Fudan University, in collaboration with Microsoft Research Asia, discovered how the model computes the structured, common sense knowledge for downstream NLP tasks.
According to the researchers, it has been a long-standing debate as to whether pre-trained language models can solve tasks leveraging only a few shallow clues and their common sense of knowledge. To figure that out, researchers used a CommonsenseQA dataset for BERT to solve multiple-choice problems.
#opinions #ai common sense #bert #bert model #common sense #nlp model #nlp models
In Part 1 of this series we examined the key differences between software and models; in Part 2 we explored the twelve traps of conflating models with software; and in Part 3 we looked at the evolution of models. In this article, we go through the model lifecycle, from the initial conception of the idea to build models to finally delivering the value from these models.
We breakdown the entire lifecycle of models into four major phases — scoping, discovery, delivery, and stewardship. While there are many similarities between this model lifecycle and a typical software lifecycle, there are significant differences as well, stemming from the differences between software and models that we started this series with. Here we go over the four phases and the nine steps within these phases.
#modeling #machine-learning #model #data-science #software-development
Over the past couple of years, many enterprise IoT applications have produced disappointing financial results. Several high-profile industrial IoT projects (which I will not mention here) got bogged down in implementation and were pared back substantially.
Some IoT products and solutions have done okay, but most have underperformed expectations. As of June 2019, Gartner’s highest-rated IoT Platforms — PTC, Software AG, and Hitachi — still had not risen to the ‘Leaders’ (Magic) quadrant.
What happened? Were the revenue models wrong? Or was the tech to blame? And what will happen next?
#iot #startup #scalable-revenue-modeling #business-models #edgecomputing #ai-applications #iot-top-story #iot-revenue-modelling