In this special guest feature, Farhan Abrol, Head of Machine Learning Products at Pure Storage, examines the disparity between the hype and whats been delivered, and where well see the most impactful advancements in efficiency and capacity in the coming year. Farhan oversees the development and execution of Meta, a machine learning engine that uses IoT data to optimize customer experience. He has 5+ years of enterprise technology experience and has successfully led engineer teams in enhancing data resiliency, streamlining management operations, and improving customer experience. Farhan is also passionate about shaping the future of the industry, and is an active mentor for future IT leaders.

Enterprise investment in intelligentinfrastructure management is growing seemingly in lockstep with the rise inhype around the potential for AI and machine learning to improve IT infrastructure yet the anticipated value is only beginning to be realized.

IDC estimates global spending on cognitive systems will reach $31.3B by 2019. Many of the applications and use cases for these systems are in infrastructure management services currently one of the least automated parts of IT capabilities.

That represents a big opportunity for many organizations particularly around things like monitoring and workload planning. Intelligent IT infrastructure is predictive, self-healing, self-optimizing, and self-protective automating all of these key elements without the need for human intervention. The most common focus areas for this type of investment include capacity planning, performance tuning, and observability and log analysis. Yet complexity, siloed data and other issues are proving to be barriers as IT teams seek to modernize with artificial intelligence for IT operations (AIOps).

Weve already seen some examples of activity in AIOps that point to where were heading. Netapp recently acquired Cognigo, an Israeli data compliance and security supplier, which provides an AI-driven data protection platform to help enterprises protect their data and stay in compliance with privacy regulations such as Europes GDPR.

In June, HPE expanded its hyper-converged infrastructure portfolio when it introduced HPE Nimble Storage dHCI, which provides self-optimizing performance, predictive support automation and problem prevention. And ServiceNow acquired Israel-based Loom Systems to add AIOps to its portfolio.

Workload planning

As these investments play out, they will begin to provide actual value to customers across the enterprise landscape. But were not there yet. While some use cases have yet to be put into action in a big way, perhaps the most meaningful benefits that AI has brought to infrastructure management so far are around planning and tuning.

Workload planning is one of the hardest thingsto do in storage. A storage environment isnt static new requirements come inevery day. Changes may include, for example, a need to double an Oracleworkload or support five times the number of VDI users.

To anticipate those requirements, everyinfrastructure admin needs to do planning and tuning. It used to take hours leading to either underutilization or overuse neither of which are optimal.

Machine learning models, on the other hand, canpredict load and capacity, find the ideal location for workloads, and minimizerisk. Being able to forecast the future means IT teams can sleep easier,knowing they can forecast and simulate the impact of changing hardwarecomponents on both load and capacity.

Getting to the workload DNA for ML models

At the heart of building intelligent planningtools that impact the different outcome measures we care about namelyperformance and capacity is having as many signals as possible. The more dataone can gather around the nature of the workload the time series of readbandwidth, write IOPS, IOSize, the spatial overwrite pattern and the like thebetter the models can perform. We call this holistic representation theworkload DNA.

Our models take the workload DNA as inputs and build time series forecastingand regression models to predict how performance and capacity will evolve on apiece of infrastructure. Doing this across all pieces of infrastructure allowsus to give IT teams an easy view into which components will run out of steam onwhich axis, when.

We then take the next step and allow teams tosimulate migrating workloads between different nodes and suggest placements foroptimal load balancing and utilization across performance and capacity. Allthis is enabled by the core prediction models. Its exciting stuff and the tipof the iceberg for AIOps.

The road ahead

As models mature, machine learning for infrastructure planning will go from advisory roles to automated action, and the focus will turn to data. In order for predictive feedback systems to scale and be applicable in more contexts, machine learning will be applied to the task of efficiently finding where the model performs poorly, and augmenting data for that feature space. Just take a look at the number of companies being funded for labelling data. The quality of the models is reasonably mature; the quality of the data dictates how effective theyll be.

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Microsoft claims a machine learning models its built for software developers can distinguish between security and non-security bugs 99% of the time.

Microsoft plans to open-source the methodology behind a machine learning algorithm that it claims can distinguish between security bugs and non-security bugs with 99% accuracy.

The company developed a machine learning model to help software developers more easily spot security issues and identify which ones need to prioritized.

By pairing the system with human security experts, Microsoft said it was able to develop an algorithm that was not only able to correctly identify security bugs with nearly 100% accuracy, but also correctly flag critical, high priority bugs 97% of the time.

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The company plans to open-source its methodology on GitHub "in the coming months".

According to Microsoft, its team of 47,000 developers generate some 30,000 bugs every month across its AzureDevOps and GitHub silos, causing headaches for security teams whose job it is to ensure critical security vulnerabilities don't go missed.

While tools that automatically flag and triaged bugs are available, sometimes false-positives are tagged or bugs are classified as low-impact issues when they are in fact more severe.

To remedy this, Microsoft set to work building a machine learning model capable of both classifying bugs as security or non-security issues, as well as identifying critical and non-critical bugs "with a level of accuracy that is as close as possible to that of a security expert."

This first involved feeding the model training data that had been approved by security experts, based on statistical sampling of security and non-security bugs. Once the production model had been approved, Microsoft set about programming a two-step learning model that would enable the algorithm to learn how to distinguish between security bugs and non-security bugs, and then assign labels to bugs indicating whether they were low-impact, important or critical.

Crucially, security experts were involved with the production model through every stage of the journey, reviewing and approving data to confirm labels were correct; selecting, training and evaluating modelling techniques; and manually reviewing random samples of bugs to assess the algorithm's accuracy.

Scott Christiansen, Senior Security Program Manager at Microsoft and Mayana Pereira, Microsoft Data and Applied Scientist, explained that the model was automatically re-trained with new data to it kept pace with the Microsoft's internal production cycle.

"The data is still approved by a security expert before the model is retrained, and we continuously monitor the number of bugs generated in production," they said.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99 percent of the time. The model is also 97 percent accurate at labeling critical and non-critical security bugs.

"This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited."

From the hottest programming languages to the jobs with the highest salaries, get the developer news and tips you need to know. Weekly

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Developers: This new tool spots critical security bugs 97% of the time - TechRepublic

Machine Learning as a Service Market research report enhanced worldwide COVID19 Impact analysis on Market Size (Value, Production, Sales, Consumption, Revenue, and Growth Rate), Gross Margin, Industry Chain, Trends, Top Manufacturers, Development Trends, History and 6 Year Forecast. This Machine Learning as a Service Market competitive landscapes provides details by topmost manufactures like (Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround) with data such as Company Profiles, Trade Sales Volume, Gross, Cost, Industry Share By Type, Product Revenue , Specifications and Contact Information. Besides, Machine Learning as a Service industry report helps to analyse competitive developments such as Joint Ventures, Strategic Alliances, Mergers and Acquisitions, New Product Developments, Research and Developments.

Get Free Sample PDF (including COVID19 Impact Analysis, full TOC, Tables and Figures)of Machine Learning as a Service[emailprotected]https://www.researchmoz.us/enquiry.php?type=S&repid=2302143

Target Audience of the Machine Learning as a Service Market in This Study: Key Consulting Companies & Advisors, Large, medium-sized, and small enterprises, Venture capitalists, Value-Added Resellers (VARs), Manufacturers, Third-party knowledge providers, Equipment Suppliers/ Buyers, Machine Learning as a Service market Investors/Investment Bankers, Research Professionals, Emerging Companies, Service Providers.

Scope of Machine Learning as a Service Market:Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

On the basis of product type, this report displays the shipments, revenue (Million USD), price, and market share and growth rate of each type.

Private clouds Public clouds Hybrid cloud

On the basis on the end users/applications,this report focuses on the status and outlook for major applications/end users, shipments, revenue (Million USD), price, and market share and growth rate foreach application.

Personal Business

Do You Have Any Query Or Specific Requirement? Ask to Our Industry[emailprotected]https://www.researchmoz.us/enquiry.php?type=E&repid=2302143

Geographically, the report includes the research on production, consumption, revenue, Machine Learning as a Service market share and growth rate, and forecast (2020-2026) of the following regions:

Key Developments in the Machine Learning as a Service Market:

To describe Machine Learning as a Service Introduction, product type and application, market overview, market analysis by countries, Machine Learning as a Service market Opportunities, Market Risk, Market Driving Force;

To analyze the manufacturers of Machine Learning as a Service market , with Profile, Main Business, News, Sales, Price, Revenue and Market Share in 2016 and 2020;

To display the competitive situation among the top manufacturers in Global, with sales, revenue and Machine Learning as a Service market share in 2016 and 2020;

To analyze the key countries by manufacturers, Type and Application, covering North America, Europe, Asia Pacific, Middle-East and South America, with sales, revenue and Machine Learning as a Service market share by manufacturers, types and applications;

To analyze the Machine Learning as a Service market Manufacturing Cost, Key Raw Materials and Manufacturing Process etc.

To analyze the Industrial Chain, Sourcing Strategy and Downstream End Users (buyers);

To describe Machine Learning as a Service market sales Channel, Distributors, Traders, Dealers etc.

To describe Machine Learning as a Service market Research Findings and Conclusion, Appendix, Methodology and Data Source.

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Machine Learning as a Service Market COVID19 Impact Analysis Drivers, Analysis, Share, Growth, and Trends & Forecast to 2026: Amazon, Oracle...

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the ML process.

When it comes to deploying ML models for real-time prediction, Amazon SageMaker provides you with a large selection of AWS instance types, from small CPU instances to multi-GPU instances. This lets you find the right cost/performance ratio for your prediction infrastructure. Today we announce the availability of Inf1 instances in Amazon SageMaker to deliver high performance, low latency, and cost-effective inference.

The Amazon EC2 Inf1 instances were launched at AWS re:Invent 2019. Inf1 instances are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads. When compared to G4 instances, Inf1 instances offer up to three times the inferencing throughput and up to 45% lower cost per inference.

Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips. An AWS Inferentia chip contains four NeuronCores. Each implements a high-performance systolic array matrix multiply engine, which massively speeds up typical deep learning operations such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps cut down on external memory accesses and saves I/O time in the process.

When several AWS Inferentia chips are available on an Inf1 instance, you can partition a model across them and store it entirely in cache memory. Alternatively, to serve multi-model predictions from a single Inf1 instance, you can partition the NeuronCores of an AWS Inferentia chip across several models.

To run machine learning models on Inf1 instances, you need to compile models to a hardware-optimized representation using the AWS Neuron SDK. Since the launch of Inf1 instances, AWS has released five versions of the AWS Neuron SDK that focused on performance improvements and new features, with plans to add more on a regular cadence. For example, image classification (ResNet-50) performance has improved by more than 2X, from 1100 to 2300 images/sec on a single AWS Inferentia chip. This performance improvement translates to 45% lower cost per inference as compared to G4 instances. Support for object detection models starting with Single Shot Detection (SSD) was also added, with Mask R-CNN coming soon.

Now let us show you how you can easily compile, load and run models on ml.Inf1 instances in Amazon SageMaker.

Compiling and deploying models for Inf1 instances in Amazon SageMaker is straightforward thanks to Amazon SageMaker Neo. The AWS Neuron SDK is integrated with Amazon SageMaker Neo to run your model optimally on Inf1 instances in Amazon SageMaker. You only need to complete the following steps:

In the following example use case, you train a simple TensorFlow image classifier on the MNIST dataset, like in this sample notebook on GitHub. The training code would look something like the following:

To compile the model for an Inf1 instance, you make a single API call and select ml_inf1 as the deployment target. See the following code:

Once the machine learning model has been compiled, you deploy the model on an Inf1 instance in Amazon SageMaker using the optimized estimator from Amazon SageMaker Neo. Under the hood, when creating the inference endpoint, Amazon SageMaker automatically selects a container with the Neo Deep Learning Runtime, a lightweight runtime that will load and invoke the optimized model for inference.

Thats it! After you deploy the model, you can invoke the endpoint and receive predictions in real time with low latency. You can find a full example on Github.

Inf1 instances in Amazon SageMaker are available in four sizes: ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, and ml.inf1.24xlarge. Machine learning models developed using TensorFlow and MxNet frameworks can be compiled with Amazon SageMaker Neo to run optimally on Inf1 instances and deployed on Inf1 instances in Amazon SageMaker for real-time inference. You can start using Inf1 instances in Amazon SageMaker today in the US East (N. Virginia) and US West (Oregon) Regions.

Julien Simon is an Artificial Intelligence & Machine Learning Evangelist for EMEA, Julien focuses on helping developers and enterprises bring their ideas to life.

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Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference - idk.dev

Like many of you, my focus during this crisis has been less on analytics and more about family, friends, etc. which on a more positive note seems to gain greater emphasis as we reassess our priorities. But the bombardment of news regarding this crisis certainly focuses on numbers in terms of providing a perspective of when this crisis might end. The discussion revolves around the so-called notion of flattening the curve and essentially looks at two key metrics: Number of positive COVID-19 cases Number of Deaths Given my other priorities, I have not really paid attention to these numbers being

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The report entitled Machine Learning as a Service Market: Global Industry Analysis 2020-2026is a comprehensive research study presenting significant data By Reportspedia.com

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Chapter 2 display top manufacturers of Machine Learning as a Service market with sales and revenue and market share. Furthermore, report analyses the import and export scenario of industry, demand and supply ratio, labor cost, raw material supply, production cost, marketing sources, and downstream consumers of market.

Chapter 3, 4, 5 analyses Machine Learning as a Service report competitive analysis based on product type, their region wise depletion and import/export analysis, the composite annual growth rate of market and foretell study from 2017 to 2026.

Chapter 6 gives an in-depth study of Machine Learning as a Service business channels, market sponsors, vendors, dispensers, merchants, market openings and risk.

Chapter 7 gives Machine Learning as a Service market Research Discoveries and Conclusion

Chapter 8 gives Machine Learning as a Service Appendix

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Global Machine Learning as a Service Market Developments, Key Players, Trending Technologies and Forecast to 2026 Cole Reports - Cole of Duty

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Artificial Intelligence and Machine Learning in Big Data and IoT Market to Exhibit Impressive Growth by2020-2026 | Augury Systems, Baidu, C-B4, Comfy,...

Open Resources to Become Knowledgeable in the Field of AI

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As Artificial Intelligence has become one of the most significant forces driving innovation and economic development, this societal transformation requires new knowledge and an additional set of skills. Just as knowing a BIM software has become a prerequisite for most architecture jobs, understanding or even knowing how to use AI-related tools would become a desirable asset, if not a requirement in the future. However, with a vast array of information available, how does one begin to venture into this topic? The following is a compilation of online resources, lectures, and courses, that could provide a better understanding of the field and how to incorporate it into the practice of architecture.

What does Artificial Intelligence represent, what is the difference between machine learning and deep learning? These notions might seem interchangeable and so navigating the topic could become confusing. Before diving into the actual list of resources, it is essential to have the proper use of the most common terms.

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with the development of systems able to perform tasks typically requiring human intelligence. The AI encountered in different applications today is Artificial Narrow Intelligence (ANI), or "weak AI", used on performing a specific task, within a limited context, following pre-programmed rules. Google search, personal assistants, image recognition software, all fall into this category. Artificial General Intelligence (AGI) or Strong AI is still the realm of science fiction, as it would entail the general intelligence of a human being, able to solve any problem.

Put simply, Machine Learning is a subfield of AI, which consists of feeding data to a computer and using statistics and trial and error to help the network learn how to get better at a task, without having been programmed explicitly for that task, thus eliminating the need for writing overwhelmingly extensive code. Machine learning allows computers to make connections, discover patterns and make predictions based on what they learned in the past. A great way of understanding how this works in practice is the visual introduction in machine learning, created by R2D3, which uses a hypothetical example to explain the machine learning process.

Deep learning is a type of machine learning that feeds the data through a neural network architecture inspired by the human way of processing information, known as Artificial Neural Networks (ANNs). An example of usage for machine learning and deep learning is Google Image search.

Generative Design is a buzzword that has penetrated the architecture field a while now (see Archdaily's coverage of the topic here), but can it be framed as Artificial Intelligence or is it just a problem solver engaging multiple variables? Generative design is an iterative and exploratory process, where the input consists of parameters such as spatial requirements, performance, material constraints, as well as design goals. The software explores all possible solutions. Whether it falls in the realm of AI or not depends on whether the software is capable of testing and learning from each iteration, thus "learning" to give optimized answers.

With the ambitious goal to educate 1 % of European citizens in the fundamentals of AI by 2021, Elements of AI, a series of online courses created by Reaktor and the University of Helsinki sets the foundation for understanding the field, explaining what AI is, what it can and can't do, and how to start employing AI methods. The course is free and available in multiple languages, the aim being to teach people from a variety of backgrounds on the basic concepts of artificial intelligence technology. With almost 400.000 students so far from over 170 countries, the course is indeed proving an accessible and engaging resource.

Another beginner course, Coursera's Introduction to Artificial Intelligence, is also a great place to start building up the foundation concepts of the field, and it also contains some hands-on exercises.

For a non-aficionado, navigating literature on the subject of AI can be daunting. Therefore this Machine Learning Glossary provided by Google is a fast and reliable way of checking the meaning of terms when facing specialized jargon. The concepts are explained in a clear, straightforward fashion, and the glossary is an information resource in itself.

Learning is always more successful with a hands-on approach, and you can get acquainted with AI tools without having to learn to code. Project Runway ML is a public beta software and a platform dedicated to creators of all kinds that allow them to use AI tools without necessitating coding experience. From object detection to generating images from sketches, or creating text descriptions for images, the platform is a fun way to explore some design applications of AI.

This discussion at Columbia GSAPP explores artificial intelligence in architecture through the lens of several research projects.

Harvard GSD's lecture presents how AI-based tools and computer simulations could support landscape architecture.

In this lecture at the Strelka Institute, sociologist and professor Benjamin Bratton talks about AI and shares the results of the research projects undertaken in collaboration with Google Research. Read more about Bratton's ideas concerning in this Archdaily interview.

In addition, you can now take a virtual tour of the AI & Architecture exhibition, which was scheduled to take place at the Pavillon de l'Arsenal in Paris, France, but closed down due to the COVID-19 crisis. The curators decided to offer to the public an immersive experience, by recreating the exhibit as a virtual tour. Featuring the opening conference, a timeline of AI development, examples of its application to architecture, the exhibition is very rich in information and indeed an immersive experience.

Going further would require maths, as well as computer science prior training. Still, there are plenty of online resources addressing a more knowledgeable audience.

Google's Machine Learning Crash Course does not require any prior knowledge in machine learning, but students should have some experience programming in Python. However, all the different topics have an Introduction page that can at least provide an idea about how the process works.

Another Machine Learning course, this time offered by Stanford University, focuses on gaining the practical know-how on the subject.

For those already skilled in computer science, there is also the Artificial Intelligence course available on MIT's Youtube channel.

For a more comprehensive picture of AI in architecture, see Archdaily's coverage of the topic here.

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Open Resources to Become Knowledgeable in the Field of AI - ArchDaily

Tesla has released a few impressive videos of its Autopilot-powered emergency braking feature helping to avoid running over inattentive pedestrians.

What might be even more impressive is that the automaker says that it sees those events happen every day.

Theres a lot of talk about Tesla Autopilot, but one of the least reported aspects of Teslas semi-autonomous driver-assist system is that it powers a series of safety features that Tesla includes for free in all cars.

One of those features is Emergency Automatic Braking.

We saw the Autopilot-powered safety feature stop for pedestrians in impressive tests by Euro NCAP last year, but now we see it perform in real-world scenarios and avoiding potentially really dangerous situations.

Tesla has now released some examples of its system braking just in time to save pedestrians.

The new videos were released by Andrej Karpathy, Teslas head of AI and computer vision, in a new presentation at the Scaled Machine Learning Conference.

It was held at the end of February, but a video of the presentation was just released (starting when he shows the videos):

In the three video examples, you can see pedestrians emerging from the sides, out of the field of view, and Teslas vehicles braking just in time.

Tesla is able to capture and save those videos, thanks to its integrated TeslaCam dashcam feature.

Karpathy says:

This car might not even have been on the Autopilot, but we continuously monitor the environment around us. We saw that there was a person in front and we slammed on the brake.

The engineer added that Tesla is seeing a lot of those events being prevented by its system:

We see a lot of these tens to hundreds of these per day where we are actually avoiding a collision and not all of them are true positive, but a good fraction of them are.

In the rest of the presentation, Karpathy explains how Tesla is applying machine learning to its system in order to improve it enough to lead to a fully self-driving system.

I think its important to bring attention to these examples considering if an accident happens on Autopilot, it gathers so much attention from the media.

Lets see how many of them run with this story.

But I get it. People love crashes a lot more than a near-miss.

On another note, I really like how Karpathy communicates Teslas self-driving effort. His presentations are always super clear and informative, even for people who are not super experienced in machine learning.

In order for TeslaCam and Sentry Mode to work on a Tesla, you need a few accessories. We recommendJedas Model 3 USB hub(now also available for Model Y) to be able to still use the other plugs and hide your Sentry Mode drive. For the drive, Im now usinga Samsung portable SSD, which you need to format, but it gives you a ton of capacity, and it can be easily hidden in the Jeda hub.

What do you think? Let us know in the comment section below.

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Tesla releases impressive videos of cars avoiding running over pedestrians - Electrek

Microsoft has revealed how it's applying machine learning to the challenge of correctly identifying which bug reports are actually security-related.

Its goal is to correctly identify security bugs at scale using a machine-learning model to analyze just the label of bug reports.

According to Microsoft, its 47,000 developers generate about 30,000 bugs a month, but only some of the flaws have security implications that need to be addressed during the development cycle.

Microsoft says its machine-learning model correctly distinguishes between security and non-security bugs 99% of the time. It can also accurately identify critical security bugs 97% of the time.

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The model allows Microsoft to label and prioritize bugs without necessarily throwing more human resources at the challenge. Fortunately for Microsoft, it has a trove of 13 million work items and bugs it's collected since 2001 to train its machine-learning model on.

Microsoft used a supervised learning approach to teach a machine-learning model how to classify data from pre-labeled data and then used that model to label data that wasn't already classified.

Importantly, the classifier is able to classify bug reports just from the title of the bug report, allowing it to get around the problem of handling sensitive information within bug reports such as passwords or personal information.

"We train classifiers for the identification of security bug reports (SBRs) based solely on the title of the reports," explain Mayana Pereira, a Microsoft data scientist, and Scott Christiansen from Microsoft's Customer Security and Trust division in a new paper titled Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data.

"To the best of our knowledge this is the first work to do so. Previous works either used the complete bug report or enhanced the bug report with additional complementary features," they write.

"Classifying bugs based solely on the tile is particularly relevant when the complete bug reports cannot be made available due to privacy concerns. For example, it is notorious the case of bug reports that contain passwords and other sensitive data."

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Microsoft still relies on security experts who are involved in training, retraining, and evaluating the model, as well as approving training data that its data scientists fed into the machine-learning model.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99% of the time. The model is also 97% accurate at labeling critical and non-critical security bugs. This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited," Pereira and Christiansen said in a blogpost.

Microsoft plans to share its methodology on GitHub in the coming months.

Read more:
Microsoft: Our AI can spot security flaws from just the titles of developers' bug reports - ZDNet