Machine learning (ML) has exciting potential for a constellation of uses in clinical trials. But hype surrounding the term may build expectations that ML is not equipped to deliver. Ultimately, ML is a tool, and like any tool, its value will depend on how well users understand and manage its strengths and weaknesses. A hammer is an effective tool for pounding nails into boards, after all, but it is not the best option if you need to wash a window.

ML has some obvious benefits as a way to quickly evaluate large, complex datasets and give users a quick initial read. In some cases, ML models can even identify subtleties that humans might struggle to notice, and a stable ML model will consistently and reproducibly generate similar results, which can be both a strength and a weakness.

ML can also be remarkably accurate, assuming the data used to train the ML model was accurate and meaningful. Image recognition ML models are being widely used in radiology with excellent results, sometimes catching things missed by even the most highly trained human eye.

This doesnt mean ML is ready to replace clinicians judgment or take their jobs, but results so far offer compelling evidence that ML may have value as a tool to augment their clinical judgment.

A tool in the toolbox

That human factor will remain important, because even as they gain sophistication, ML models will lack the insight clinicians build up over years of experience. As a result, subtle differences in one variable may cause the model to miss something important (false negatives), or overstate something that is not important (false positives).

There is no way to program for every possible influence on the available data, and there will inevitably be a factor missing from the dataset. As a result, outside influences such as a person moving during ECG collection, suboptimal electrode connection, or ambient electrical interference may introduce variability that ML is not equipped to address. In addition, ML wont recognize if there is an error such as an end user entering an incorrect patient identifier, but because ECG readings are unique like fingerprints a skilled clinician might realize that the tracing they are looking at does not match what they have previously seen from the same patient, prompting questions about who the tracing actually belongs to.

In other words, machines are not always wrong, but they are also not always right. The best results come when clinicians use ML to complement, not supplant, their own efforts.

Maximizing ML

Clinicians who understand how to effectively implement ML in clinical trials can benefit from what it does well. For example:

The value of ML will continue to grow as algorithms improve and computing power increases, but there is little reason to believe it will ever replace human clinical oversight. Ultimately, ML provides objectivity and reproducibility in clinical trials, while humans provide subjectivity and can contribute knowledge about factors the program does not take into account. Both are needed. And while MLs ability to flag data inconsistencies may reduce some workload, those predictions still must be verified.

There is no doubt that ML has incredible potential for clinical trials. Its power to quickly manage and analyze large quantities of complex data will save study sponsors money and improve results. However, it is unlikely to completely replace human clinicians for evaluating clinical trial data because there are too many variables and potential unknowns. Instead, savvy clinicians will continue to contribute their expertise and experience to further develop ML platforms to reduce repetitive and tedious tasks with a high degree of reliability and a low degree of variability, which will allow users to focus on more complex tasks.

Photo: Gerd Altmann, Pixabay

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Machine Learning Has Value, but It's Still Just a Tool - MedCity News

Artificial intelligence (AI) is set to be the most disruptive emerging technology in drug development in 2023, unlocking advanced analytics, enabling automation, and increasing speed across the clinical trial value chain.

Todays clinical trials landscape is being shaped by macro trends that include the Covid-19 pandemic, geopolitical uncertainty, and climate pressures. Meanwhile, advancements in adaptive design, personalisation and novel treatments mean that clinical trials are more complex than ever. Sponsors seek greater agility and faster time to commercialisation while maintaining quality and safety in an evolving global market. Across every stage of clinical research, AI offers optimisation opportunities.

A new whitepaper from digital technology solutions provider Taimei examines the transformative impact of AI on the clinical trials of today and explores how it will shape the future.

The big delay areas are always patient recruitment, site start-up, querying, data review, and data cleaning, explains Scott Clark, chief commercial officer at Taimei.

Patient recruitment is typically the most time-consuming stage of a clinical trial. Sponsors must find and identify a set of subjects, gather information, and use inclusion/exclusion criteria to filter and select participants. And high-quality patient recruitment is vital to a trials success.

Once patients are recruited, they must be managed effectively. Patient retention has a direct impact on the quality of the trials results, so their management is crucial. In todays clinical trials, these patients can be distributed over more than a hundred sites and across multiple geographies, presenting huge data management challenges for sponsors.

AI can be leveraged across patient recruitment and management to boost efficiency, quality, and retention. Algorithms can gather subject information and screen and filter potential participants. They can analyse data sources such as medical records and even social media content to detect subgroups and geographies that may be relevant to the trial. AI can also alert medical staff and patients to clinical trial opportunities.

The result? Faster, more efficient patient recruitment, with the ability to reach more diverse populations and more relevant participants, as well as increase quality and retention. [Using AI], you can develop the correct cohort, explains Clark. Its about accuracy, efficiency, and safety.

Study build can be a laborious and repetitive process. Typically, data managers must read the study protocol and generate as many as 50-60 case report forms (CRFs). Each trial has different CRF requirements. CRF design and database building can take weeks and has a direct impact on the quality and accuracy of the clinical trial.

Enter AI. Automated text reading can parse, categorise, and stratify corpora of words to automatically generate eCRFs and the data capture matrix. In study building, AI is able to read the protocols and pull the best CRF forms for the best outcomes, adds Clark.

It can then use the data points from the CRFs to build the study base, creating the whole database in a matter of minutes rather than weeks. The database is structured for export to the biostatisticians programming. AI can then facilitate the analysis of data and develop all of the required tables, listings and figures (TLFs). It can even come to a conclusion on the outcomes, pending review.

Optical character recognition (OCR) can address structured and unstructured native documents. Using built-in edit checks, AI can reduce the timeframe for study build from ten weeks to just one, freeing up data managers time. We are able to do up to 168% more edit checks than are done currently in the human manual process, says Clark. AI can also automate remote monitoring to identify outliers and suggest the best route of action, to be taken with approval from the project manager.

AI data management is flexible, agile, and robust. Using electronic data capture (EDC) removes the need to manage paper-based documentation. This is essential for modern clinical trials, which can present huge amounts of unstructured data thanks to the rise of advances such as decentralisation, wearables, telemedicine, and self-reporting.

Once the trial is launched, you can use AI to do automatic querying and medical coding, says Clark. When theres a piece of data that doesnt make sense or is not coded, AI can flag it and provide suggestions automatically. The data manager just reviews what its corrected, adds Clark. Thats a big time-saver. By leveraging AI throughout data input, sponsors also cut out the lengthy process of data cleaning at the end of a trial.

Implementing AI means establishing the proof of concept, building a customised knowledge base, and training the model to solve the problem on a large scale. Algorithms must be trained on large amounts of data to remove bias and ensure accuracy. Today, APIs enable best-in-class advances to be integrated into clinical trial applications.

By taking repetitive tasks away from human personnel, AI accelerates the time to market for life-saving drugs and frees up man-hours for more specialist tasks. By analysing past and present trial data, AI can be used to inform future research, with machine learning able to suggest better study design. In the long term, AI has the potential to shift the focus away from trial implementation and towards drug discovery, enabling improved treatments for patients who need them.

To find out more, download the whitepaper below.

Originally posted here:

How AI, automation, and machine learning are upgrading clinical trials - Clinical Trials Arena

The most crucial aspect of every business is its cybersecurity. It aids in ensuring the security and safety of their data. Artificial intelligence and machine learning are in high demand, changing the cybersecurity industry as a whole. Cybersecurity may benefit greatly from machine learning, which can be used to better available antivirus software, identify cyber dangers, and battle online crime. With the increasing sophistication of cyber threats, companies are constantly looking for innovative ways to protect their systems and data. Machine learning is one emerging technology that is making waves in cybersecurity. Cybersecurity professionals can now detect and mitigate cyber threats more effectively by leveraging artificial intelligence and machine learning algorithms. This article will delve into key areas where machine learning is transforming the security landscape.

One of the biggest challenges in cybersecurity is accurately identifying legitimate connection requests and suspicious activities within a companys systems. With thousands of requests pouring in constantly, human analysis can fall short. This is where machine learning can play a crucial role. AI-powered cyber threat identification systems can monitor incoming and outgoing calls and requests to the system to detect suspicious activity. For instance, there are many companies that offer cybersecurity software that utilizes AI to analyze and flag potentially harmful activities, helping security professionals stay ahead of cyber threats.

Traditional antivirus software relies on known virus and malware signatures to detect threats, requiring frequent updates to keep up with new strains. However, machine learning can revolutionize this approach. ML-integrated antivirus software can identify viruses and malware based on their abnormal behavior rather than relying solely on signatures. This enables the software to detect not only known threats but also newly created ones. For example, companies like Cylance have developed smart antivirus software that uses ML to learn how to detect viruses and malware from scratch, reducing the dependence on signature-based detection.

Cyber threats can often infiltrate a companys network by stealing user credentials and logging in with legitimate credentials. It can be challenging to detect with traditional methods. However, machine learning algorithms can analyze user behavior patterns to identify anomalies. By training the algorithm to recognize each users standard login and logout patterns, any deviation from these patterns can trigger an alert for further investigation. For instance, Darktrace offers cybersecurity software that uses ML to analyze network traffic information and identify abnormal user behavior patterns.

Machine learning offers several advantages in the field of cyber security. First and foremost, it enhances accuracy by analyzing vast amounts of data in real time, helping to identify potential threats promptly. ML-powered systems can also adapt and evolve as new threats emerge, making them more resilient against rapidly growing cyber-attacks. Moreover, ML can provide valuable insights and recommendations to cybersecurity professionals, helping them make informed decisions and take proactive measures to prevent cyber threats.

As cyber threats continue to evolve, companies must embrace innovative technologies like machine learning to strengthen their cybersecurity defenses. Machine learning is transforming the cybersecurity landscape with its ability to analyze large volumes of data, adapt to new threats, and detect anomalies in user behavior. By leveraging the power of AI and ML, companies can stay ahead of cyber threats and safeguard their systems and data. Embrace the future of cybersecurity with machine learning and ensure the protection of your companys digital assets.

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Application od Machine Learning in Cybersecurity - Read IT Quik

Q: What are the challenges to undertaking data analytical research? And where have these modes of analysis been successful?

The challenges are many, especially when you want to make a meaningful impact in one of the most complex sectorsthe health care sector. The health care sector involves a variety of stakeholders, especially in the United States, where health care is extremely decentralized yet highly regulated, for example in the areas of data collections and data use. Analytics-based solutions that can help one part of this sector might harm other parts, making finding globally optimal solutions in this sector extremely difficult. Therefore, finding data-driven approaches that can have public impact is not a walk in the park.

Then there are various challenges in implementation. In my lab, we can design advanced machine learning and AI algorithms that have outstanding performance. But if they are not implemented in practice, or if the recommendations they provide are not followed, they wont have any tangible impact.

In some of our recent experiments, for example, we found that the algorithms we had designed outperformed expert physicians in one of the leading U.S. hospitals. Interestingly, when we provided physicians with our algorithmic-based recommendations, they did not put much weight on the advice they got from the algorithms, and ignored it when treating patients, although they knew the algorithm most likely outperforms them.

We then studied ways of removing this obstacle. We found that combining human expertise with the recommendations provided by algorithms not only made it more likely for the physicians to put more weight on the algorithms advice, but also synthesized recommendations that are superior to both the best algorithms and the human experts.

We have also observed similar challenges at the policy level. For example, we have developed advanced algorithms trained on large-scale data that could help the Centers for Disease Control and Prevention improve its opioid-related policies. The opioid epidemic caused more than 556,000 deaths in the United States between 2000 and 2020, and yet the authorities still do not have a complete understanding of what can be done to effectively control this deadly epidemic. Our algorithms have produced recommendations we believe are superior to the CDCs. But, again, a significant challenge is to make sure CDC and other authorities listen to these superior recommendations.

I do not want to imply that policymakers or other authorities are always against these algorithm-driven solutionssome are more eager than othersbut I believe the helpfulness of algorithms is consistently underrated and often ignored in the practice.

Q: How do you think about the role of oversight and regulation in this field of new technologies and data analytical models?

Imposing appropriate regulations is important. There is, however, a fine line: while new tools and advancements should be guarded against misuses, the regulations should not block these tools from reaching their full potential.

As an example, in a paper that we published in the National Academy of Medicine in 2021, we discussed that the use of mobile health (mHealth) interventions (mainly enabled through advanced algorithms and smart devices) have been rapidly increasing worldwide as health care providers, industry, and governments seek more efficient ways of delivering health care. Despite the technological advances, increasingly widespread adoption, and endorsements from leading voices from the medical, government, financial, and technology sectors, these technologies have not reached their full potential.

Part of the reason is that there are scientific challenges that need to be addressed. For example, as we discuss in our paper, mHealth technologies need to make use of more advanced algorithms and statistical experimental designs in deciding how best to adapt the content and delivery timing of a treatment to the users current context.

However, various regulatory challenges remainsuch as how best to protect user data. The Food and Drug Administration in a 2019 statement encouraged the development of mobile medical apps (MMAs) that improve health care but also emphasized its public health responsibility to oversee the safety and effectiveness of medical devicesincluding mobile medical apps. Balancing between encouraging new developments and ensuring that such developments abide by the well-known principle of do no harm is not an easy regulatory task.

At the end, what is needed are two-fold: (a) advancements in the underlying science, and (b) appropriately balanced regulations. If these are met, the possibilities for using advanced analytics science methods in solving our lingering societal problems are endless.

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Big data and machine learning can usher in a new era of policymaking - Harvard Kennedy School

Chicago - David Higginson has some advice for hospitals and health systems looking to use machine learning.

"Get started," he says.

Higginson, the chief innovation officer of Phoenix Children's Hospital, offered a presentation on machine learning at the HIMSS Global Health Conference & Exhibition. He described how machine learning models helped identify children with malnutrition and people who would be willing to donate to the hospital's foundation.

After the session, he spoke with Chief Healthcare Executive and offered some guidance for health systems looking to do more with machine learning.

"I would say get started by thinking about how you going to use it first," Higginson says. "Don't get tricked into actually building the model."

"Think about the problem, frame it up as a prediction problem," he says, while adding that not all problems can be framed that way.

"But if you find one that is a really nice prediction problem, ask the operators, the people that will use it everyday: 'Tell me how you'd use this,'" Higginson says. "And work with them on their workflow and how it's going to change the way they do their job.

"And when they can see it and say, 'OK, I'm excited about that, I can see how it's going to make a difference,' then go and build it," he says. "You'll have more motivation to do it, you'll understand what the goal is. But when you finally do get it, you'll know it's going to be used."

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David Higginson of Phoenix Children's Hospital on using machine ... - Chief Healthcare Executive

Data augmentation is the process of applying various transformations to the training data. It helps increase the diversity of the dataset and prevent overfitting. Overfitting mostly occurs when you have limited data to train your model.

Here, you will learn how to use TensorFlow's data augmentation module to diversify your dataset. This will prevent overfitting by generating new data points that are slightly different from the original data.

You will use the cats and dogs dataset from Kaggle. This dataset contains approximately 3,000 images of cats and dogs. These images are split into training, testing, and validation sets.

The label 1.0 represents a dog while the label 0.0 represents a cat.

The full source code implementing data augmentation techniques and the one that does not are available in a GitHub repository.

To follow through, you should have a basic understanding of Python. You should also have basic knowledge of machine learning. If you require a refresher, you may want to consider following some tutorials on machine learning.

Open Google Colab. Change the runtime type to GPU. Then, execute the following magic command on the first code cell to install TensorFlow into your environment.

Import TensorFlow and its relevant modules and classes.

The tensorflow.keras.preprocessing.image will enable you to perform data augmentation on your dataset.

Create an instance of the ImageDataGenerator class for the train data. You will use this object for preprocessing the training data. It will generate batches of augmented image data in real time during model training.

In the task of classifying whether an image is a cat or a dog, you can use the flipping, random width, random height, random brightness, and zooming data augmentation techniques. These techniques will generate new data which contains variations of the original data representing real-world scenarios.

Create another instance of the ImageDataGenerator class for the test data. You will need the rescale parameter. It will normalize the pixel values of the test images to match the format used during training.

Create a final instance of the ImageDataGenerator class for the validation data. Rescale the validation data the same way as the test data.

You do not need to apply the other augmentation techniques to the test and validation data. This is because the model uses the test and validation data for evaluation purposes only. They should reflect the original data distribution.

Create a DirectoryIterator object from the training directory. It will generate batches of augmented images. Then specify the directory that stores the training data. Resize the images to a fixed size of 64x64 pixels. Specify the number of images that each batch will use. Lastly, specify the type of label to be binary (i.e., cat or dog).

Create another DirectoryIterator object from the testing directory. Set the parameters to the same values as those of the training data.

Create a final DirectoryIterator object from the validation directory. The parameters remain the same as those of the training and testing data.

The directory iterators do not augment the validation and test datasets.

Define the architecture of your neural network. Use a Convolutional Neural Network (CNN). CNNs are designed to recognize patterns and features in images.

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(1, activation='sigmoid'))

Compile the model by using the binary cross-entropy loss function. Binary classification problems commonly use It. For the optimizer, use the Adam optimizer. It is an adaptive learning rate optimization algorithm. Finally, evaluate the model in terms of accuracy.

Print a summary of the model's architecture to the console.

The following screenshot shows the visualization of the model architecture.

This gives you an overview of how your model design looks.

Train the model using the fit() method. Set the number of steps per epoch to be the number of training samples divided by the batch_size. Also, set the validation data and the number of validation steps.

The ImageDataGenerator class applies data augmentation to the training data in real time. This makes the training process of the model slower.

Evaluate the performance of your model on the test data using the evaluate() method. Also, print the test loss and accuracy to the console.

The following screenshot shows the model's performance.

The model performs reasonably well on never seen data.

When you run code that does not implement the data augmentation techniques, the model training accuracy is 1. Which means it overfits. It also performs poorly on data it has never seen before. This is because it learns the peculiarities of the dataset.

TensorFlow is a diverse and powerful library. It is capable of training complex deep learning models and can run on a range of devices from smartphones to clusters of servers. It has helped power edge computing devices that utilize machine learning.

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How to Improve Your Machine Learning Model With TensorFlow's ... - MUO - MakeUseOf

The M.Sc. program in Machine Learning & Data Science at the Efi Arazi School of Computer Science aims to provide a deep theoretical understanding of machine learning and data-driven methods as well as a strong proficiency in using these methods. As part of this unique program, students with solid exact science backgrounds, but not necessarily computer science backgrounds, are trained to become data scientists. Headed by Prof. Zohar Yakhini and PhD Candidate Ben Galili, this program provides students with the opportunity to become skilled and knowledgeable data scientists by preparing them with fundamental theoretical and mathematical understandings, as well as endowing them with scientific and technical skills necessary to be creative and effective in these fields. The program offers courses in statistics and data analysis, different levels of machine -learning courses as well as unique electives such as a course in recommendation systems and on DNA and sequencing technologies.

M.Sc. student Guy Assa, preparing DNA for sequencing on a nanopore device, in Prof. Noam Shomrons DNA sequencing class, part of the elective curriculum (Credit: private photo)

In recent years, data science methodologies have become a foundational language and a main development tool for science and industry. Machine learning and data-driven methods have developed considerably and now penetrate almost all areas of modern life. The vision of a data-driven world presents many exciting challenges to data experts in diverse fields of application, such as medical science, life science, social science, environmental science, finance, economics, business.

Graduates of the program are successful in becoming data scientists in Israeli hi-tech companies. Lior Zeida Cohen, a graduate of the program says After earning a BA degree in Aerospace Engineering from the Technion and working as an engineer and later leading a control systems development team, I sought out a graduate degree program that would allow me to delve deeply into the fields of Data Science and Machine Learning while also allowing me to continue working full-time. I chose to pursue the ML & Data Science Program, at Reichman University. The program provided in-depth study in both the theoretical and practical aspects of ML and Data Science, including exposure to new research and developments in the field. It also emphasized the importance of learning the fundamental concepts necessary for working in these domains. In the course of completing the program, I began work at Elbit Systems as an algorithms developer in a leading R&D group focusing on AI and Computer Vision. The program has greatly contributed to my success in this position".

As a part of the curriculum, the students execute collaborative research projects with both external and internal collaborators, in Israel and around the world; One active collaboration is with the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany. In this collaboration, the students, led by Prof. Zohar Yakhini and Dr. Shay Ben-Elazar, a Principal Data Science and Engineering Manager at Microsoft Israel, as well as Dr. Johannes Bhl from TROPOS, are using data science and machine learning tools in order to infer properties of stratospheric layers by using data from sensory devices. The models developed in the project provide inference from simple devices that achieves an accuracy which is close to that which is obtained through much more expensive measurements. This improvement is enabled through the use of neural network models (deep learning).

Results from the TROPOS project: a significant improvement in the inference accuracy. Left panel: actual atmospheric status as obtained from the more expensive measurements (Lidar + Radar) Middle panel: predicted status as inferred from Lidar measurements using physical models. Right panel: status determined by the deep learning model developed in the project.

Additional collaborations include a number of projects with Israeli hospitals such as Sheba Tel Hashomer, Beilinson Hospital, and Kaplan Medical Center, as well as with the Israel Nature and Parks Authority and with several hi-tech companies.

PhD candidate Ben Galili, Academic Director of Machine Learning and Data Science Program (Credit: private photo)

Several research and thesis projects are led by students in the program addressing data analysis questions related to spatial biology the study of molecular biology processes in their bigger location context. One project, led by student Guy Attia and supervised by Dr. Leon Anavy addressed imputation methods for spatial transcriptomics data. A second one, led by student Efi Herbst, aims to expand the inference scope of data from spatial transcriptomics, into molecular properties that are not directly measured by the technology device.

According to Maya Kerem, a recent graduate, the MA program taught me a number of skills that would enable me to easily integrate into a new company based on the knowledge I gained. I believe that this program is particularly unique because it always makes sure that the learnings are applied to industry-related problems at the end of each module. This is a hands-on program at Reichman University, which is what drew me to enroll in this MA program.

For more info

This article was written in cooperation with Reichman University

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An M.Sc. computer science program in RUNI, focusing on machine learning - The Jerusalem Post

The world of artificial intelligence (AI) is growing exponentially, with machine learning playing an instrumental role in bringing intelligent systems to life. As a result, machine learning engineers are in high demand in the tech industry. If youre contemplating a career in this captivating domain, this article will give you a comprehensive understanding of a machine learning engineers role, their primary responsibilities, average salary, and the steps to becoming one.

A machine learning engineer is a specialized type of software engineer who focuses on the design, implementation, and optimization of machine learning models and algorithms. They serve as a link between data science and software engineering, working in close collaboration with data scientists to transform prototypes and ideas into scalable, production-ready systems. Machine learning engineers play a vital role in converting raw data into actionable insights and ensuring that AI systems are efficient, accurate, and dependable.

Machine learning engineers have a wide range of responsibilities, including:

The average salary of a machine learning engineer can vary based on factors such as location, experience, and company size. According to Glassdoor, as of 2023, the average base salary for a machine learning engineer in the United States is approximately $118,000 per year. However, experienced professionals and those working in high-demand areas can earn significantly higher salaries.

To become a machine learning engineer, follow these steps:

the key traits that contribute to the success of a machine learning engineer.

Machine learning engineers often face complex challenges that require innovative solutions. A successful engineer must possess excellent analytical and problem-solving skills to identify patterns in data, understand the underlying structure of problems, and develop effective strategies to address them. This involves breaking down complex problems into smaller, more manageable components, and using a logical and methodical approach to solve them.

A solid foundation in mathematics and statistics is crucial for machine learning engineers, as these disciplines underpin many machine learning algorithms and techniques. Engineers should have a strong grasp of linear algebra, calculus, probability, and optimization methods to understand and apply various machine learning models effectively.

Machine learning engineers must be proficient in programming languages such as Python, R, or Java, as these are often used to develop machine learning models. Additionally, they should be well-versed in software engineering principles, including version control, testing, and code optimization. This knowledge enables them to create efficient, scalable, and maintainable code that can be seamlessly integrated into production environments.

Successful machine learning engineers must be adept at using popular machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn. These tools streamline the development and implementation of machine learning models, allowing engineers to focus on refining their algorithms and optimizing their models for better performance.

The field of machine learning is constantly evolving, with new techniques, tools, and best practices emerging regularly. A successful machine learning engineer must possess an innate curiosity and a strong desire for continuous learning. This includes staying up-to-date with the latest research, attending conferences and workshops, and engaging in online communities where they can learn from and collaborate with other professionals.

Machine learning projects often require engineers to adapt to new technologies, tools, and methodologies. A successful engineer must be adaptable and flexible, willing to learn new skills and pivot their approach when necessary. This agility enables them to stay ahead of the curve and remain relevant in the fast-paced world of AI.

Machine learning engineers frequently work in multidisciplinary teams, collaborating with data scientists, software engineers, and business stakeholders. Strong communication and collaboration skills are essential for effectively conveying complex ideas and concepts to team members with varying levels of technical expertise. This ensures that the entire team works cohesively towards a common goal, maximizing the success of machine learning projects.

Developing effective machine learning models requires a high degree of precision and attention to detail. A successful engineer must be thorough in their work, ensuring that their models are accurate, efficient, and reliable. This meticulous approach helps to minimize errors and ensures that the final product meets or exceeds expectations.

Becoming a machine learning engineer requires a strong foundation in mathematics, computer science, and programming, as well as a deep understanding of various machine learning algorithms and techniques. By following the roadmap outlined in this article and staying current with industry trends, you can embark on a rewarding and exciting career as a machine learning engineer. Develop an understanding of data preprocessing, feature engineering, and data visualization techniques.

Learn about different machine learning algorithms, including supervised, unsupervised, and reinforcement learning approaches. Gain practical experience through internships, personal projects, or freelance work. Build a portfolio of machine learning projects to showcase your skills and knowledge to potential employers.

Originally posted here:

What is a Machine Learning Engineer? Salary & Responsibilities - Unite.AI

TOKYO, April 24, 2023 (GLOBE NEWSWIRE) -- The Global Machine Learning as a Service Market Size accounted for USD 7.1 Billion in 2022 and is projected to achieve a market size of USD 173.5 Billion by 2032 growing at a CAGR of 37.9% from 2023 to 2032.

Machine Learning as a Service Market Research Report Highlights and Statistics:

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Machine Learning as Service Market Report Coverage:

Machine Learning as a Service Market Overview:

The increasing adoption of cloud-based technologies and the need for managing the enormous amount of data generated has led to the rise in demand for MLaaS solutions. MLaaS provides pre-built algorithms, models, and tools, making it easier and faster to develop and deploy machine learning applications. This service is being used in various industries such as healthcare, retail, BFSI, manufacturing, and others.

The healthcare industry is using MLaaS for patient monitoring and disease prediction. In retail, MLaaS is being used for personalized recommendations and fraud detection. MLaaS is also being utilized for financial fraud detection, sentiment analysis, recommendation systems, predictive maintenance, and much more.

The Natural Language Processing (NLP) segment is expected to grow rapidly during the forecast period. NLP is being used by organizations to analyze customer feedback, improve customer experience, and automate customer service. MLaaS vendors such as Amazon Web Services, IBM Corporation, Google LLC, Microsoft Corporation, and Oracle Corporation offer various pricing models and features, making the Machine Learning as a Service market competitive.

Trends in the Machine Learning as a Service Market:

Machine Learning as a Service Market Dynamics

Growth Hampering Factors in the Market for Machine Learning as a Service:

Check the detailed table of contents of the report @

https://www.acumenresearchandconsulting.com/table-of-content/machine-learning-as-a-service-mlaas-market

Market Segmentation:

By Type of component

By Application

By Size of Organization

End User

Machine Learning as a Service Market Overview by Region:

North America's Machine Learning as a Service market share is the highest globally, due to the high adoption of cloud computing and the presence of several major players in the region. The United States is the largest market for MLaaS in North America, driven by the increasing demand for predictive analytics, the growing use of deep learning, and the rising adoption of artificial intelligence (AI) across various industries. For instance, companies in the healthcare sector are using MLaaS for predicting patient outcomes, and retailers are using it to analyze customer behavior and preferences to deliver personalized experiences.

The Asia-Pacific region's Machine Learning as a Service Market share is also huge and is growing at the fastest rate, due to the increasing adoption of cloud computing, the growth of IoT devices, and the rise of e-commerce in the region. China is the largest market for MLaaS in the Asia Pacific region, with several major companies investing in AI and machine learning technologies. For example, Alibaba, the largest e-commerce company in China, is using MLaaS for predictive analytics and recommendation engines. Japan is another significant market for MLaaS in the region, with companies using it for predictive maintenance and fraud detection.

Europe is another key market for Machine Learning as a Service, with countries such as the United Kingdom, Germany, and France driving growth in the region. The adoption of MLaaS in Europe is being driven by the growth of e-commerce and the increasing demand for personalized experiences. For example, companies in the retail sector are using MLaaS to analyze customer data and make personalized product recommendations. The healthcare sector is also a significant user of MLaaS in Europe, with providers using it for predictive analytics and diagnosis.

The MEA and South American regions have a growing Machine Learning as a Service market share, however it is expected to grow at a steady pace.

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Machine Learning as a Service Market Key Players:

Some of the major players in the Machine Learning as a Service market include Amazon Web Services, Google LLC, IBM Corporation, Microsoft Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Development LP, Fair Isaac Corporation (FICO), Fractal Analytics Inc., H2O.ai, DataRobot, Alteryx Inc., Big Panda Inc., RapidMiner Inc., SAS Institute Inc., Angoss Software Corporation, Domino Data Lab Inc., TIBCO Software Inc., Cloudera Inc., and Databricks Inc. These companies offer a wide range of MLaaS solutions, including predictive analytics, machine learning algorithms, natural language processing, deep learning, and computer vision.

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Machine Learning as a Service Market Size Growing at 37.9% CAGR Set to Reach USD 173.5 Billion By 2032 - Benzinga

A new machine-learning method could help us gauge the time of our internal body clock, helping us all make better health decisions, including when and how long to sleep.

The research, which has been conducted by the University of Surrey and the University of Groningen, used a machine learning programme to analyse metabolites in blood to predict the time of our internal circadian timing system.

To date the standard method to determine the timing of the circadian system is to measure the timing of our natural melatonin rhythm, specifically when we start producing melatonin, known as dim light melatonin onset (DLMO).

Professor Debra Skene, co-author of the study from the University of Surrey, said:

After taking two blood samples from our participants, our method was able to predict the DLMO of individuals with an accuracy comparable or better than previous, more intrusive estimation methods.

The research team collected a time-series of blood samples from 24 individuals 12 men and 12 women. All participants were healthy, did not smoke and had regular sleeping schedules seven days before they visited the University clinical research facility. The research team then measured over 130 metabolite rhythms using a targeted metabolomics approach. These metabolite data were then used in a machine learning programme to predict circadian timing.

Professor Skene continued:

"We are excited but cautious about our new approach to predicting DLMO as it is more convenient and requires less sampling than the tools currently available. While our approach needs to be validated in different populations, it could pave the way to optimise treatments for circadian rhythm sleep disorders and injury recovery.

Smart devices and wearables offer helpful guidance on sleep patterns but our research opens the way to truly personalised sleep and meal plans, aligned to our personal biology, with the potential to optimise health and reduce the risks of serious illness associated with poor sleep and mistimed eating."

Professor Roelof Hut, co-author of the study from University of Groningen, said:

Our results could help to develop an affordable way to estimate our own circadian rhythms that will optimize the timing of behaviors, diagnostic sampling, and treatment.

The study has been published by PNAS

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Notes to editors

Professor Debra Skene and Professor Roelof Hut are available for interview upon request however requests will be limited due to their work commitments.

For more information, please contact the University of Surrey's press office via mediarelations@surrey.ac.uk

Proceedings of the National Academy of Sciences

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Machine learning estimation of human body time using metabolomic profiling

24-Apr-2023

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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New machine-learning method predicts body clock timing to improve ... - EurekAlert