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MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for machine learning operations.Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models.

MLOps is the natural progression of DevOps in the context of AI, said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC).While it leverages DevOps' focus on security, compliance, and management of IT resources, MLOps real emphasis is on the consistent and smooth development of models and their scalability.

The origins of MLOps goes back to 2015 from a paper entitled Hidden Technical Debt in Machine Learning Systems.And since then, the growth has been particularly strong.Consider that the market for MLOps solutions is expected to reach $4 billion by 2025.

Putting ML models in production, operating models, and scaling use cases has been challenging for companies due to technology sprawl and siloing, said Santiago Giraldo, who is the Senior Product Marketing Manager and Data Engineer at Cloudera.In fact, 87% of projects dont get past the experiment phase and therefore, never make it into production.

Then how can MLOps help?Well, the handling of data is a big part of it.

Some key best practices are having a reproducible pipeline for data preparation and training, having a centralized experiment tracking system with well-defined metrics, and implementing a model management solution that makes it easy to compare alternative models across various metrics and roll back to an old model if there is a problem in production, said Matei Zaharia, who is the chief technologist at Databricks.These tools make it easy for ML teams to understand the performance of new models and catch and repair errors in production.

Something else to consider is that AI models are subject to change.This has certainly been apparent with the COVID-19 pandemic.The result is that many AI models have essentially gone haywire because of the lack of relevant datasets.

People often think a given model can be deployed and continue operating forever, but this is not accurate, said Randy LeBlanc, who is the VP of Customer Success at RapidMiner.Like a machine, models must be continuously monitored and maintained over time to see how theyre performing and shifting with new dataensuring that theyre delivering real, ongoing business impact.MLOps also allows for faster intervention when models degrade, meaning greater data security and accuracy, and allows businesses to develop and deploy models at a faster rate. For example, if you discovered an algorithm that will save you a million dollars per month, every month this model isnt in production or deployment costs you $1 million.

MLOps also requires rigorous tracking that is based on tangible metrics.If not, a project can easily go off the rails.When monitoring models, you want to have standard performance KPIs as well as those that are specific to the business problem, said Sarah Gates, who is an Analytics Strategist at SAS.This should be through a central location regardless of where the model is deployed or what language it was written in.That tracking should be automatedso you immediately know and are alertedwhen performance degrades.Performance monitoring should be multifaceted, so you are looking at your models from different perspectives.

While MLOps tools can be a huge help, there still needs to be discipline within the organization.Success is more than just about technology.

"Monitoring/testing of models requires a clear understanding of the data biases," said Michael Berthold, who is the CEO and co-founder of KNIME. "Scientific research on event, model change, and drift detection has most of the answers, but they are generally ignored in real life. You need to test on independent data, use challenger models and have frequent recalibration. Most data science toolboxes today totally ignore this aspect and have a very limited view on 'end-to-end' data science."

Tom (@ttaulli) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the COBOL programming language.

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MLOps: What You Need To Know - Forbes

AI advances

More than a decade ago, Nicholas Carr,in his workDoes IT Matter, suggested that the widespread availability and low prices of technology made it more of a utility like electricity or water, versus a competitive differentiator. This may be happening with artificial intelligence to some degree.

It appears that AIs early adopter phase is ending; the market is now moving into the early majority chapter of this maturing set of technologies, write Beena Ammanath, David Jarvis and Susanne Hupfer, all with Deloitte, in their most recent analysis of the enterprise AI space. Early-mover advantage may fade soon. As adoption becomes ubiquitous, AI-powered organizations may have to work harder to maintain an edge over their industry peers.

Seventy-four prevent of 2,727 executives responding to a Deloitte survey agree that AI will be integrated into all of their enterprise applications within three years. Although adopters are still bullish on AI, their advantage may wane as barriers to adoption fall and usage grows, Ammanath and her co-authors state. Organizations are finding it easier and easier to employ AI technologies. Data science and machine learning platforms have proliferated; AI-optimized hardware is providing greater compute power. It is now easier to train algorithms through self-service data preparation tools, synthetic data, small data, and pretrained models.

It is increasingly clear that we are on the path toward an era of pervasive AI, they add. The challenge now is leveraging AI in innovative ways to maintain its advantages, the study finds. For example, much of the work with AI is still confined to managing IT systems. In addition, there still isnt enough AI talent to go around.

At least 26% of the companies surveyed can be considered seasoned AI adopters, meaning they have undertaken a large number of AI production deployments and have developed a high level of AI expertise across the board. These AI leaders are still seeing competitive advantage 45% of this group said that AI technologies have enabled them to establish a significant lead over their competitors, versus 26% of the entire sample.

Still, this means a majority of even the most advanced AI companies, 55%, still arent seeing competitive advantage. Part of this may be due to the fact that AI is still confined to IT departments and functions, including cybersecurity Forty-seven percent of respondents indicated that IT was one of the top two functions for which AI was primarily used, the survey shows.

This could mean that companies are using AI for IT-related applications such as analyzing IT infrastructure for anomalies, automating repetitive maintenance tasks, or guiding the work of technical support teams, Ammanath and her co-authors note. Tellingly, business functions such as marketing, human resources, legal, and procurement ranked at the bottom of the list of AI-driven functions.

An area that needs work is finding or preparing individuals to work with AI systems. Fewer than half of executives (45%) say they have a high level of skill around integrating AI technology into their existing IT environments, the survey shows. This could include data science and machine learning platforms, enterprise applications powered by AI, tools for developing conversation interfaces, and low-code or no-code tools. Across all these different technology areas, 93% are using cloud-based AI capabilities, while 78% employ open-source AI capabilities.

Ammanath and her team offer some suggestions for keeping the edge with AI:

Pursue creative approaches. Take inspiration from inventive use cases to develop solutions that are both useful and novel.

Push boundaries. Expand your view of what may be possible to accomplish with AI technologies. Try to pursue a more diverse portfolio of projects that could potentially enhance multiple business functions across the enterprise.

Create the new. Look to develop new AI-powered products and services that take advantage of the technologies ability to learn and solve problems that humans cant.

Expand the circle. Move AI beyond the IT department by involving more of the business in AI efforts. Look for new vendors, partnerships, data sources, tools, and techniques to advance your efforts.

Leverage a diverse team. Include both technical and business experts in selecting AI technologies and suppliers. Having a broad perspective from developers, integrators, end users, and business owners can help ensure organizational alignment and a focus on business outcomes. Along with any vendor support consider using working groups, dedicated leaders, or communities of practice.

Actively address risks. Developing a set of principles and processes to actively manage the range of AI risks can help build trust within your business and with customers and partners.

Challenge vendors. While it is important to build trust and transparency with providers of your AI-powered systems, it can be equally essential to ensure that what they provide is aligned with your organizations ethical principles.

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Artificial Intelligence Loses Some Of Its Edginess, But Is Poised To Take Off - Forbes

AI applications in healthcare can literally change patients lives, improving diagnostics and treatment and helping patients and healthcare providers make informed decisions quickly.

AI in the global healthcare market (the total value of products and services sold) was valued at $2.4 billion in 2019 and is projected to reach $31.02 billion in 2025.

Now in the COVID-19 pandemic, AI is being leveraged to identify virus-related misinformation on social media and remove it. AI is also helping scientists expedite vaccine development, track the virusand understand individual and population risk, among other applications.

Companies such as Microsoft, which recently stated it will dedicate $20 million to advance the use of artificial intelligence in COVID-19 research, recognize the need for and extraordinary potential of AI in healthcare.

The ultimate goal of AI in healthcare is to improve patient outcomes by revolutionizing treatment techniques. By analyzing complex medical data and drawing conclusions without direct human input, AI technology can help researchers make new discoveries.

Various subtypes of AI are used in healthcare. Natural language processing algorithms give machines the ability to understand and interpret human language. Machine learning algorithms teach computers to find patterns and make predictions based on massive amounts of complex data.

AI is already playing a huge role in healthcare, and its potential future applications are game-changing. Weve outlined four distinct ways that AI is transforming the healthcare industry.

This transformative technology has the ability to improve diagnostics, advance treatment options, boost patient adherence and engagement, and support administrative and operational efficiency.

AI can help healthcare professionals diagnose patients by analyzing symptoms, suggesting personalized treatments and predicting risk. It can also detect abnormal results.

Analyzing symptoms, suggesting personalized treatments and predicting risk

Many healthcare providers and organizations are already using intelligent symptom checkers. This machine learning technology asks patients a series of questions about their symptoms and, based on their answers, informs them of appropriate next steps for seeking care.

Buoy Health offers a web-based, AI-powered health assistant that healthcare organizations are using to triage patients who have symptoms of COVID-19. It offers personalized information and recommendations based on the latest guidance from the Centers for Disease Control and Prevention.

Additionally, AI can take precision medicine healthcare tailored to the individual to the next level by synthesizing information and drawing conclusions, allowing for more informed and personalized treatment. Deep learning models have the ability to analyze massive amounts of data, including information about a patients genetic content, other molecular/cellular analysis and lifestyle factors and find relevant research that can help doctors select treatments.

AI can also be used to develop algorithms that make individual and population health risk predictions in order to help improve outcomes. At the University of Pennsylvania, doctors used a machine learning algorithm that can monitor hundreds of key variables in real time to anticipate sepsis or septic shock in patients 12 hours before onset.

Detecting disease

Imaging tools can advance the diagnostic process for clinicians. The San Francisco-based company Enlitic develops deep learning medical tools to improve radiology diagnoses by analyzing medical data. These tools allow clinicians to better understand and define the aggressiveness of cancers. In some cases, these tools can replace the need for tissue samples with virtual biopsies, which would aid clinicians in identifying the phenotypes and genetic properties of tumors.

These imaging tools have also been shown to make more accurate conclusions than clinicians. A 2017 study published in JAMA found that of 32 deep learning algorithms, seven were able to diagnose lymph node metastases in women with breast cancer more accurately than a panel of 11 pathologists.

Smartphones and other portable devices may also become powerful diagnostic tools that could benefit the areas of dermatology and ophthalmology. The use of AI in dermatology focuses on analyzing and classifying images and the ability to differentiate between benign and malignant skin lesions.

Using smartphones to collect and share images could widen the capabilities of telehealth. In ophthalmology, the medical device company Remidio has been able to detect diabetic retinopathy using a smartphone-based fundus camera, a low-power microscope with an attached camera.

AI is becoming a valuable tool for treating patients. Brain-computer interfaces could help restore the ability to speak and move in patients who have lost these abilities. This technology could also improve the quality of life for patients with ALS, strokes, or spinal cord injuries.

There is potential for machine learning algorithms to advance the use of immunotherapy, to which currently only 20% of patients respond. New technology may be able to determine new options for targeting therapies to an individuals unique genetic makeup. Companies like BioXcel Therapeutics are working to develop new therapies using AI and machine learning.

Additionally, clinical decision support systems can help assist healthcare professionals make better decisions by analyzing past, current and new patient data. IBM offers clinical support tools to help healthcare providers make more informed and evidence-based decisions.

Finally, AI has the potential to expedite drug development by reducing the time and cost for discovery. AI supports data-driven decision making, helping researchers understand what compounds should be further explored.

Wearables and personalized medical devices, such as smartwatches and activity trackers, can help patients and clinicians monitor health. They can also contribute to research on population health factors by collecting and analyzing data about individuals.

These devices can also be useful in helping patients adhere to treatment recommendations. Patient adherence to treatment plans can be a factor in determining outcome. When patients are noncompliant and fail to adjust their behaviors or take prescribed drugs as recommended, the care plan can fail.

The ability of AI to personalize treatment could help patients stay more involved and engaged in their care. AI tools can be used to send patients alerts or content intended to provoke action. Companies like Livongo are working to give users personalized health nudges through notifications that promote decisions supporting both mental and physical health.

AI can be used to create a patient self-service model an online portal accessible by portable devices that is more convenient and offers more choice. A self-service model helps providers reduce costs and helps consumers access the care they need in an efficient way.

AI can improve administrative and operational workflow in the healthcare system by automating some of the process. Recording notes and reviewing medical records in electronic health records takes up 34% to 55% of physicians time, making it one of the leading causes of lost productivity for physicians.

Clinical documentation tools that use natural language processing can help reduce the time providers spend on documentation time for clinicians and give them more time to focus on delivering top-quality care.

Health insurance companies can also benefit from AI technology. The current process of evaluating claims is quite time-consuming, since 80% of healthcare claims are flagged by insurers as incorrect or fraudulent. Natural language processing tools can help insurers detect issues in seconds, rather than days or months.

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How AI is revolutionizing healthcare - Nurse.com

This article was originally published here

Can Assoc Radiol J. 2020 Jul 31:846537120941434. doi: 10.1177/0846537120941434. Online ahead of print.

ABSTRACT

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

PMID:32735493 | DOI:10.1177/0846537120941434

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Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making - DocWire News

New carbon emission tracking technology will quantify emissions of greenhouse gas, holding the energy industry accountable for its CO2 output. Backed by Google, this cutting-edge initiative will be known as Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions).

Advanced AI and machine learning now make it possible to trace greenhouse gas (GHG) emissions from factories, power plants and more. By using image processing algorithms to detect carbon emissions from power plants, AI technology makes use of the growing global satellite network to develop a more comprehensive global database of power plant activity. Because most countries self-report emissions and manually compile results, scientists often rely on data that is several years out of date. Moreover, companies often underreport carbon emissions, rendering existing data inaccurate.

Climate TRACE addresses these issues by partnering with other leaders in sustainability practicesincluding former U.S. Vice President Al Gore, WattTime, CarbonPlan, Carbon Tracker, Earthrise Alliance, Hudson Carbon, OceanMind, Rocky Mountain Institute, Blue Sky Analytics and Hypervine. The Climate TRACE coalition aims to help countries in meeting Paris Agreement targets and place the world on a path to sustainability.

The carbon tracking efforts of Climate TRACE will result in a conglomeration of data to be made available to the public, which may assist plaintiffs in climate liability cases and lead to enhanced enforcement of environmental laws. The slow pace of international climate negotiations has led to an increase in lawsuits demanding action on global warming. As of this year, 1,600 climate-related lawsuits have been filed worldwide, including 1,200 lawsuits in the United States alone. Currently, climate liability cases rely predominantly on a database run by the Carbon Disclosure Project and the Climate Accountability Institute. This database, initially released in 2013 as the Carbon Majors Report, attempts to link carbon pollution to emitters. The 2013 report pinpointed 100 producers responsible for 71% of global industrial GHG emissions. Its 2017 report, for instance, indicated that 25 corporate and state producing entities account for 51% of global industrial GHG emissions. While the Carbon Majors Report has assisted in determining the largest carbon emitters on a global scale, Climate TRACE will provide more frequent and accurate monitoring of pollutants.

Data from Climate TRACE will also help hold countries accountable to the Paris Climate Agreement, expanding upon European efforts to monitor global warming. Early last year, a space budget increase put Europe in the lead to monitor carbon from space using satellite technology. In December 2019, member governments awarded the European Space Agency $12.5 billion. This substantial increase allowed the ESA to devote $1.8 billion to Copernicus, a satellite technology program which continuously tracks Earths atmosphere. The program allowed Europe to analyze human carbon emissions regularly. With Copernicus, the ESA became the only space agency to monitor pledges made under the Paris Climate Agreement. The Climate TRACE coalitionwith members spanning across three continentswill make carbon monitoring a global effort.

Climate TRACE has created a working prototype that is currently in its developmental stages. The coalition intends to release its first version of the AI project by the summer of 2021.

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Artificial Intelligence and Satellite Technology to Enhance Carbon Tracking Measures - JD Supra