Machine learning (ML), a commonly used type of artificial intelligence (AI), is one of the fastest-growing fields in technology.

Especially as the workplace, products, and service expectations are changing through digital transformations, more companies are leaning into machine learning solutions to optimize, automate, and simplify their operations.

So what does ML technology look like today and where is it heading in the future? Read on to learn about some of the top trends in machine learning today.

More on the ML market: Machine Learning Market

Many businesses are investing significant time and resources into ML development because they recognize its potential for automation.

When an ML model is designed with business processes in mind, it can automate a variety of business functions across marketing, sales, HR, and even network security. MLOps and AutoML are two of the most popular applications of machine learning today, giving teams the ability to automate tasks and bring DevOps principles to machine learning use cases.

Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, believes that both MLOps and AutoML strategies eliminate several traditional business blockers.

Scaling AI for the enterprise requires a new set of tools and skills designed for modern infrastructure and collaboration, Maloney said. Teams using manual deployment and management find they are quickly strapped for resources and after getting a few models into production, cannot scale beyond that.

Machine learning operations (MLOps), is the set of practices and technology that enable organizations to scale and manage AI in production, essentially bringing the development practice of DevOps to machine learning. MLOps helps data science and IT teams collaborate and empowers IT teams to lead production machine learning projects, without having to rely on data science expertise.

AutoML solves a few of the biggest blockers to ML adoption, including faster time to ROI and more quickly and easily developing models. AutoML automates key parts of the data science workflow to increase productivity, without compromising model quality, interpretability, and performance.

With AutoML, you can automate algorithm selection, feature generation, hyper-parameter tuning, iterative modeling, and model assessment. By automating repetitive tasks in the workflow, data scientists can focus on the data and the business problems they are trying to solve and speed time from experiment to impact.

Automation through ML is desirable in theory, but in practice, its sometimes difficult for business leaders to envision how ML tools can optimize their business operations.

Amaresh Tripathy, SVP and global business leader at Genpact, a digital transformation and professional services firm, offered some common examples of how MLOps and MLOps-as-a-service help businesses in various industries.

One [MLOps] example is using AI models to efficiently direct sales teams to identify the next best customer, Tripathy said. Another is optimizing pricing and revenue management systems using dynamic demand forecasting.

AI and automation in the workforce: Artificial Intelligence and Automation

Machine learning is still considered a niche and complex technology to develop, but a growing segment of tech professionals are working to democratize the field, particularly by making ML solutions more widely accessible.

Jean-Francois Gagne, head of AI product and strategy at ServiceNow, a workflow management software company, believes that ML democratization involves creating easier access to develop and deploy ML models as well as giving more people access to useful ML training data.

Good training data is often scarce, Gagne said. Low-data learning techniques are helping in enterprise AI use cases, where customers want to adapt pre-trained out-the-box models to their unique business context. In most cases, their own data sets are not that big, but methods such as transfer learning, self-supervised learning, and few-shot learning help minimize the amount of labeled training data needed for an application.

ML democratization is also about creating tools that consider the backgrounds and use cases of a more diverse range of users.

Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that more users and developers are starting to recognize the benefit of a diverse team for developing ML solutions.

Ignoring the technical for a moment, we must focus on the human aspects of AI as well, Gilmore said. There seems to be a trend building around the democratization of the ML ecosystem, bringing more diverse stakeholders to the table no matter where in the value chain.

Bias is probably the single greatest obstacle to ML efficacy, and leading companies are learning to combat bias and build better applications by embracing diversity and inclusion (D&I).

ML needs additional variety in training data, for sure. Still, we should also consider the positive impact of D&I on the teams that design, build, label, and deliver the ML-driven applications this can genuinely differentiate ML products.

More on data democratization: Data Democratization Trends

ML developers are increasingly creating their models in containers.

When a machine learning product is developed and deployed within a containerized environment, users can ensure that its operational power is not negatively impacted by other programs running on the server. More importantly, ML becomes more scalable through containerization, as the packaged model makes it possible to migrate and adjust ML workloads over time.

Ali Siddiqui, chief product officer at BMC, a SaaS company with a variety of ITOps solutions, believes that containerized development of machine learning is the best way forward, particularly in the case of digital enterprises incorporating autonomous operations.

Its trending to use machine learning workloads in containers, Siddiqui said. Containers allow autonomous digital enterprises to have isolation, portability, unlimited scalability, dynamic behavior, and rapid change through advanced enterprise DevOps processes.

ML workloads are typically spiky and require high scalability and in some cases, real-time stream processing. For instance, when you take a look at ML projects, they typically have two phases: algorithm creation and algorithm execution. The first involves a lot of data and data processing. The second typically requires a lot of compute power in production. Both can benefit from container deployment to ensure scalability and availability.

More on containerization: Containers are Shaping IoT Development

In another trending effort toward ML democratization, a number of ML developers have perfected their models over time and found ways to create template-like versions, available to a wider pool of users via API and other integrations.

Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that prepackaged ML tools, particularly via APIs and digital storefronts, are some of the most common and useful applications of machine learning today:

API-fication of ML models is another key trend we are seeing, whether it is GPT3, CODEX, or even Hugging Face, where they train and deploy state-of-the-art NLP models and make them available as web APIs or Python packages for inferencing, DR said. [Theres also] AI stores with pre-trained models exposed via APIs, which provide a drag-and-drop option for AI development across enterprises.

Also read: Artificial Intelligence vs. Machine Learning

Machine learning models can only improve their functionality over time if they are consistently fed new data in intervals. Since so many ML models rely on timeline-based updates, a number of ML solutions are using a time series approach to improve the models understanding of the what, when, and why behind different data sets.

Read Maloney of H2O.ai explained why time series solutions are necessary for truly predictive ML:

On a long enough horizon, all problems eventually become time series problems, Maloney said. ML is a phenomenal method for predicting events in real-time, and as we observe these predictions over time, we need more and more time series solutions.

Every business needs to make predictions, whether forecasting sales, estimating product demand, or predicting future inventory levels. In all cases, data is necessary as well as specific techniques and tools to account for time.

Selecting the right machine learning support for your business: Top Machine Learning Companies

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Machine Learning Trends to Watch 2021 - Datamation

A study published in Social Psychological and Personality Science presents evidence that people make judgments about strangers personalities based on how closely their resting faces resemble emotional expressions. It was found that among seven classes of facial characteristics, resemblance to emotional expressions was the strongest predictor of impressions of both trustworthiness and dominance.

It has long been demonstrated that people form rapid impressions of others based on their physical appearances. Such quick judgments can have strong repercussions for example, when juries are forming impressions of the accused during criminal trials or when hiring managers are screening potential candidates.

One thing I find fascinating about first impressions is how quickly and intuitively they come to mind. For example, I might see a stranger on the train and immediately get the feeling that they cannot be trusted. I want to understand where these intuitions come from. What is it about a persons appearance that makes them appear untrustworthy, intelligent, or dominant to us? said study author Bastian Jaeger, an assistant professor at the Vrije Universiteit Amsterdam.

While many studies have identified specific facial characteristics that are associated with personality impressions, Jaeger and his colleague Alex L. Jones note that this type of research comes with its challenges. Since many facial features are correlated, it is tricky to identify the unique effects of a given characteristic. For example, if a face is manipulated to look more like it is smiling, these adjustments will also influence the babyfacedness of the face. For this reason, Jaeger and Jones set out to examine the relative predictive value of a given facial characteristic for personality impressions, by examining a wide range of facial features at once.

The researchers analyzed a dataset from the Chicago Face Database, which included 597 faces of individuals maintaining a neutral expression in front of a plain background. The dataset had previously been presented to a sample of 1,087 raters who each rated a subset of 10 faces on a wide range of characteristics. These characteristics included attractiveness, unusualness, babyfacedness, dominance, and trustworthiness of the face. The sample also rated the extent that faces resembled six emotional expressions happiness, sadness, anger, disgust, fear, and surprise.

In total, the database included information on 28 facial features which the researchers divided into seven categories: demographics, morphological features, facial width-to-height ratio (fWHR), perceived attractiveness, perceived unusualness, perceived babyfacedness, and emotion resemblance.

Using machine learning, Jaeger and Jones tested the predictive value of each of these classes of facial features for impressions of trustworthiness and dominance. It was found that resemblance to emotional expressions was the best predictor for perceptions of both trustworthiness and dominance. Emotion resemblance also explained the most variance in perceptions of trustworthiness and dominance out of all seven classes.

Next, using regression analysis, the researchers examined the relative predictive value of each of the 28 facial features. Here, they found that resemblance to a happy expression was the strongest predictor of trustworthiness. Attractiveness and being Asian were also substantial positive predictors, and resemblance to an angry expression was a fairly strong negative predictor. For perceptions of dominance, resemblance to an angry expression was the strongest positive predictor, and being female was the strongest negative predictor. Contrary to previous findings, fWHR was not a strong predictor of either trustworthiness or dominance perceptions.

The studys authors say this pattern of findings is in line with a phenomenon called emotion overgeneralization, which posits that people are especially sensitive to reading emotions in other peoples faces since emotions convey highly relevant social information. Because of this oversensitivity, people end up detecting emotions even in neutral faces that structurally resemble emotional expressions. This information is then used to infer personality characteristics from the face, such as trustworthiness.

We shouldnt be too confident in our first impressions, Jaeger told PsyPost. They might come to mind easily and effortlessly, but not because we are so good at judging others. Rather, it seems like our oversensitive emotion detection system makes us see things in others faces. Even when a person is not sending any emotional signals, we might detect a smile, just because the corners of their mouth are slightly tilted upwards. And because of our tendency to overgeneralize from emotional states to psychological traits, we not only think that they are happy right now, but that they are happy, outgoing, and trustworthy in general.

Notably, the results imply that there are additional features that relate to impression formation that the study did not test for. Emotion resemblances explained 53% and 42% of the variance in trustworthiness and dominance perceptions, Jaeger and Jones report. Even the optimized Elastic Net models explained around 68% of the variance, indicating there are other important factors contributing to personality impressions. Future studies should attempt to uncover more predictors and shed additional light on the relative importance of specific facial features.

Our findings are based on relatively large and demographically diverse samples of raters and targets, but they were all from the United States, Jaeger noted. Its important to test the generalizability of our results. We find that first impressions are largely based on how much a persons facial features resemble a smile or a frown, but is that also true for people in China, Chile, or Chad?

The study, Which Facial Features Are Central in Impression Formation?, was authored by Bastian Jaeger and Alex L. Jones.

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Machine learning study identifies facial features that are central to first impressions - PsyPost

Googles Pixel 6 and Pixel 6 Pro are officially here, and with them, the debut of Googles new Tensor chip. Google has finally revealed more information on what the new SoC can actually do, for the fastest Pixel phones ever.

The initial reveal of the Pixel 6 and the Tensor chip was largely centered on its AI-focused TPU (Tensor processing unit) and how the custom hardware would help Google differentiate itself from competitors.

Thats still the big focus of Googles announcement today: the company calls Tensor a milestone for machine learning that was co-designed alongside Google Research to allow it to easily translate AI and machine learning advances into actual consumer products. For example, Google says that the Tensor chip will have the most accurate Automatic Speech Recognition (ASR) that its offered, for both quick Google Assistant queries and longer audio tasks like live captions or the Recorder app.

Tensor also enables new Pixel 6 features like Motion Mode, more accurate face detection, and live translations that can convert text to a different language as quickly as you can type it. Google also says that the Tensor chip will handle dedicated machine learning tasks with far more power efficiency than previous Pixel phones.

But theres a lot more to a smartphone chip than its AI chops, and with the reveal of the Pixel 6, we finally have more details on the rest of the chip, including the CPU, GPU, modem, and the major components that make Tensor tick.

As rumored, the Tensor chip uses a unique combination of CPU cores. Theres the custom TPU (Tensor Processing Unit) for AI, two high-power Cortex-X1 cores, two midrange (rumored to be older Cortex-A76 cores), and then four low power efficiency cores (likely Arms usual Cortex-55 designs). Graphics are offered by a 20-core GPU, in addition to a context hub that powers ambient experiences like the always-on display, a private computer core, and a new Titan M2 chip for security. Theres also a dedicated image processing core to help with the Pixels hallmark photography.

Its not entirely clear why Google would choose to use the Cortex-A76 cores instead of the more modern Cortex-A78 (which are both more powerful and more power efficient). But it is worth noting that the Pixel 5s Snapdragon 765G also used two Cortex-A76 cores for its main CPU cores, so its possible Google is sticking with what it knows.

The new phones should still be the fastest Pixel phones yet, with Google promising 80 percent faster CPU performance compared to the Pixel 5, and 370 percent faster GPU performance.

The real question, though, is how the Pixel 6 and its Tensor chip hold up compared to other traditional Android flagships. Googles CPU configuration is a unique one, compared to the more traditional four high-performance and four efficiency cores used by major Qualcomm and Samsung chips.

In theory, Google is offering double the number of X1 performance cores the most powerful Arm design than the Snapdragon 888 or Exynos 2100, which both use a single Cortex-X1, three Cortex-A78, and four Cortex-A55 cores. But Google is also swapping out the two high-end cores with midrange ones, which may help battery life and performance... or may just result in a weaker overall device. Well find out soon once weve had the chance to put the Pixel 6 and Tensor through their paces.

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The Pixel 6s Tensor processor promises to put Googles machine learning smarts in your pocket - The Verge

A recent study in Nature Machine Intelligence by researchers at Carnegie Mellon sought to investigate the impact that mitigating bias in machine learning has on accuracy.

Despite what researchers referred to as a "commonly held assumption" that reducing disparities requires either accepting a drop in accuracy or developing new, complex methods, they found that the trade-offs between fairness and effectiveness can be "negligible in practice."

"You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," said Rayid Ghani, a CMU computer science professor and an author on the study, in a statement.

At the same time, Ghani noted, "It does require you to deliberately design systems to be fair and equitable. Off-the-shelf systems won't work."

WHY IT MATTERS

Ghani, along with CMU colleagues Kit Rodolfa and Hemank Lamba, focused on the use of machine learning in public policy contexts specifically with regard to benefit allocation in education, mental health, criminal justice and housing safety programs.

The team found that models optimized for accuracy could predict outcomes of interest, but showed disparities when it came to intervention recommendations.

But when they adjusted the outputs of the models with an eye toward improving their fairness, they discovered that disparities based on race, age or income depending on the situation could be successfully removed.

In other words, by defining the fairness goal upfront in the machine learning process and making design choices to achieve that goal, they could address slanted outcomes without sacrificing accuracy.

"In practice, straightforward approaches such as thoughtful label choice, model design or post-modelling mitigation can effectively reduce biases in many machine learning systems," read the study.

Researchersnotedthat a wide variety of fairness metrics exists, depending on the context, and a broader exploration of the fairness-accuracy trade-offs is warranted especially when stakeholders may want to balance multiple metrics.

"Likewise, it may be possible that there is a tension between improving fairness across different attributes (for example, sex and race) or at the intersection of attributes," read the study.

"Future work should also extend these results to explore the impact not only on equity in decision-making, but also equity in longer-term outcomes and implications in a legal context," it continued.

The researchers noted that fairness in machine learning goes beyond the models predictions; it also includes how those predictions are acted on by human decision makers.

"The broader context in which the model operates must also be considered, in terms of the historical, cultural and structural sources of inequities that society as a whole must strive to overcome through the ongoing process of remaking itself to better reflect its highest ideals of justice and equity," they wrote.

THE LARGER TREND

Experts and advocates have sought to shine a light on the ways that bias in artificial intelligence and ML can play out in a healthcare setting. For instance, a study this past August found that under-developed models may worsen COVID-19 health disparities for people of color.

And as Chris Hemphill, VP of applied AI and growth at Actium Health, told Healthcare IT News this past month, even innocuous-seeming data can reproduce bias.

"Anything you're using to evaluate need, or any clinical measure you're using, could reflect bias," Hemphill said.

ON THE RECORD

"We hope that this work will inspire researchers, policymakers and data science practitioners alike to explicitly consider fairness as a goal and take steps, such as those proposed here, in their work that can collectively contribute to bending the long arc of history towards a more just and equitable society," said the CMU researchers.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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AI and the tradeoff between fairness and efficacy: 'You actually can get both' - Healthcare IT News

One of the most exciting fields to be in right now is machine learning. But starting your journey there can be quite intimidating at first. With the internet containing so much information, the amount of content can be overwhelming for someone, especially at the initial stages of learning. Getting access to the right kind of resources when one is starting out sets the foundation right for growing in the domain.

Here is the list of books that you should read as a beginner just starting out in machine learning:

This is a good book as an introductory text to machine learning. It teaches you how to download data sets and what kind of tools and ML libraries one needs. It introduces you to data scrubbing techniques, including one-hot encoding, binning and dealing with missing data, preparing data for analysis, including k-fold validation, regression analysis to create trend lines, and clustering. The book also contains the basics of neural networks, decision trees, and bias/variance. It does not require prior coding experience to understand the concepts of the book.

This book is written by two data scientists and introduces anyone who wants to use machine learning techniques for practical tasks. It makes the reader understand the meaning of programming languages and the tools needed to make ML-based turns work in reality. It also helps comprehend how daily activities are powered by machine learning and introduces R and Python to perform pattern-oriented tasks and data analysis.

Even if one uses Python as a beginner, the book will help the reader build machine learning solutions. The reader will learn about the basic concepts and applications, the advantages and pitfalls of popularly used machine learning algorithms, and how to represent data processed by machine learning. This will include which aspects of data to focus on, advanced methods for model evaluation and parameter tuning, pipelines for chaining models and encapsulating the workflow and methods for working with text data, including text-specific processing techniques and suggestions for improving your machine learning and data science skills.

This book is a good start for newcomers to machine learning. It contains topics starting with ML basics, classifying with k-nearest neighbours, splitting datasets one feature at a time, decision trees, logistic regression, tree-based regression, using principal component analysis to simplify data, simplifying data with the singular value decomposition and big data and MapReduce. Most of the examples use Python; hence, familiarity in Python will be desirable.

The book is for developers and does not use academic language but takes the reader through techniques used in daily work. It contains examples in Python that bring out the core algorithms of statistical data processing, data analysis, and data visualization in code that one can reuse.

It is a popular choice among machine learning enthusiasts. A newbie in machine learning will find this book comfortable to comprehend, setting the scene for their machine learning journey. Experienced people will use this book as a collection of pointers to the directions of further self-improvement. It comes with a wiki that contains pages that extend some book chapters with additional information, Q&A, code snippets, further reading, tools, etc.

This book gives a good start to someone interested in the field of statistical learning. It includes topics like linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering while citing real-world examples. Each chapter contains a tutorial on implementing the analyses and methods shown in R. It is a combined work of a group of authors with experience teaching machine learning and working with predictive analysis.

If you desire to enter the exciting field of machine learning and build algorithms, these books can act as a stepping stone in your journey.

Sreejani Bhattacharyya is a journalist with a postgraduate degree in economics. When not writing, she is found reading on geopolitics, economy and philosophy. She can be reached at [emailprotected]

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Books To Read To Start Your ML Journey - Analytics India Magazine

Reading Time: 5 minutes

Join our free online course Introduction to Machine Learning and AI to discover the fundamentals of machine learning and learn to train your own machine learning models using free online tools.

Although artificial intelligence (AI) was once the province of science fiction, these days youre very likely to hear the term in relation to new technologies, whether thats facial recognition, medical diagnostic tools, or self-driving cars, which use AI systems to make decisions or predictions.

By the end of this free, online, self-paced course, you will have an appreciation for what goes into machine learning and artificial intelligence systems and why you should think carefully about what comes out.

Youll also often hear about AI systems that use machine learning (ML). Very simply, we can say that programs created using ML are trained on large collections of data to learn to produce more accurate outputs over time. One rather funny application you might have heard of is the muffin or chihuahua? image recognition task.

More precisely, we would say that a ML algorithm builds a model, based on large collections of data (the training data), without being explicitly programmed to do so. The model is finished when it makes predictions or decisions with an acceptable level of accuracy. (For example, it rarely mistakes a muffin for a chihuahua in a photo.) It is then considered to be able to make predictions or decisions using new data in the real world.

But how does all this actually work? If you dont know, its hard to judge what the impacts of these technologies might be, and how we can be sure they benefit everyone an important discussion that needs to involve people from across all of society. Not knowing can also be a barrier to using AI, whether thats for a hobby, as part of your job, or to help your community solve a problem.

For teachers and educators its particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (Weve also got a free seminar series about teaching these topics.)

To help you understand the fundamentals of AI and ML, weve put together a free online course: Introduction to Machine Learning and AI. Over four weeks in two hours per week, learning at your own pace, youll find out how machine learning can be used to solve problems, without going too deeply into the mathematical details. Youll also get to grips with the different ways that machines learn, and you will try out online tools such as Machine Learning for Kids and Teachable Machine to design and train your own machine learning programs.

As well as finding out how these AI systems work, youll look at the different types of tasks that they can help us address. One of these is classification working out which group (or groups) something fits in, such as distinguishing between positive and negative product reviews, identifying an animal (or a muffin) in an image, or spotting potential medical problems in patient data.

Youll also learn about other types of tasks ML programs are used for, such as regression (predicting a numerical value from a continuous range) and knowledge organisation (spotting links between different pieces of data or clusters of similar data). Towards the end of the course youll dive into one of the hottest topics in AI today: neural networks, which are ML models whose design is inspired by networks of brain cells (neurons).

Before an ML program can be trained, you need to collect data to train it with. During the self-paced course youll see how tools from statistics and data science are important for ML but also how ethical issues can arise both when data is collected and when the outputs of an ML program are used.

By the end of the course, you will have an appreciation for what goes into machine learning and artificial intelligence systems and why you should think carefully about what comes out.

The Introduction to Machine Learning and AI course is open for you to sign up to now. Sign-ups will pause after 12 December. Once you sign up, youll have access for six weeks. During this time youll be able to interact with your fellow learners, and before 25 October, youll also benefit from the support of our expert facilitators. So what are you waiting for?

As part of our research on computing education, we would like to find out about educators views on machine learning. Before you start the course, we will ask you to complete a short survey. As a thank you for helping us with our research, you will be offered the chance to take part in a prize draw for a 50 book token!

To develop your computing knowledge and skills, you might also want to:

If you are a teacher in England, you can develop your teaching skills through the National Centre for Computing Education, which will give you free upgrades for our courses (including Introduction to Machine Learning and AI) so youll receive certificates and unlimited access.

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Learn the fundamentals of AI and machine learning with our free online course - Blogdottv

Attendees will gain insights into the vital role human intelligence plays in developing machine learning data operations and AI data solutions

Published: Oct. 21, 2021 at 1:00 PM EDT

LOS GATOS, Calif., Oct. 21, 2021 /PRNewswire/ --iMerit, a leading AI data solutions company, today announced its inaugural conference, the iMerit ML DataOps Summit, which is a live virtual event taking place on December 2, 2021 at 9 a.m. PDT. Hosted in partnership with TechCrunch, the iMerit ML DataOps Summit will bring together innovators at the forefront of data operations, machine learning, and artificial intelligence. Register here.

Attendees will gain insights on the importance of leveraging human intelligence to advance AI, how to solve edge cases with high quality data, scaling data pipelines for rapid deployment and more. Through engaging keynotes, panels, and fireside chats, participants will hear the challenges and opportunities of machine learning data operations trending across a variety of industries, including autonomous mobility, medical AI, geospatial, technology, and more.

Some of this year's featured speakers include:

"A staggering number of companies have accelerated their AI adoption initiatives, with many incorporating AI as a mainstream technology within their business," said Radha Basu, CEO and Founder of iMerit. "As a leader in end-to-end AI data solutions, iMerit looks forward to gathering the top minds in artificial intelligence to discuss strategies around machine learning data operations and unveiling why leveraging human intelligence is the critical path to advancing AI."

Accelerated by COVID-19, digital innovation has put AI and analytics at the forefront of many business operations. The iMerit ML DataOps Summit will provide insights on how businesses can find efficient methods, tools, processes and principles to prepare the data needed to conquer AI at the edge.

"We're excited to host this conference in partnership with iMerit," said Joey Hinson, Senior Director of Operations at TechCrunch. "This dynamic speaker panel will deliver the compelling discussions around AI and machine learning that our audience expects."

Additionally, the iMerit ML DataOps Summit will host a virtual expo showcasing data annotation and automation tool providers that are building the future of ML DataOps.

For more information or to register for the free virtual event, click here.

About iMeritiMerit is a leading AI data solutions companyproviding high quality data across computer vision, natural language processing and content services that powers machine learning and artificial intelligence applications for large enterprises. iMerit provides end-to-end data labeling services to Fortune 500 companies in a wide array of industries including agricultural AI, autonomous vehicles, commerce, geospatial, government, financial services, medical AI and technology. iMerit employs more than 5,000 full-time data annotation experts in Bhutan, Europe, India and the United States. Raising $23.5 million in funding to date, iMerit investors are CDC Group, Khosla Impact, Michael and Susan Dell Foundation and Omidyar Network. For more information, visit imerit.net.

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SOURCE iMerit Technology

The above press release was provided courtesy of PRNewswire. The views, opinions and statements in the press release are not endorsed by Gray Media Group nor do they necessarily state or reflect those of Gray Media Group, Inc.

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iMerit and TechCrunch Announce ML DataOps Summit to be Held on December 2nd, 2021 - WWSB

On this four-week course from the Raspberry Pi Foundation, youll learn about different types of machine learning, and use online tools to train your own AI models. Youll delve into the problems that ML can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a ML model. For teachers and educators its particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (Weve also got a free seminar series about teaching these topics.)

The first week of this course will guide you through how you can use machine learning to label data, whether to work out if a comment is positive or negative or to identify the contents of an image. Then youll look at algorithms that create models to give a numerical output, such as predicting house prices based on information about the house and its surroundings. Youll also explore other types of machine learning that are designed to discover connections and groupings in data that humans would likely miss, giving you a deeper understanding of how it can be used.

To register for the course for free jump over to the official course page by following the link below.

Source : RPiF

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Learn about machine learning and the fundamentals of AI with free Raspberry Pi course - Geeky Gadgets