As enterprises look to become more agile and move towards a DevOps and continuous testing, the need for microservices has grown manifolds.

Businesses require a next-generation web application firewall (WAF) that enables secure delivery of applications. Software development life cycle (SDLC), is as flexible as the dynamic environment and threat landscape and adapts to the needs of the business. Before considering any solution, make sure it meets the requirements of both development and operations (DevOps) and security teams.

SQL injections, cross-site scripting, access violations, remote file inclusion — running applications in a service mesh architecture don’t eliminate the risk from data leakage or service disruptions. Emerging continuous integration and continuous delivery (CI/CD) technologies disrupt common practices and processes and create new blind spots.

Here are 10 characteristics to look for when considering protection to data and applications in a service mesh architecture.

Native Fit into CI/CD Pipeline

• Kubernetes controlled elasticity — Easily orchestrated, grows and scales application security along with Kubernetes pods, including auto-learned policies and configuration settings.
• Automation at the speed of development — Application programming interfaces (APIs) for integration with common tools for security provisioning of new services and applications, with a local management and reporting interface.
• TLS termination — End-to-end encryption is necessary to secure data integrity and avoid eavesdropping and man-in-the-middle (MITM) attacks. A single TLS termination at the host also eliminates spreading multiple certificates across third parties.
• Minimal footprint — Microservices are all about micro units; thus, the enforcement point in the data plane should be lightweight while the control plane (management, analytics and learning algorithms) is integrated into the environment independently.

Quality of Protection

• Extensive security — Application protection today goes beyond the OWASP Top 10, so a good WAF needs to accurately detect malicious bot activity, secure APIs and mitigate denial-of-service attacks.
• Effective security (zero-day protection) — Negative and positive security models are necessary to protect against known and unknown threats, thus maximizing security and minimizing false positives.
• Adaptive security — Immediate detection of new and modified applications in the CI/CD pipeline isn’t enough and must be followed by automatic generation and optimization of security policies.
• Data leakage prevention — Make sure data that is being shared externally is protected. Credit card and Social Security numbers must be masked, cookies must be encrypted, and scrapers should be misled with fake data.

Supplementary Requirements

Endorsed technology — Multiple firms evaluate technology solutions, including ICSA, NSS, Forrester and Gartner. Don’t take our word for it — check it for yourself.

Comprehensive reporting and analytics — Visibility to both development, security and operations (DevSecOps) and security teams via integration with common tools and platforms like elastic Kibana, Grafana, Prometheus, among others.

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An aim of Data Science Market report is to guide the user to know the market in terms of its definition, market potential, vital trends, and the challenges facing by the market. Step by step study of Data Science market provides an extensive outlook on the market trends from 2019 to 2028 covering crucial information on product demand, Data Science industry segmentation and market abstract in each region. We have given a detail analysis of the vendor landscape to offer you with a full picture of ongoing and future Data Science market competitive scenarios. The report covers Data Science market inforrmation including historical and upcoming trends for supply, prices, competition, trading, and value chain. Moreover, the report also provides a SWOT analysis that comprise the strengths, weaknesses, challenges, opportunities, and threats impacting the overall market.

At first the report introduced the definitions, classifications, Data Science market applications and market outlook; product statement; manufacturing processes; cost frame, raw materials and so on. Then it studied the main region market conditions globally, containing the product price, profit, Data Science capacity, supply, demand, production, market growth rate and forecast to 2028 etc. In the end, the report offers new project SWOT analysis, Data Science market investment feasibility study, and investment return analysis.

If you are a stakeholder in the Data Science Market, this research study will help you understand the growth model. Click to get a Free Brochure report in PDF (including ToC, Tables and Figures)

The Prime Manufacturers Covered In This Report Are: Apteryx Inc, Rapid Miner Inc, SAS Institute Inc, IBM Corporation, Datalink SAS, Microsoft Corporation, SAP SE, Math Works Inc and Fair Isaac Corporation (FICO)

Global Data Science Market Segmentation, 2019-2028:

By type: Solutions, Services. By end user: Banking and Financial Institutions (BFSI), Telecommunication, Transportation and Logistics, Healthcare, Manufacturing

On the basis of region, the Data Science market is segmented into Europe, Japan, Southeast Asia, United States, China, South America, South Africa, India and the rest of the world.

The future of the industries is projected on the basis of the ongoing scenario, profit, and Data Science market growth opportunities. Distinct graphical presentation methods are used to demonstrate the facts. Further, we discuss some internal and external factors that drive or restraint the Data Science market. The study is a thorough mixture of qualitative and quantitative data including Data Science market size, revenue, and volume (if applicable) by vital segments. It also scrutinizes the performance of the Data Science market key players operating in the industry including their company profile, corporate summary, financial review. The report examines market segmentation based upon the different segments like type, application, end user and region. In addition foremost regions featuring ‘North America, Asia-Pacific, United Kingdom, Europe, Central & South America, Middle East & Africa.’

Any Query? Fill Free To Inquire Here. We’ll Put You On The Right Path:

The main features are covered in the Global Data Science Market 2019 report:

– The Data Science market report comprises the latest mechanical enhancements and latest releases to engage our consumers to produce, settle on instructed business decisions, and build their future estimated achievements.

– The Data Science market report further focuses more on ongoing business and progressions, future methodology changes, and open entryways for the worldwide Data Science market.

– The investment return study, SWOT analysis, and feasibility study are also used for data study.

Key questions include:

1. What will the outstanding growth rate and also Data Science industry size globally by 2028?

2. What will be the crucial elements driving the Data Science market?

3. What will be the impact of existing and new emerging Data Science market trends 2019-2028?

4. What would be the future Data Science market behavior forecast 2028 with trends, challenges, opportunities and drivers, challenges for development?

5. Who are the leading vendors of the market?

6. Which would be Data Science industry opportunities and dangers faced with most vendors in the market?

7. What are the parameters that affecting the Data Science market share?

8. What will be the outcomes of Data Science market SWOT five forces study?

The post Data Science Market New Research Report| Microsoft Corporation, IBM Corporation, SAS Institute Inc appeared first on Artificial Intelligence.

The combination of analytics and video is helping coaches to hone player performance and the sport is keen to push innovative use of technology in all areas of the game.

Big data is changing how tennis stars train and play; but the key to success is taking all that information and turning it into something players can use to win. 

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Craig O’Shannessy, official strategy analyst for both the ATP Tour and all-time great Novak Djokovic, says that the smart use of data when preparing can have a significant impact on a match.

O’Shannessy explains to ZDNet at the ATP Tour Finals in London how he uses a range of tools to give Djokovic that data-led advantage. These tools include the Infosys Tennis Platform, which is being used for the first time in 2019 across the ATP Tour, which is the worldwide top-tier tennis tour for men organised by the Association of Tennis Professionals.

SEE: 60 ways to get the most value from your big data initiatives (free PDF)    

The platform includes a portal that gives players and coaches access to advanced analytics and match video. This portal uses artificial intelligence and machine learning to match big data with video of game-changing points. Players and coaches have access to more than 100 filters in over 1,000 combinations to analyse performance, and the potential strengths and weaknesses of opponents.

O’Shannessy says the ability to use a data-led portal to create a custom playlist of video clips is “a very big deal”. Being able to easily and quickly highlight the key points in as little as 20 minutes after a match is a potential competitive differentiator.

“When I first started working with Novak, at the 2017 Australian Open, I sat down with him and said, ‘Listen, I do a lot of different things, how can I best help you?’ The first thing he pointed to was video, the ability to see himself play and the ability to see the best patterns of play,” he says.

O’Shannessy also uses tools from Tennis Analytics, a big-data specialist that provides match and technical analysis services to top professionals. He says the smart use of video and analytics technology from a coaching perspective is all about distilling information.

“The worst thing that I could do is show Novak the match chronologically – that’s not the way to do it in today’s game,” he says. “Success is about taking big data and simplifying it and distilling it, so that a player can use it under five minutes with you – whether it’s the video, the numbers or the data tables. It’s about finding the 10 or 15 points that matter the most and explaining that these are the patterns of play that that you want to repeat in upcoming games to win those matches.”

O’Shannessy paints a picture of a sport that is using as much data as possible to help hone player performance. He says the use of technology within tennis has become more entrenched during the past three decades – and he expects the sport to pioneer more data-led developments in the future.

“We need to remember where our sport has come from in order to know also where it’s going to,” he says. “Before 1991, we weren’t counting anything – we didn’t have analytics. Now, I can use technology to focus on player performance. I want to put numbers to everything and that’s the route that I want the technology to continue to take.”

Smart preparation starts a long way from the tennis court. “Every guy that Novak plays, I have video and analytics on, so there are no more surprises coming from the other side of the court,” he says. “So, is it worth one point a match? Absolutely. Is it worth two? For sure it’s worth two points. I’d say in a three-set match, it’s worth more in the range of five to 10 points.”

Other experts also note the willingness of tennis to embrace technology. Chris Brauer, director of innovation in the Institute of Management Studies at Goldsmiths, University of London, says that, while a common theme in his research is a lack of consensus when it comes to change, he says the people running tennis – such as O’Shannessy – are open to innovation.

“He’s able to ask the question of the system and create a set of insights that allows him to be clear about the reality of what’s actually happening on court,” he says. “That’s the difference between a data-driven insight approach to tennis and one that’s based exclusively on intuition or experience – which is hugely valuable, but which on its own can lead us as human beings towards a lot of false positives.”

ATP umpire Ali Nili is another senior figure in the game who believes tennis has taken a pioneering approach to technology. He gives the example of the early implementation of live, online scoring in the sport. Nili also refers to the introduction Hawk-Eye technology 15 years ago, which provides automated line-calling assistance to umpires.

SEE: Wimbledon: How AI, chatbots and big data help serve up a winning experience

While other high-profile sports like football have been slow to use similar systems, tennis has been one of the trailblazing adopters. The more recent and heavy use of data and video to help coaches hone player performance is just the latest in a long line of innovations.

“You see that a lot of major sports are now just trying to adopt Hawk-Eye and use technology,” says Nili. “We’ve been good at that and we hopefully continue to be good at exploring innovations and trying things.”

The experts at the ATP Tour Finals event in London suggested the next area of development in tennis is likely to focus on connectivity, particularly in terms of IoT sensors on balls and on players. These sensors would provide even more data to players, coaches and other potentially interested parties, including broadcasters and fans.

Some sports are already pioneering developments in this area, including golf’s European Tour, as ZDNet found out recently. So, for once, tennis might have to play catch-up when it comes to introducing new technology. However, the sport’s history of innovation leaves Goldsmith’s Brauer thinking that the future of tennis will be closely intertwined with innovation.

“Hardly a day goes by when a new technology isn’t being released that helps to inform this process,” he says. “The coaches and the community of players and stakeholders at the highest levels of the game are entirely engaged. As a result, the game has already been fundamentally transformed by the opportunities that insight and analytics provides – and there’s more to come.”

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There’s been a lot of media attention in recent years about how artificial intelligence (AI) and machine learning (ML) are going to change the world—how they’re going to create new and interesting applications in fields as diverse as education, law, health care and transportation. This may happen. But if I had to bet on a use case where AI and ML will create a tangible, lasting impact, I’m putting my chips on DevOps.

DevOps is all about automation of tasks. Its focus is on automating and monitoring every step of the software delivery process, ensuring that work gets done quickly and frequently. While it doesn’t eliminate human tasks—far from it—it does encourage enterprises to set up repeatable processes that promote efficiency and reduce variability.

AI and ML are perfect fits for a DevOps culture. They can process vast amounts of information and help perform menial tasks, freeing the IT staff to do more targeted work. They can learn patterns, anticipate problems and suggest solutions. If DevOps’ goal is to unify development and operations, AI and ML can smooth out some of the tensions that have divided the two disciplines in the past.

Here are six ways AI and ML can and will change DevOps for the better.

Promoting Feedback on Performance

One of the key tenets of DevOps is the use of continuous feedback loops at every stage of the process. This includes using monitoring tools to provide feedback on the operational performance of running applications. This is one area today where ML is impacting DevOps already. Monitoring platforms gather massive amounts of data in the form of performance metrics, log files and other types. Advanced monitoring platforms are applying machine learning to these datasets to proactively identify problems very early and make recommendations. These recommendations go to the DevOps team members so that they can ensure that the application service remains viable. Machine learning is enhancing the continuous feedback loops that are critical to DevOps.

Enabling Communication

Communication and feedback is always one of the biggest challenges when organizations move to a DevOps methodology. Human interaction is vital, but with so much information flowing through the system, teams need to set up a wider variety of channels to set and revise workflows on the fly. Using automation technology, chatbots and other systems initiated by AI, these communications channels can become more streamlined and more proactive.

Correlate Data Across Platforms and Tools

To operate efficiently, DevOps teams need to simplify tasks. This is getting more difficult as environments get more complex. Start with monitoring tools: Teams tend to use multiple tools that monitor an application’s health and performance in different ways. Machine learning applications can absorb these data streams and find correlations, giving the team a more holistic view of the application’s overall health.

Manage a Flurry of Alerts

Since DevOps encourages teams to “fail but fail fast,” it’s critical to have an alert system that spots a flaw quickly. This tends to create scenarios where alerts are coming fast and furious, all labeled with the same severity, making it difficult for teams to react. Machine learning applications can help teams prioritize their responses based on factors such as past behavior, the magnitude of the current alert and the source that specific alerts are coming from. Humans can set up rules, but machines can help manage these types of situations when too much data overwhelms the system.

Evaluating Past Performance

AI/ML also has the potential to help developers during the application creation process. By examining the success of past applications in terms of build/compile success, successful testing completion and operational performance, machine learning algorithms could make recommendations to developers proactively based on the code they are writing or the application that they are building. The AI engine could direct the developer in how to build the most efficient and highest-quality application.

Software Testing

In the future, we could see AI/ML applied to other stages of the software development life cycle to provide enhancements to a DevOps methodology or approach. One area where this may happen could be in the area of software testing. Unit tests, regression tests, functional tests and user acceptance tests all produce large amounts of data in the form of test results. Applying AI or machine  learning algorithms to these test results could identify patterns of poor coding practices that result in too many errors caught by the tests. This information could then inform the development teams so that they can become more efficient in the future.

Similarly, leveraging historical data, AI/ML could be used to fine-tune deployment strategies as applications are moved from Dev to Test to Production environments.

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