Half of enterprises have adopted machine learning (ML) technologies as part of their enterprise business. The rest are exploring it. Clearly, the age of machine learning is upon us.

Nowhere is this more intriguing than in the office of finance, which is where every organizations financial and operational data comes together. More than merely reporting what has happened, modern finance organizations wield the latest technologies to help their businesses anticipate what will happen.

One of those technologies is ML, which leverages the advantages of automation, scalable cloud computing and data analytics to generate predictions based on historical and real-time data. Over time, you can train your ML engine to improve the accuracy of its predictions by feeding it more data (known as training data). Your ML engine grows even more intelligent through a built-in feedback loop that further teaches the platform by choosing to act (or not) on its predictions.

Predictions Versus Judgment And Why It Matters

Machines are very good at automating and accelerating the act of predicting. ML makes them even better at it. But judgment is very much a human strength, and its likely to remain so for some time. We can program machines to make limited judgments based on a preprogrammed set of variables and tolerances. If you have assisted driving features on your car, then youre already seeing this in action. These systems are trained to detect potential problems and then take specific actions based on that data.

But its important to recognize that these systems are designed to operate in relatively contained, discrete scenarios: keeping your automobile in its lane or braking when your car spots an object in your blind spot. For now, at least, they lack the contextual awareness required to make the countless decisions necessary to safely navigate your way.

For that, you need people.

In a larger business context, situational awareness helps us weigh factors that may not have been ingested by the ML engine. We know to question a prediction or proposed action that doesnt fit with our companys values or culture. The numbers might add up, but the action doesnt. We need people to make that call. A well-designed finance platform will leave room for you to make those calls, because in a world awash in data, even the best ML engines can be fooled by spurious data and false correlations. Thats why ML complements, rather than replaces, humans.

Is ML A DIY Project?

Ive overseen the development and implementation of ML at two companies the first to spot potentially fraudulent health insurance claims, and the other to model accurate forecasts and develop insightful what-if scenarios. Ive learned a lot from the experience.

But my experience may not look like yours, at least not in the details. As a technologist, I was responsible for bringing ML-powered solutions to life working with development teams to incorporate ML into products for our customers. And, theres every chance that ML will enter your environment via a SaaS (software-as-a-service) financial management or planning platform.

If youre a SaaS platform customer, the actual implementation of ML in your finance environment may be relatively transparent a built-in algorithm that drives powerful next-level features, intuits business drivers and helps support decision making (at least, thats how it should work). But since every ML engine is so dependent on data, and on the decisions you make around that data, you still must address some considerations.

Here are three important ones:

1. Understand where your data is coming from. Your ML predictions will only be as relevant as the data you use to train them. So one of the first steps is to decide what data youll want to input into the system. Theres general ledger (GL) and operational data, of course. But how much historical data is enough? What other sources do you want to tap? HCM? CRM? Do those platforms integrate with your ML-driven finance management or planning platform? Sit down with your IT team to craft a data ingestion strategy that will set you up for success.

2. Appreciate the cost of anomalies. No system is perfect, and occasionally yours will output outlier data that can skew your predictions. Understanding and acknowledging what these anomalies can cost your business is critical. In fact, one of the first uses we defined for ML in business planning purposes was to detect anomalies that could unwittingly put decision-makers on the wrong track. We designed this feature to flag outliers so managers can determine for themselves if they want to accept or disregard them.

3. Acknowledge and avoid bias. This is a big one. Whether we like to admit it or not, bias of all kinds affects much of our decision-making process, and it can threaten the success of your use of ML. Say you want your workforce planning system to model the ideal FP&A hires over the next eight quarters. One reasonable approach is to pick your highest-performing talent, define their key characteristics, and model your future hires after them. But if the previous managers tended to hire menwhether they were high performers or not youll be left with a skewed ingest data sampling that is unwittingly tainted by historical bias.

Harnessing the promise and power of machine learning is an exciting prospect for finance executives. Before long, planning systems will operate much like a navigation system for finance teams a kind of Waze for business. The business specifies its goals, where it would like to go, and the planning system will analyze all available data about past and current business performance, intuit the most important drivers, and offer different potential scenarios along with their relative pros and cons.

Think of ML as a way to make better, smarter use of data at a time when the way forward is increasingly uncertain. For businesses seeking agility, ML offers a way for them to find their true north in the office of finance.

Go here to see the original:
Bringing Machine Learning To Finance? Don't Overlook These Three Key Considerations - Forbes

Throughout the tech community, artificial intelligence has become a blanket term often used to describe any computing process that requires little human input. Tasks like routine database functions, scheduled system scans, and software that adds automation to repetitive actions are regularly referred to as AI.

In truth, AI can play a part in these processes, but there are some major differences between basic machine learning and true AI.

It is vital to consider this distinction when reading blogs and articles that try to outline the key aspects of AI and present an objective analysis of the shortcomings inherent to certain types of AI.

MixMode has made a name for itself as an AI-Powered network traffic analysis leader with the most powerful and advanced AI in the cybersecurity industry, and want to share a few considerations to keep in mind when researching products claiming to use AI and ML.

Not all artificial intelligence is created equal.

Advances in machine learning over the past several years, enhancing processes like facial recognition technologies and revolutionizing the self-driving car industry. These supervised learning AI applications are remarkable and signal a societal shift in how humans interact with technology.

However, supervised learning is limited in its ability to handle complex, sprawling tasks like discovering the threats lurking on an organizations network. Supervised AI can only locate specific threats it has seen or labeled before. Unsupervised learning, on the other hand, never ceases in its search for network anomalies.

Supervised learning relies on labeling to understand information. Once a SecOps professional has labeled data, supervised learning can recognize it and respond according to set parameters. A supervised learning platform might automate a message alerting the security team to a concerning data point. However, it cannot label data on its own.

These limitations would be sufficient for securing networks if SecOps teams knew exactly what to tell supervised learning platforms to find. The reality is that cybersecurity doesnt work that way. Bad actors are always a few steps ahead of the game, coming up with new methods of attack all the time.

Unsupervised AI to the rescue.

No matter what tactic a hacker uses, unsupervised machine learning AI seeks out patterns outside the network norm. SecOps teams can immediately focus on issues as they arise and even swat down attacks before they cause damage or lead to data loss.

When it comes to chatter within the VC and Startup community about startups claiming to have a foundation in AI and deep learning (and their opportunity for success in the market), there are four market claims that MixMode CTO and Chief Scientist, Igor Mezic, would like to address.

Mezic developed the patented unsupervised AI that drives the MixMode platform and shares his thoughts here:

The computational cost related to training can quickly offset the potential benefits of deep learning. Mezic explains that this cost is a concern only when deep learning is used exclusively to reach customer data. Mezic developed the MixMode platform as a layered approach an unsupervised, or semi-supervised architecture that yields efficiency in computation, widening profit margins.

Additionally, Mezic says, MixMode can train AI via an efficient seven-day initial period. (Check out our most recent video on Network Baselines here.) From there, the AI keeps learning in an unsupervised manner.

Mezic stresses that the MixModes semi-supervised algorithms require less human touch, as well, further driving down expenses. MixMode requires little labeling and depends on customer interactions with the AI rather than internal MixMode resources, he explains.

Tech differentiation is difficult to achieve in the cluttered AI landscape. Mezic points to MixModes Third Wave class of AI algorithms that are not commoditized. Mezic says, the MixMode algorithm has led to several implementation patents and substantial additional network effects that enhance defensive IP moats.

Some claim that each organization on the customer side might have different data and associated requirements. However, enterprise networks are similar in the type of data they produce. Thus, Mezic argues, a single algorithm can be applied to all such data without substantial modifications on the ingest side.

Many machine learning startups are service-oriented. However, Mezic makes a distinction between the norm and what MixMode brings to the table.

We have structured the AI here to apply to all the types of data that occurs in our narrow, specifically defined problem, he explains. This level of specificity provides much more robust network protection over services that approach data handling generically and only according to a specific set of requirements.

Some industry experts claim large organizations have inherent data and process inefficiencies, which make them ideal targets for meaningful machine learning benefits but that the process of adopting machine learning AI is too arduous and time-consuming. However, Mezic says the security data in many large organizations is well-structured and organized, which contributes to a streamlined system set-up.

In the end, while startup industry experts make valid points about the current state of AI-enhanced services and software, these arguments must be evaluated with an overarching understanding that AI can be a limiting term. MixModes unstructured machine-learning AI is distinct, and far more powerful, than garden-variety, supervised AI offerings.

Learn more about how MixMode is helping organizations detect, analyze, visualize, investigate, and respond to threats in real-time.

What the Clearview AI Breach Tells Us About Cybersecurity Today

The Big Switch: A Lack of Employable Security Professionals Causes Companies to Make the Switch to AI

New Video: Does MixMode work in the cloud, on premise, or in hybrid environments?

IDC Report: MixMode An Unsupervised AI-Driven Network Traffic Analysis Platform

MixMode Raises $4 Million in Series A Round Led by Entrada Ventures

In Case You Missed It: MixMode Whitepapers & Case Studies

How a Massive Shift to Working From Home Leaves an Enterprises Cybersecurity Vulnerable

In Case You Missed It: MixMode Integrations of 2020

View original post here:
Not All AI and ML is Created Equal - Security Boulevard

The origin of machine and deep learning algorithms, which increasingly affect almost all aspects of our life, is the learning mechanism of synaptic (weight) strengths connecting neurons in our brain. Attempting to imitate these brain functions, researchers bridged between neuroscience and artificial intelligence over half a century ago. However, since then experimental neuroscience has not directly advanced the field of machine learning and both disciplines neuroscience and machine learning seem to have developed independently.

In an article published today in the journalScientific Reports, researchers reveal that they have successfully rebuilt the bridge between experimental neuroscience and advanced artificial intelligence learning algorithms. Conductingnew types of experimentson neuronal cultures, the researchers were able to demonstrate a new accelerated brain-inspired learning mechanism. When the mechanism was utilized on the artificial task of handwritten digit recognition, for instance, its success rates substantially outperformed commonly-used machine learning algorithms.

To rebuild this bridge, the researchers set out to prove two hypotheses: that the common assumption that learning in the brain is extremely slow might be wrong, and that the dynamics of the brain might include accelerated learning mechanisms.Surprisingly, both hypotheses were proven correct.

A learning step in our brain is believed to typically last tens of minutes or even more, while in a computer it lasts for a nanosecond, or one million times one million faster, said the studys lead author Prof. Ido Kanter, of Bar-Ilan Universitys Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center. Although the brain is extremely slow, its computational capabilities outperform, or are comparable, to typical state-of-the-art artificial intelligence algorithms, added Kanter, who was assisted in the research by Shira Sardi, Dr. Roni Vardi, Yuval Meir, Dr. Amir Goldental, Shiri Hodassman and Yael Tugendfaft.

The teams experiments indicated thatadaptationin our brain is significantly accelerated with training frequency. Learning by observing the same image 10 times in a second is as effective as observing the same image 1,000 times in a month, said Shira Sardi, a main contributor to this work. Repeating the same image speedily enhances adaptation in our brain to seconds rather than tens of minutes. It is possible that learning in our brain is even faster, but beyond our current experimental limitations, added Dr. Roni Vardi, another main contributor to the research. Utilization of this newly-discovered, brain-inspired accelerated learning mechanism substantially outperforms commonly-used machine learning algorithms, such as handwritten digit recognition, especially where small datasets are provided for training.

The reconstructed bridge from experimental neuroscience to machine learning is expected to advance artificial intelligence and especially ultrafast decision making under limited training examples, similar to many circumstances of human decision making, as well as robotic control and network optimization.

Go here to see the original:
Researchers Rebuild the Bridge Between Neuroscience and Artificial Intelligence - Global Health News Wire

Linear regression is probably the first algorithm that one would learn when commencing a career in machine or deep learning because its simple to implement and easy to apply in real-time. This algorithm is widely used in data science and statistical fields to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Several types of regression techniques are available based on the data being used. Although linear regression involves simple mathematical logic, its applications are put into use across different fields in real-time. In this article, well discuss linear regression in brief, along with its applications, and implement it using TensorFlow 2.0.

Regression analysis is used to estimate the relationship between a dependent variable and one or more independent variables. This technique is widely applied to predict the outputs, forecasting the data, analyzing the time series, and finding the causal effect dependencies between the variables. There are several types of regression techniques at hand based on the number of independent variables, the dimensionality of the regression line, and the type of dependent variable. Out of these, the two most popular regression techniques are linear regression and logistic regression.

Researchers use regression to indicate the strength of the impact of multiple independent variables on a dependent variable on different scales. Regression has numerous applications. For example, consider a dataset consisting of weather information recorded over the past few decades. Using that data, we could forecast weather for the next couple of years. Regression is also widely used in organizations and businesses to assess risk and growth based on previously recorded data.

You can find the implementation of regression analysis directly as a deployable code chunk. In modern machine learning frameworks like TensorFlow and PyTorch, in-built libraries are available to directly proceed with the implementation of our desired application.

The goal of linear regression is to identify the best fit line passing through continuous data by employing a specific mathematical criterion. This technique falls under the umbrella of supervised machine learning. Prior to jumping into linear regression, though, we first should understand what supervised learning is all about.

Machine learning is broadly classified into three types; supervised learning, unsupervised learning, and reinforcement learning. This classification is based on the data that we give to the algorithm. In supervised learning, we train the algorithm with both input and output data. Unsupervised learning occurs when theres no output data given to the algorithm and it has to learn the underlying patterns by analyzing the input data. Finally, reinforcement learning involves an agent taking an action in an environment to maximize the reward in a particular situation. It paves the way for choosing the best possible path for an algorithm to traverse. Now, lets look more closely at linear regression itself.

Linear regression assumes that the relationship between the features and the target vector is approximately linear. That is, the effect (also called coefficient, weight, or parameter) of the features on the target vector is constant. Mathematically, linear regression is represented by the equationy = mx + c + .

In this equation, y is our target, x is the data for a single feature, m and c are the coefficients identified by fitting the model, and is the error.

Now, our goal is to tune the values of m and c to establish a good relationship between the input variable x and the output variable y. The variable m in the equation is called variance and is defined as the amount by which the estimate of the target function changes if different training data were used. The variable c represents the bias, the algorithms tendency to consistently learn the wrong things by not taking into account all the information in the data. For the model to be accurate, bias needs to be low. If there are any inconsistencies or missing values in the dataset, bias increases. Hence, we must carry out proper preprocessing of the data before we train the algorithm.

The two main metrics we use to evaluate linear regression models are accuracy and error. For a model to be highly accurate with minimum error, we need to achieve low bias and low variance. We partition the data into training and testing datasets to keep bias in check and ensure accuracy.

Before we build a supervised machine learning model, all we have is data comprising inputs and outputs. To estimate the dependency between them using linear regression, we pick two random values, variance and bias. Thereby, we consider a tuple from the dataset, feed the input values to the equation y = mx + c, and predict the new values. Later, we calculate the loss incurred by the predicted value using a loss function.

The values of m and c are picked randomly, but they must be updated to minimize the error. We thereby consider loss function as a metric to evaluate the model. Our goal is to obtain a line that best reduces the error.

The most common loss function used is mean squared error. It is mathematically represented as

If we dont square the error, the positive and negative points cancel each other out. The static mathematical equations of bias and variance are as follows:

When we train a network to find the ideal variance and bias, different values can yield different errors. Out of all the values, there will be one point where the error value will be minimized, and the parameters corresponding to this value will yield an optimal solution. At this point, gradient descent comes into the picture.

Gradient descent is an optimization algorithm that finds the values of parameters (coefficients) of a function (f) to minimize the cost function (cost). The learning rate defines the rate at which the parameters are updated. It controls the rate at which we would be adjusting the weights of our network with respect to the loss gradient. The lower the value, the slower we travel the downward slope along which the weights get updated at every step.

Both the m and c values are updated as follows:

Once the model is trained and achieves a minimum error, we can fix the values of bias and variance. Ultimately, this is how the best fit line looks like when plotted between the data points:

So far, weve seen the fundamentals of linear regression, and now its time to implement one. We could use several data science and machine learning libraries to directly import linear regression functions or APIs and apply them to the data. In this section, we will build a model with TensorFlow thats based on the math which we talked about in the previous sections. The code is organized as a sequence of steps. You can simultaneously implement these chunks of code in your local machine or in any of the cloud platforms like Paperspace or Google Colab. If its your local machine, make sure to install Python and TensorFlow. If you are using Google Colab Notebooks, TensorFlow is preinstalled. To install any other modules like sklearn or matplotlib, you can use pip. Make sure you add an exclamation (!) symbol as a prefix to the pip command, which allows you to access the terminal from the notebook.

Step 1: Importing the Necessary Modules

Getting started, first and foremost, we need to import all the necessary modules and packages. In Python, we use the import keyword to do this. We can also alias them using the keyword as. For example, to create a TensorFlow variable, we import TensorFlow first, followed by the class tensorflow.Variable(). If we create an alias for TensorFlow as tf, we can create the variable as tf.Variable(). This saves time and makes the code look clean. We then import a few other methods from the __future__ library to help port our code from Python 2 to Python 3. We also import numpy to create a few samples of data. We declare a variable rng with np.random which is later used to initialize random weights and biases.

Step 2: Creating a Random Dataset

The second step is to prepare the data. Here, we use numpy to initialize both the input and output arrays. We also need to make sure that both arrays are the same shape so that every element in the input array would correspond to every other element in the output array. Our goal is to identify the relationship between each corresponding element in the input array and the output array using Linear Regression. Below is the code snippet that we would use to load the input values into variable x and output values into variable y.

Step 3: Setting up the Hyperparameters

Hyperparameters are the core components of any neural network architecture because they ensure accuracy of a model. In the code snippet below, we define learning rate, number of epochs, and display steps. You can also experiment by tweaking the hyperparameters to achieve a greater accuracy.

Step 4: Initializing Weights and Biases

Now that we have our parameters equipped, lets initialize weights and biases with random numerics. We do this using the rng variable that was previously declared. We define two tensorflow variables W and b and set them to random weights and biases, respectively, using the tf.Variable class.

Step 5: Defining Linear Regression and Cost Function

Here comes the essential component of our code! We now define linear regression as a simple function, linear_regression. The function takes input x as a parameter and returns the weighted sum, weights * inputs + bias. This function is later called in the training loop while training the model with data. Further, we define loss as a function called mean_square. This function takes a predicted value that is returned by the linear_regression method and a true value that is picked from the dataset. We then use tf to replicate the math equation discussed above and return the computed value from the function thereupon.

Step 6: Building Optimizers and Gradients

We now define our optimizer as stochastic gradient descent and plug in learning rate as a parameter to it. Next, we define the optimization process as a function, run_optimization, where we calculate the predicted values and the loss that they generate using our linear_regression() and mean_square() functions as defined in the previous step. Thereafter, we compute the gradients and update the weights in the optimization process. This function is invoked in the training loop that well discuss in the upcoming section.

Step 7: Constructing the Training Loop

This marks the end of our training process. We have set all the parameters, declared our models, loss function, and the optimization function. In the training loop, we stack all these and iterate the data for a certain number of epochs. The model gets trained and with every iteration, the weights get updated. Once the total number of iterations is complete, we get the ideal values of W and b.

Lets work through the code chunk below. We write a simple for loop in Python and iterate the data until the total number of epochs is complete. We then run our optimization function by invoking the run_optimization method where the weights get updated using the previously defined SGD rule. We then display the loss and the step number using the print function, along with the metrics.

Step 8: Visualizing Linear Regression

While concluding the code, we visualize the best fit line using matplotlib library.

Linear regression is a powerful statistical technique that can generate insights on consumer behavior, help to understand business better, and comprehend factors influencing profitability. It can also be put to service evaluating trends and forecasting data in a variety of fields. We can use linear regression to solve a few of our day-to-day problems related to supporting decision making, minimizing errors, increasing operational efficiency, discovering new insights, and creating predictive analytics.

In this article, we have reviewed how linear regression works, along with its implementation in TensorFlow 2.0. This method sets the baseline to further explore the various ways of chalking out machine learning algorithms. Now that you have a handle on linear regression and TensorFlow 2.0, you can try experimenting further with a lot other frameworks by considering various datasets to check how each one of those fares.

Vihar Kurama is a machine learning engineer who writes regularly about machine learning and data science.

Expert Contributor Network

Built Ins expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industrys definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.

See the original post:
Linear Regression: Concepts and Applications with TensorFlow 2.0 - Built In

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.

Sign up for the free insideBIGDATAnewsletter.

More here:
Rubber Meets the Road: Reality of AI in Infrastructure Monitoring - insideBIGDATA

SAN DIEGO, April 23, 2020 /PRNewswire/ --The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining officially opened registration for its annual KDD Cup, the organization's signature data science competition. This year's competition features four distinct tracks that welcome participants to tackle challenges in e-commerce, generative adversarial networks, automatic graph representation learning (AutoGraph) and mobility-on-demand (MoD) platforms. Winners will be recognized at KDD 2020, the leading interdisciplinary conference in data science, in San Diego on August 23-27, 2020.

"As one of the first competitions of its kind, the KDD Cup has a long history of solving problems by crowd sourcing participation and has given rise to many other popular competition platforms," said Iryna Skrypnyk, co-chair of KDD Cup 2020 and head of the AI Innovation Lab at Pfizer. "Today, KDD Cup is not only an opportunity for data scientists to build their profiles and connect with leading companies but apply their skillset to emerging areas with machine learning on graphs like knowledge graph or drug design, and growth markets like the rideshare industry."

In 2019, more than 2,800 teams registered for the KDD Cup, representing 39 countries and 230 academic or corporate institutions. KDD Cup competition winners are selected by an entirely automated process. In 2020, the KDD Cup features different types of data science including regular machine learning, automated machine learning and reinforcement learning. The competition tracks include:

In addition to Iryna Skrypnyk, KDD Cup 2020 is co-chaired by Claudia Perlich, senior data scientist at Two Sigma; Jie Tang, professor of Computer Science at Tsinghua University; and Jieping Ye, vice president of research at Didi Chuxing and associate professor of Computer Science at the University of Michigan. For updates on this year's KDD Cup and links to each challenge, please visit:www.kdd.org.

AboutACM SIGKDD:ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining.SIGKDD is ACM's Special Interest Group on Knowledge Discovery and Data Mining.The annual KDD International Conference on Knowledge Discovery and Data Miningis thepremierinterdisciplinary conference for data mining, data science and analytics.

Contact or Follow SIGKDD on:Facebookhttps://www.facebook.com/SIGKDDTwitterhttps://twitter.com/kdd_newsLinkedInhttps://www.linkedin.com/groups/160888/

Read more:
KDD 2020 Invites Top Data Scientists To Compete in 24th Annual KDD Cup - Monterey County Weekly

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.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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

See the article here:
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.

Contact:

ResearchMozMr. Nachiket Ghumare,Tel: +1-518-621-2074USA-Canada Toll Free: 866-997-4948Email:[emailprotected]

Browse More Reports Visit @https://www.mytradeinsight.blogspot.com/

Read the original:
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.

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