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AI adoption in the workforce

#artificialintelligence

Over the past few years, artificial intelligence has matured into a collection of powerful technologies that are delivering competitive advantage to businesses across industries. Global AI adoption and investment are soaring. By one account, 37 percent of organizations have deployed AI solutions--up 270 percent from four years ago.1 Analysts forecast global AI spending will more than double over the next three years, topping US$79 billion by 2022.2 Deloitte's State of AI in the Enterprise, 2nd Edition offers a global perspective of AI early adopters, based on surveying 1,900 IT and business executives from seven countries and a variety of industries.3 These adopters are increasing their spending on AI technologies and realizing positive returns. Almost two-thirds (65 percent) report that AI technologies are enabling their organizations to move ahead of the competition. Sixty-three percent of the leaders surveyed already view AI as "very" or "critically" important to their business success, and that number is expected to grow to 81 percent within two years.


Testing and Monitoring Machine Learning Model Deployments

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HOT & NEW, 4.8 (15 ratings), Created by Christopher Samiullah, Soledad Galli, English [Auto-generated] Learn how to test & monitor production machine learning models. You've taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren't any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.


Testing and Monitoring Machine Learning Model Deployments

#artificialintelligence

Learn how to test & monitor production machine learning models. You've taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren't any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.


The Data Science Behind Netflix

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Last year Netflix announced that it signed on 135 million Paid customers worldwide. Netflix's US Users' demographics perfectly represent the overall US population in terms of different factors like wealth, age and education. With no ads, Netflix's Business model relies on customers who subscribe to their service in the long run. The happier the customers are, the longer they stay subscribed to the service. This is why it is central to Netflix's business to identify and analyze factors that impact the viewer's enjoyment.


The Notebook Anti-Pattern - KDnuggets

#artificialintelligence

In the past few years there has been a large increase in tools trying to solve the challenge of bringing machine learning models to production. One thing that these tools seem to have in common is the incorporation of notebooks into production pipelines. This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way. Let's start by defining what these are, for those readers who haven't been exposed to notebooks, or call them by a different name. Notebooks are web interfaces that allow a user to create documents containing code, visualisations and text.


Deploy Machine Learning Models with Django

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The simplest approach is to run the ML algorithm locally to compute predictions on prepared test data and share predictions with others. This approach is easy and fast in implementation. However, it has many drawbacks. It is hard to govern, monitor, scale and collaborate. The second, similar approach, is to hard-code the ML algorithm in the system's code.


The assets available to robotising industry

#artificialintelligence

Robotics, increasingly employed across all industrial sectors, enables small and medium-sized enterprises and industries to improve their performance via a flexible, connected production system. In April 2019, a report on French robotics by French MP Bruno Bonnell and robotics expert Catherine Simon drew an encouraging picture of the change taking place in industry. Without sweeping France's shortcomings under the rug, the two authors focused on the country's many assets in the drive to, as the report's title puts it, "make France into an international champion in robotics and intelligent systems". "Over 50% of routine business tasks will be performed by machines by 2025" "Although industrial robots are being introduced more slowly in France than in other countries for the time being, the move is growing at a rate ranging between 6 and 13% per year through 2021," says Thomas Hoffmann, Business Development Director for the Actemium brand, the VINCI Energies network of automated industrial solutions integrators. As part of the move to digitalise businesses, robotics is a promising market, and an increasing number of small and medium-sized businesses and microenterprises will be seeking to automate their processes.


Top AI Research Advances For Machine Learning Infrastructure

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As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies. To help you stay aware of the latest research advances in ML infrastructure, we've summarized some of the most important research papers recently introduced in this area. As you read these summaries, you will be able to learn from the experience of the leading tech companies, including Google, Microsoft, and LinkedIn. The papers we've selected cover data labeling and data validation frameworks, different approaches to distributed training of ML models, a novel approach to tracking ML model performance in production, and more. If you'd like to skip around, here are the papers we've summarized: If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below.


Top AI Research Advances For Machine Learning Infrastructure

#artificialintelligence

As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies. To help you stay aware of the latest research advances in ML infrastructure, we've summarized some of the most important research papers recently introduced in this area. As you read these summaries, you will be able to learn from the experience of the leading tech companies, including Google, Microsoft, and LinkedIn. The papers we've selected cover data labeling and data validation frameworks, different approaches to distributed training of ML models, a novel approach to tracking ML model performance in production, and more. If you'd like to skip around, here are the papers we've summarized: If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below.


Maximising AI for your business

#artificialintelligence

We are living through a new industrial revolution in which artificial intelligence (AI) and machine learning (ML) permeate all industries and sectors of our economy, enhancing the impact and value of businesses worldwide. The UK has acknowledged this, and consequently is heavily investing in these technologies. But despite numerous successes, there have also been many failed AI projects that simply did not provide the expected return on investment. In many cases, the reasons for these failures are mismatched expectations and the overselling of AI's capabilities. Businesses must be better informed and trained to understand the limitations and expectations of what can be achieved when harnessing AI to solve their problems.