ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. Though efforts are being made to shed its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders for a fairer deployment. Companies have started to take machine learning model management more seriously now. Recently, a machine learning company Comet.ml, based out of Seattle and founded in 2017, announced that they are making a $4.5 million investment to bring state-of-the-art meta-learning capabilities to the market.

The tools developed by Comet.ml enable data scientists to track, compare, monitor, and optimise model development. Their announcement of an additional $4.5 million investment from existing investors Trilogy Equity Partners and Two Sigma Ventures is aimed at boosting their plans to domesticate the use of machine learning model management techniques to more customers.

Since their product launch in 2018, Comet.ml has partnered with top companies like Google, General Electric, Boeing and Uber. This elite list of customers use comet.al services, which have enterprise-level toolkits, and are used to train models across multiple industries spanning autonomous vehicles, financial services, technology, bioinformatics, satellite imagery, fundamental physics research, and more.

Talking about this new announcement, one of the investors, Yuval Neeman of Trilogy Equity Partners, reminded that the professionals from the best companies in the world choose Comet and that the company is well-positioned to become the de-facto Machine Learning development platform.

This platform, says Neeman, allows customers to build ML models that bring significant business value.

According to a report presented by researchers at Google, there are several ML-specific risk factors to account for in system design, such as:

Debugging all these issues require round the clock monitoring of the models pipeline. For a company that implements ML solutions, it is challenging to manage in-house model mishaps.

If we take the example of Comet again, its platform provides a central place for the team to track their ML experiments and models, so that they can compare and share experiments, debug and take decisive actions on underperforming models with great ease.

Predictive early stopping is a meta-learning functionality not seen in any other experimentation platforms, and this can be achieved only by building on top of millions of public models. And this is where Comets enterprise products come in handy. The freedom of experimentation that these meta learning-based platforms offer is what any organisation would look up to. Almost all ML-based companies would love to have such tools in their arsenal.

Talking about saving the resources, Comet.ml in their press release, had stated that their platform led to the improvement of model training time by 30% irrespective of the underlying infrastructure, and stopped underperforming models automatically, which reduces cost and carbon footprint by 30%.

Irrespective of the underlying infrastructure, it stops underperforming models automatically, which reduces cost and carbon footprint by 30%.

The enterprise offering also includes Comets flagship visualisation engine, which allows users to visualise, explain, and debug model performance and predictions, and a state-of-the-art parameter optimisation engine.

When building any machine learning pipeline, data preparation requires operations like scraping, sampling, joining, and plenty of other approaches. These operations usually accumulate haphazardly and result in what the software engineers would like to call a pipeline jungle.

Now, add in the challenge of forgotten experimental code in the code archives. Things only get worse. The presence of such stale code can malfunction, and an algorithm that runs this malfunctioning code can crash stock markets and self-driving cars. The risks are just too high.

So far, we have seen the use of ML for data-driven solutions. Now the market is ripe for solutions that help those who have already deployed machine learning. It is only a matter of time before we see more companies setting up their meta-learning shops or partner with third-party vendors.

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Is Machine Learning Model Management The Next Big Thing In 2020? - Analytics India Magazine

NEW DELHI: iPhone SE, the first iPhone that Apple has launched in 2020, is also the first iPhone in the companys lineup to use "Single Image Monocular Depth Estimation, as per a blog post by Halide, a popular camera app.This means that the latest generation of iPhone SE is the first iPhone that can generate a portrait effect using nothing but a single, 2D image, claims the app. Readers must note that even though the iPhone XR also offers a single rear camera, it does obtain depth information through hardware. It tapped into the sensors focus pixels, which you can think of as tiny pairs of eyes designed to help with focus. The XR uses the very slight differences seen out of each eye to generate a very rough depth map, says the blog post.However, unlike the iPhone XR, the iPhone SE doesnt use focus pixels as it offers an older sensor same as iPhone 8 as claimed by iFixit that apparently doesnt have enough coverage. Therefore, the depth effect generated by the budget iPhone is said to be based completely on machine learning. Therefore, the iPhone SE is capable of capturing photos in Portrait Mode from both the back and front camera, claims Apple Insider, thanks to the powerful A13 Bionic chipset with a third-generation Neural Engine, which powers the much more expensive iPhone 11 lineup. Meanwhile, the iPhone SE scored a 6 out of 10 repairability score on iFixit ranking, in the overall teardown. Display and battery are said to be the same as the iPhone 8 are easily fixable which is a good thing as these are two more commonly replaced components of smartphones. However, the glass back is said to be fragile and impractical to replace.

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iPhone SE and the 'art' of machine learning - Gadgets Now

Perhaps the most important step we can all take to mitigate the spread of the coronavirus, also known as COVID-19, is to actively practice social distancing.

Why? Because the further away you are from another person, the less likely you'll contract or transmit COVID-19.

But when we go about our daily routines, especially when out on a grocery run or heading to the hospital, social distancing can be a challenging task to uphold.

And some of us just have God awful spatial awareness in general.

But how do we monitor and enforce social distancing when looking at a mass population? We resort to the wonders of artificial intelligence (AI), of course.

In a recent blog post, the company demonstrated a nifty social distancing detector that shows a feed of people walking along a street in the Oxford Town Center of the United Kingdom.

The tool encompasses every individual in the feed with a rectangle. When they're properly observing social distancing, that rectangle is green. But when they get too close to another person (less than 6 feet away), the rectangle turns red, accompanied by a line 'linking' the two people that are too close to one another.

On the right-hand side of the tool there's a 'Bird's-Eye View' that allows for monitoring on a bigger scale. Every person is represented by a dot. Working the same way as the rectangles, the dots are green when social distancing is properly adhered to. They turn red when people get too close.

More specifically, work settings like factory floors where physical space is abundant, thus making manual tracking extremely difficult.

According to Landing AI CEO and Founder Andrew Ng, the technology was developed in response to requests by their clients, which includes Foxconn, the main manufacturer of Apple's prized iPhones.

The company also says that this technology can be integrated into existing surveillance cameras. However, it's still exploring ways in which to alert people when they get too close to each other. One possible method is the use of an audible alarm that rings when individuals breach the minimum distance required with other people.

According to Reuters, Amazon already uses a similar machine-learning tool to monitor its employees in their warehouses. In the name of COVID-19 mitigation, companies around the world are grabbing whatever machine-learning AI tools they can get in order to surveil their employees. A lot of these tools tend to be cheap, off-the-shelf iterations that allow employers to watch their employees and listen to phone calls as well.

Landing AI insists that their tool is only for use in work settings, even including a little disclaimer that reads "The rise of computer vision has opened up important questions about privacy and individual rights; our current system does not recognize individuals, and we urge anyone using such a system to do so with transparency and only with informed consent."

Whether companies that make use of this tool adhere to that, we'll never really know.

But we definitely don't want Big Brother to be watching our every move.

Cover image sourced from New Straits Times / AFP.

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This AI tool uses machine learning to detect whether people are social distancing properly - Mashable SE Asia

Monitors display a video showing facial recognition software in use at the headquarters of the artificial intelligence company Megvii, in Beijing, May 10, 2018. Beijing is putting billions of dollars behind facial recognition and other technologies to track and control its citizens.

Gilles Sabri | The New York Times

The world is facing its biggest health crisis in decades but one of the world's most promising technologies artificial intelligence (AI) isn't playing the major role some may have hoped for.

Renowned AI labs at the likes of DeepMind, OpenAI, Facebook AI Research, and Microsoft have remained relatively quiet as the coronavirus has spread around the world.

"It's fascinating how quiet it is," said Neil Lawrence, the former director of machine learning at Amazon Cambridge.

"This (pandemic) is showing what bulls--t most AI hype is. It's great and it will be useful one day but it's not surprising in a pandemic that we fall back on tried and tested techniques."

Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.

Of course, there are a few useful AI projects happening here and there.

In March, DeepMind announced that it hadused a machine-learning technique called "free modelling" to detail the structures of six proteins associated with SARS-CoV-2, the coronavirus that causes the Covid-19 disease.Elsewhere, Israeli start-up Aidoc is using AI imaging to flag abnormalities in the lungs and a U.K. start-up founded by Viagra co-inventor David Brown is using AI to look for Covid-19 drug treatments.

Verena Rieser, a computer science professor at Heriot-Watt University, pointed out that autonomous robots can be used to help disinfect hospitals and AI tutors can support parents with the burden of home schooling. She also said "AI companions" can help with self isolation, especially for the elderly.

"At the periphery you can imagine it doing some stuff with CCTV," said Lawrence, adding that cameras could be used to collect data on what percentage of people are wearing masks.

Separately, a facial recognition system built by U.K. firm SCC has also been adapted to spot coronavirus sufferers instead of terrorists.In Oxford, England, Exscientia is screening more than 15,000 drugs to see how effective they are as coronavirus treatments. The work is being done in partnership withDiamond Light Source, the U.K.'s national "synchotron."

But AI's role in this pandemic is likely to be more nuanced than some may have anticipated. AI isn't about to get us out of the woods any time soon.

"It's kind of indicating how hyped AI was," said Lawrence, who is now a professor of machine learning at the University of Cambridge. "The maturity of techniques is equivalent to the noughties internet."

AI researchers rely on vast amounts of nicely labeled data to train their algorithms, but right now there isn't enough reliable coronavirus data to do that.

"AI learns from large amounts of data which has been manually labeled a time consuming and expensive task," said Catherine Breslin, a machine learning consultant who used to work on Amazon Alexa.

"It also takes a lot of time to build, test and deploy AI in the real world. When the world changes, as it has done, the challenges with AI are going to be collecting enough data to learn from, and being able to build and deploy the technology quickly enough to have an impact."

Breslin agrees that AI technologies have a role to play. "However, they won't be a silver bullet," she said, adding that while they might not directly bring an end to the virus, they can make people's lives easier and more fun while they're in lockdown.

The AI community is thinking long and hard about how it can make itself more useful.

Last week, Facebook AI announced a number of partnerships with academics across the U.S.

Meanwhile, DeepMind's polymath leader Demis Hassabis is helping the Royal Society, the world's oldest independent scientific academy, on a new multidisciplinary project called DELVE (Data Evaluation and Learning for Viral Epidemics). Lawrence is also contributing.

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A.I. can't solve this: The coronavirus could be highlighting just how overhyped the industry is - CNBC

During the International Conference on Learning Representations (ICLR) 2020 this week, which as a result of the pandemic took place virtually, Turing Award winner and director of the Montreal Institute for Learning Algorithms Yoshua Bengio provided a glimpse into the future of AI and machine learning techniques. He spoke in February at the AAAI Conference on Artificial Intelligence 2020 in New York alongside fellow Turing Award recipients Geoffrey Hinton and Yann LeCun. But in a lecture published Monday, Bengio expounded upon some of his earlier themes.

One of those was attention in this context, the mechanism by which a person (or algorithm) focuses on a single element or a few elements at a time. Its central both to machine learning model architectures like Googles Transformer and to the bottleneck neuroscientific theory of consciousness, which suggests that people have limited attention resources, so information is distilled down in the brain to only its salient bits. Models with attention have already achieved state-of-the-art results in domains like natural language processing, and they could form the foundation of enterprise AI that assists employees in a range of cognitively demanding tasks.

Bengio described the cognitive systems proposed by Israeli-American psychologist and economist Daniel Kahneman in his seminal book Thinking, Fast and Slow. The first type is unconscious its intuitive and fast, non-linguistic and habitual, and it deals only with implicit types of knowledge. The second is conscious its linguistic and algorithmic, and it incorporates reasoning and planning, as well as explicit forms of knowledge. An interesting property of the conscious system is that it allows the manipulation of semantic concepts that can be recombined in novel situations, which Bengio noted is a desirable property in AI and machine learning algorithms.

Current machine learning approaches have yet to move beyond the unconscious to the fully conscious, but Bengio believes this transition is well within the realm of possibility. He pointed out that neuroscience research has revealed that the semantic variables involved in conscious thought are often causal they involve things like intentions or controllable objects. Its also now understood that a mapping between semantic variables and thoughts exists like the relationship between words and sentences, for example and that concepts can be recombined to form new and unfamiliar concepts.

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Attention is one of the core ingredients in this process, Bengio explained.

Building on this, in a recent paper he and colleagues proposed recurrent independent mechanisms (RIMs), a new model architecture in which multiple groups of cells operate independently, communicating only sparingly through attention. They showed that this leads to specialization among the RIMs, which in turn allows for improved generalization on tasks where some factors of variation differ between training and evaluation.

This allows an agent to adapt faster to changes in a distribution or inference in order to discover reasons why the change happened, said Bengio.

He outlined a few of the outstanding challenges on the road to conscious systems, including identifying ways to teach models to meta-learn (or understand causal relations embodied in data) and tightening the integration between machine learning and reinforcement learning. But hes confident that the interplay between biological and AI research will eventually unlock the key to machines that can reason like humans and even express emotions.

Consciousness has been studied in neuroscience with a lot of progress in the last couple of decades. I think its time for machine learning to consider these advances and incorporate them into machine learning models.

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Yoshua Bengio: Attention is a core ingredient of conscious AI - VentureBeat

Office can now suggest better phrases in Word or entire replies in Outlook, design your PowerPoint slides, and coach you on presenting them. Microsoft built those features with Azure Machine Learning and big models - while keeping your Office 365 data private.

The Microsoft Office clients have been getting smarter for several years: the first version of Editor arrived in Word in 2016, based on Bing's machine learning, and it's now been extended to include the promised Ideas feature with extra capabilities. More and more of the new Office features in the various Microsoft 365 subscriptions are underpinned by machine learning.

You get the basic spelling and grammar checking in any version of Word. But if you have a subscription, Word, Outlook and a new Microsoft Editor browser extension will be able to warn you if you're phrasing something badly, using gendered idioms so common that you may not notice who they exclude, hewing so closely to the way your research sources phrased something that you need to either write it in your own words or enter a citation, or just not sticking to your chosen punctuation rules.

SEE:Choosing your Windows 7 exit strategy: Four options(TechRepublic Premium)

Word can use the real-world number comparisons that Bing has had for a while to make large numbers more comprehensible. It can also translate the acronyms you use inside your organization -- and distinguish them from what someone in another industry would mean by them. It can even recognise that those few words in bold are a heading and ask if you want to switch to a heading style so they show up in the table of contents.

Outlook on iOS uses machine learning to turn the timestamp on an email to a friendlier 'half an hour ago' when you have it read out your messages. Mobile and web Outlook use machine learning and natural-language processing to suggest three quick replies for some messages, which might include scheduling a meeting.

Excel has the same natural-language queries for spreadsheets as Power BI, letting you ask questions about your data. PowerPoint Designer can automatically crop pictures, put them in the right place on the slide and suggest a layout and design; it uses machine learning for text and slide structure analysis, image categorisation, recommending content to include and ranking the layout suggestions it makes. The Presenter Coach tells you if you're slouching, talking in a monotone or staring down at your screen all the time while you're talking, using machine learning to analyse your voice and posture from your webcam.

How PowerPoint Designer uses AML (Azure Machine Learning).

Image: Microsoft

Many of these features are built using the Azure Machine Learning service, Erez Barak, partner group program manager for AI Platform Management, told TechRepublic. At the other extreme, some call the pre-built Azure Cognitive Services APIs for things like speech recognition in the presentation coach, as well as captioning PowerPoint presentations in real-time and live translation into 60-plus languages (and those APIs are themselves built using AML).

Other features are based on customising pre-trained models like Turing Neural Language Generation, a seventeen-billion parameter deep-learning language model that can answer questions, complete sentences and summarize text -- useful for suggesting alternative phrases in Editor or email replies in Outlook. "We use those models in Office after applying some transfer learning to customise them," Barak explained. "We leverage a lot of data, not directly but by the transfer learning we do; that's based on big data to give us a strong natural-language understanding base. For everything we do in Office requires that context; we try to leverage the data we have from big models -- from the Turing model especially given its size and its leadership position in the market -- in order to solve for specific Office problems."

AML is a machine-learning platform for both Microsoft product teams and customers to build intelligent features that can plug into business processes. It provides automated pipelines that take large amounts of data stored in Azure Data Lake, merge and pre-process the raw data, and feed them into distributed training running in parallel across multiple VMs and GPUs. The machine-learning version of the automated deployment common in DevOps is known as MLOps. Office machine-learning models are often built using frameworks like PyTorch or TensorFlow; the PowerPoint team uses a lot of Python and Jupiter notebooks.

The Office data scientists experiment with multiple different models and variations; the best model then gets stored back into Azure Data Lake and downloaded into AML using the ONNX runtime (open-sourced by Microsoft and Facebook) to run in production without having to be rebuilt. "Packaging the models in the ONNX runtime, especially for PowerPoint Designer, helps us to normalise the models, which is great for MLOps; as you tie these into pipelines, the more normalised assets you have, the easier, simpler and more productive that process becomes," said Barak.

ONNX also helps with performance when it comes to running the models in Office, especially for Designer. "If you think about the number of inference calls or scoring calls happening, performance is key: every small percentage and sub-percentage point matters," Barak pointed out.

A tool like Designer that's suggesting background images and videos to use as content needs a lot of compute and GPU to be fast enough. Some of the Turing models are so large that they run on the FPGA-powered Brainwave hardware inside Azure because otherwise they'd be too slow for workloads like answering questions in Bing searches. Office uses the AML compute layer for training and production which, Barak said, "provides normalised access to different types of compute, different types of machines, and also provides a normalised view into the performance of those machines".

"Office's training needs are pretty much bleeding edge: think long-running, GPU-powered, high-bandwidth training jobs that could run for days, sometimes for weeks, across multiple cores, and require a high level of visibility into the end process as well as a high level of reliability," Barak explained. "We leverage a lot of high-performing GPUs for both training the base models and transfer learning." Although the size of training data varies between the scenarios, Barak estimates that fine-tuning the Turing base model with six months of data would use 30-50TB of data (on top of the data used to train the original model).

Acronyms accesses your Office 365 data, because it needs to know which acronyms your organisation uses.

Image: Mary Branscombe/TechRepublic

The data used to train Editor's rewrite suggestions includes documents written by people with dyslexia, and many of the Office AI features use anonymised usage data from Office 365 usage. Acronyms is one of the few features that specifically uses your own Office 365 data, because it needs to find out which acronyms your organisation uses, but that isn't shared with any other Office users. Microsoft also uses public data for many features rather than trying to mine that from private Office documents. The similarity checker uses Bing data, and Editor's sentence rewrite uses public data like Wikipedia as well as public news data to train on.

As the home of so many documents, Office 365 has a wealth of data, but it also has strong compliance policies and processes that Microsoft's data scientists must follow. Those policies change over time as laws change or Office gets accredited to new standards -- "think of it as a moving target of policies and commitments Office has made in the past and will continue to make," Barak suggested. "In order for us to leverage a subset of the Office data in machine learning, naturally, we adhere to all those compliance promises."

LEARN MORE:Office 365 Consumer pricing and features

But models like those used in Presentation Designer need frequent retraining (at least every month) to deal with new data, such as which of the millions of slide designs it suggests get accepted and are retained in presentations. That data is anonymised before it's used for training, and the training is automated with AML pipelines. But it's important to score retrained models consistently with existing models so you can tell when there's an improvement, or if an experiment didn't pan out, so data scientists need repeated access to data.

"People continuously use that, so we continuously have new data around people's preferences and choices, and we want to continuously retrain. We can't have a system that needs to be adjusted over and over again, especially in the world of compliance. We need to have a system that's automatable. That's reproducible -- and frankly, easy enough for those users to use," Barak said.

"They're using AML Data Sets, which allow them to access this data while using the right policies and guard rails, so they're not creating copies of the data -- which is a key piece of keeping the compliance and trust promise we make to customers. Think of them as pointers and views into subsets of the data that data scientists want to use for machine learning."It's not just about access; it's about repeatable access, when the data scientists say 'let's bring in that bigger model, let's do some transfer learning using the data'. It's very dynamic: there's new data because there's more activity or more people [using it]. Then the big models get refreshed on a regular basis. We don't just have one version of the Turing model and then we're done with it; we have continuous versions of that model which we want to put in the hands of data scientists with an end-to-end lifecycle."

Those data sets can be shared without the risk of losing track of the data, which means other data scientists can run experiments on the same data sets. This makes it easier for them to get started developing a new machine-learning model.

Getting AML right for Microsoft product teams also helps enterprises who want to use AML for their own systems. "If we nail the likes and complexities of Office, we enable them to use machine learning in multiple business processes," Barak said. "And at the same time we learn a lot about automation and requirements around compliance that also very much applies to a lot of our third-party customers."

Be your company's Microsoft insider by reading these Windows and Office tips, tricks, and cheat sheets. Delivered Mondays and Wednesdays

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Microsoft Office 365: How these Azure machine-learning services will make you more productive and efficient - TechRepublic

Word in the tech community is that Apple ($NASDAQ:AAPL) employees are begnning to report hiring freezes for certain groups within the company. But other reports are that hiring is continuing at the Cupertino tech giant. In fact, we've reported on the former.

It turns out that both reports are correct. For some divisions, like Marketing and Corporate Functions, openings have been reduced. But for others, like Hardware and Machine Learning, openings and subsequent hiring appear to be as brisk as ever.

To be clear, overall, job listings at Apple have been cut back.

As recently as mid-March, Apple job listings were nearing the 6,000 mark, which would have been the company's most prolific hiring spree in history. But in late March, it became clear that no one would be going into the office any time soon, and openings quickly began disappearing from Apple's recruitment site. As of this week, openings at Apple are down to 5,240, signaling a decrease in hiring of about 13%.

But not all divisions are stalling their job listings. NeitherApple's "Hardware" or"Machine Learning and AI" groups show a decline in job listings of note.

Hardware openings are flat at worst. Today's 1,570 openings isn't significantly different than a high of 1,600 in March.

Apple's "Machine Learning and AI" group remains as healthy as ever when it comes to new listings being posted to the company's careers sites. As of this week, the team has 334 openings. Last month, that number was 300, an 11% increase in hiring activity.

However, other groups at Apple have seen significant decreases in job listings, including "Software and Services", "Marketing", and "Corporate Functions".

Apple's "Software and Services" team saw a siginificant drop in openings, particularly on April 10, when around 110 openings were cut from the company's recruiting website overnight. Since mid-March, openings on the team have fallen by about 12%.

Between April 14 and April 23, the number of listings for Apple's "Marketing" team dropped by 84. In late March, Apple was seeking 311 people for its Marketing team. Since then, openings have fallen by 36% for the team.

"Corporate Functions" jobs at Apple, which include everything from HR to Finance and Legal, have also seen a steep decline in recent weeks. In late March, Apple listed more than 300 openings for the team. As of this week, it has just around 200 openings, a roughly 1/3 hiring freeze.

So is Apple in the middle of a hiring freeze? Some parts of the company appear frozen. Others appear as hot as ever. Given the in-person nature of Marketing and Corporate Functions jobs, it's not surprising that the company would tap the breaks on interviewing for such positions. On the other hand, engineers working on hardware and machine learning can be remote interviewed and onboarded with equipment delivery.

So, yes, and yes. Apple is, and is not, in the middle of a hiring freeze.

Thinknum tracks companies using the information they post online - jobs, social and web traffic, product sales and app ratings - andcreates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.

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Apple is on a hiring freeze ... except for its Hardware, Machine Learning and AI teams - Thinknum Media

According toBlueWeave Consulting, The globalArtificial Intelligence market&Advanced Machinehas reached USD 29.8 Billion in 2019 and projected to reach USD 281.24 Billion by 2026 and anticipated to grow with CAGR of 37.95% during the forecast period from 2020-2026, owing to increasing overall global investment in Artificial Intelligence Technology.

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Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, artificial intelligence and advanced machine learning technology are paving the way for effective marketing, content creation, and consumer interactions.

In the organization size segment, large enterprises segment is estimated to have the largest market share and the SMEs segment is estimated to grow at the highest CAGR over the forecast period of 2026. The rapidly developing and highly active SMEs have raised the adoption of artificial intelligence and machine learning solutions globally, as a result of the increasing digitization and raised the cyber risks to critical business information and data. Large enterprises have been heavily adopting artificial intelligence and machine learning to extract the required information from large amounts of data and forecast the outcome of various problems.

Predictive analysis and machine learning and is rapidly used in retail, finance, and healthcare. The trend is estimated to continue as major technology companies are investing resources in the development of AI and ML. Due to the large cost-saving, effort-saving, and the reliable benefits of AI automation, machine learning is anticipated to drive the global artificial intelligence and Advanced machine learning market during the forecast period of 2026.

Digitalization has become a vital driver of artificial intelligence and advanced machine learning market across the region. Digitalization is increasingly propelling everything from hotel bookings, transport to healthcare in many economies around the globe. Digitalization had led to rising in the volume of data generated by business processes. Moreover, business developers or crucial executives are opting for solutions that let them act as data modelers and provide them an adaptive semantic model. With the help of artificial intelligence and Advanced machine learning business users are able to modify dashboards and reports as well as help users filter or develop reports based on their key indicators.

Geographically, the Global Artificial Intelligence & Advanced Machine Learning market is bifurcated into North America, Asia Pacific, Europe, Middle East, Africa & Latin America. The North America is dominating the market due to the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. North America has rapidly changed, and the most competitive global market in the world. The Asia-pacific region is estimated to be the fastest-growing region in the global AI & Advanced ML market. The rising awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the Asia-pacific region, has led Asia-pacific to become a highly potential market.

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Artificial Intelligence & Advanced Machine Learning Market: Competitive Landscape

The major market players in the Artificial Intelligence & Advanced Machine Learning market are ICarbonX, TIBCO Software Inc., SAP SE, Fractal Analytics Inc., Next IT, Iflexion, Icreon, Prisma Labs, AIBrain, Oracle Corporation, Quadratyx, NVIDIA, Inbenta, Numenta, Intel, Domino Data Lab, Inc., Neoteric, UruIT, Waverley Software, and Other Prominent Players are expanding their presence in the market by implementing various innovations and technology.

Link:
Artificial Intelligence & Advanced Machine learning Market is expected to grow at a CAGR of 37.95% from 2020-2026 - Latest Herald

H2O.ai and SAS Institute

Global Machine Learning Market Segmentation

This market was divided into types, applications and regions. The growth of each segment provides an accurate calculation and forecast of sales by type and application in terms of volume and value for the period between 2020 and 2026. This analysis can help you develop your business by targeting niche markets. Market share data are available at global and regional levels. The regions covered by the report are North America, Europe, the Asia-Pacific region, the Middle East, and Africa and Latin America. Research analysts understand the competitive forces and provide competitive analysis for each competitor separately.

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Machine Learning Market Region Coverage (Regional Production, Demand & Forecast by Countries etc.):

North America (U.S., Canada, Mexico)

Europe (Germany, U.K., France, Italy, Russia, Spain etc.)

Asia-Pacific (China, India, Japan, Southeast Asia etc.)

South America (Brazil, Argentina etc.)

Middle East & Africa (Saudi Arabia, South Africa etc.)

Some Notable Report Offerings:

-> We will give you an assessment of the extent to which the market acquire commercial characteristics along with examples or instances of information that helps your assessment.

-> We will also support to identify standard/customary terms and conditions such as discounts, warranties, inspection, buyer financing, and acceptance for the Machine Learning industry.

-> We will further help you in finding any price ranges, pricing issues, and determination of price fluctuation of products in Machine Learning industry.

-> Furthermore, we will help you to identify any crucial trends to predict Machine Learning market growth rate up to 2026.

-> Lastly, the analyzed report will predict the general tendency for supply and demand in the Machine Learning market.

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Table of Contents:

Study Coverage: It includes study objectives, years considered for the research study, growth rate and Machine Learning market size of type and application segments, key manufacturers covered, product scope, and highlights of segmental analysis.

Executive Summary: In this section, the report focuses on analysis of macroscopic indicators, market issues, drivers, and trends, competitive landscape, CAGR of the global Machine Learning market, and global production. Under the global production chapter, the authors of the report have included market pricing and trends, global capacity, global production, and global revenue forecasts.

Machine Learning Market Size by Manufacturer: Here, the report concentrates on revenue and production shares of manufacturers for all the years of the forecast period. It also focuses on price by manufacturer and expansion plans and mergers and acquisitions of companies.

Production by Region: It shows how the revenue and production in the global market are distributed among different regions. Each regional market is extensively studied here on the basis of import and export, key players, revenue, and production.

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Tags: Machine Learning Market Size, Machine Learning Market Trends, Machine Learning Market Growth, Machine Learning Market Forecast, Machine Learning Market Analysis

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Machine Learning Market Segmentation, Application, Technology, Analysis Research Report and Forecast to 2026 - Cole of Duty

Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers

To be in the race, it is important to evolve with time. Technology is booming and the new-age era has seen many changes. In the past, since the world met the internet, things changed and how. From the time of cellphones to smartphones, computers to portable laptops, things have seamlessly changed with social media taking over everyone. Earlier Facebook was considered only for chatting and now it has become a medium to make money by creating content. Besides this, there are many other platforms like YouTube, TikTok, and Instagram to earn in millions. One of the key social media players, Rashed Ali Almansoori is a digital genius with years of experience.

He is a tech blogger who believes to cope up with the latest trends. Being a digital creator, Rashed loves to create meaningful yet informative content about technology. Authenticity is the key to establish your target audience over the web, says the blogger. His other expertise includes web development, web designing, SEO building, and promoting brands over the digital domain. Rashed states that many businesses have taken the digital route considering the popular social media has given in the last decade. The coming decade will see many other innovations out of which Artificial Intelligence will be the main highlight among all.

The digital expert is currently learning the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). It would not be a surprise if machines perform tasks effectively than humans in the coming time. Upgrading yourself to stay in the game is the only solution, quoted Rashed. By learning the courses, he aims to integrate them into his works. Bringing novelty in his work is what the blogger is doing and it will benefit him in the future. The past year, the 29-year old techie built a strong image of himself on social media and his website is garnering millions of visitors from the Middle East and other countries.

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Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers - IBG NEWS