‘Big data’ may not pop up in news feeds as often as it did five or ten years ago, but not because it’s become any less important. On the contrary; today, just about everyone knows that gathering data, analysing it for new insights, and then applying those insights are all vital processes for any business that can afford them.

There are many upfront costs associated with big data, whether you’re acquiring and organising data, managing its storage, or hiring data scientists. Thankfully, for small- and mid-size firms, the rise of  Big Data as a Service (or BDaaS) has made those processes available to more companies than ever before.

As major industry players have gained more experience in managing and analysing large quantities of data, they’ve begun to offer BDaaS to smaller firms. For those businesses, being able to access what you need on a pay-as-you go model is attractive.

How ‘New’ is Big Data?

Big data sets aren’t new, and neither is the desire to analyse them. Back in the 1960s and 1970s, the first data centres were already getting up and running, and the development of the relational database was underway. By the early 2000s, it was quickly becoming clear that users of websites like Facebook and other online services were generating much more data than ever before; plus, that data was clearly valuable.

Hadoop, an open-source framework designed specifically for large data sets helped big data to take off starting around 2005. Spark, another such framework, has gained popularity more recently. In the more than 15 years since, the amount of data coming in has only increased, and the potential for what can be done with it is increasing as well.

The Three (or More) Vs of Big Data

One of the concepts that has come to dominate discussions of big data is the so-called ‘three Vs’: volume, velocity and variety. Though the idea is commonplace now, it comes from just before big data took off, thanks to the emergence of technologies like Hadoop.

Analyst Doug Laney, beginning work in the year 2000 and publishing a note in early 2001, articulated a concept that has shaped how researchers understand big data ever since. Looking at trends in e-commerce in 2000-1, Laney wrote about what he called ‘the three Vs’:

1. Volume: Even twenty-odd years ago, Laney noticed that businesses were beginning to gather more and more data, and that they were ‘reluctant to discard it’ as they had begun to understand its potential value. That trend has only increased as storage has become cheaper
2. Velocity: The other immediate issue in e-commerce at the time was the increasing speed at which all this new data was coming in. This hasn’t changed, and things have only sped up in the decades since
3. Variety: Another emerging issue was the number of kinds of data that businesses were beginning to collect. Since then, companies have developed preferred practices for reconciling the issues created by ‘incompatible data formats, non-aligned data structures, and inconsistent data semantics’ that Laney described

Since then, researchers have proposed many other Vs. Two that have gained particular currency are:

1. Value: Companies are able to glean value from their data, constantly going over it as it comes in to help them with product development, or to find ways to be more efficient.
2. Veracity: For the value to be real, the data itself must be reliable. If you can’t believe what you’re analysing, then there isn’t much use in spending resources on doing so.

Big Data Today

Today, the challenge posed by the three Vs remains, as volume, velocity and variety continue to increase:

The amount of data collected today dwarfs that which was collected twenty years ago, and it varies greatly from company to company. Some firms might be looking at terabytes of data, while others might have many petabytes to store and analyse.

The data is being collected faster than ever before, as more smart devices deliver data in real time or at speeds close to it. While data was often of mixed types in the past, it used to be (mostly) structured. Now, un- and semi-structured data types, ranging from audio and video files to twitter feeds all require different processing approaches. BDaaS helps to make solutions to these problems available, and affordable, to more and more companies.

What Can Big Data Do For Your Business?

Big data is powerful. Even small changes made by businesses and governments in response to insights garnered from big data can make a big difference in people’s lives.

To inspire your BDaaS brainstorming, here are a few ways in which organisations have used big data that may help with your needs:

• Developing products: Using customer data, companies have built predictive models that can help them to understand and anticipate customer demand. This could help you to ensure that any products in development are more likely to succeed
• Maintenance: If your company has a large fleet, there’s an opportunity to use the data that you already have to predict which vehicles will need maintenance and when. Preventing potential equipment breakdowns can save you time and money in obvious ways
• Improving the customer’s experience: Finding and retaining customers isn’t getting any easier, which makes understanding every aspect of the customer experience crucial. With big data, you can gain access to a wealth of information about your customers, and then spend resources in a targeted way to perfect their experience
• Preventing fraud: Today’s fraudsters are sophisticated, and your approach to taking them on must be likewise. Using big data, you can identify patterns that might indicate fraud, and adapt to the strategies used by those seeking to take advantage
• Machine learning: Big data is one of the key components powering machine learning. The models that enable it are only possible because of the tools developed to manage, analyse, and utilise big data sets
• Efficiency: This might be both the simplest and most important application of big data for your business. Whether you’re analysing production data or customer feedback, improving your operational efficiency will have huge an impact on your bottom line

What is Big Data as a Service (BDaaS)?

As you probably know, there is a whole range of resource intensive technology solutions available on the “as a Service” model. BDaaS itself isn’t new either—it was being called the ‘next big thing’ five years ago, but its growth runs parallel to the increasing importance of big data.

Today, companies need access to data services regardless of their size, and BDaaS makes it possible for smaller businesses to access a range of services while paying only for what they use. In other words, while you might not have the money to build a data centre and hire data scientists, you could probably still use data storage and analysis. This is where a BDaaS provider can help you.

One service that some BDaaS providers offer is access to proprietary data sets. Depending on your industry, this could be especially helpful if you don’t have the time or resources to gather and analyse a given set of data yourself.

If you’re considering a BDaaS provider, you’ll find plenty of options offering you a Hadoop-based solution, or another framework, along with varying suites of analytical tools and other add-ons. Depending on what you need, you can pick and choose among the options that make the most sense for you. Here are some things to consider when choosing which BDaaS provider is best for your business:

What are others in your industry using? If your provider has experience working with clients like you, they’ll likely be better able to meet your needs and answer your questions.

Does it meet your organisation’s specific needs? If you’ve got a simple data set, you might be able to do much of what you need on your own by hiring one or two people. If, on the other hand, you’ve got a large and unstructured dataset, and transferring it to a third-party doesn’t violate any company policies, a BDaaS provider could be the right call.

What kind of feedback and analysis tools does it provide? You should look for tools and services that allow you to see what’s happening in real time, or as close to it as possible. Otherwise, you may lose some of the benefits of BDaaS that you’re hoping for.

What level of service do you need? While most services can mix and match, make sure that you’ve got a good idea of what you need before you choose a provider. If you have experience analysing data and you simply need storage, maybe a self-service plan is right for you. If, on the other hand, this is the first time that you’ll be doing data collection and analysis, you might want a managed plan.

What Does The Future Hold For Big Data And BDaaS?

Big data and BDaaS aren’t going anywhere, and according to many working in the sector, the field is actually in its early stages. Writing in Forbes, Bernard Marr put forward four potential trends in BDaaS, and big data in general, for the rest of 2021.

More And More Sophisticated Automation Due To AI

As artificial intelligence increases in complexity, due in no small part to machine learning made possible by big data, new and more complex forms of automation become possible. Machines that are able to learn for themselves by analysing data sets can automate all kinds of processes that previously took time and resources away from your employees.

For example, you could use AI to look at customer data that could work to predict how likely a new customer is to become a regular one, based not only on how much they’ve spent, but on their web browsing and demographic data. Plus, it can learn and adjust its predictions based on whether or not those predictions are accurate.

New Ways To Look At And Understand Data

Finding new ways to interpret and analyse data is always a focus in data science. Marr calls data visualisation the ‘final mile’ of analytics, and points to the emphasis on human judgment. The problem is that people miss valuable insights all the time. Recent breakthroughs in natural language processing and visualisation techniques like extended reality (XR) a blanket term for virtual and augmented reality (VR and AR) will, according to Marr, make for new and better ways to both understand and look at data.

New Emphasis on Hybrid Cloud Computing

One of the benefits of BDaaS is that it can be adapted to your specific needs. Cloud computing has played a major role in the development of big data in general, and BDaaS specifically. Marr sees a shift coming in 2021 towards hybrid cloud computing, where some infrastructure is on-site, and other aspects are handled by third-party service providers. Privacy concerns aren’t going to decrease in the future, so data storage solutions that guarantee more control over data, for companies and consumers alike, may well grow in popularity.

More Emphasis on ‘DataOps’

Borrowing from the idea of ‘DevOps’ in software development, Marr predicts more emphasis in the coming year on employees whose jobs focus on BDaaS tools. The increasing popularity of these services makes it more and more important that you can get access to what you’re paying for, and that it’s working as expected.

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he world’s most valuable resource is no longer oil.

It’s data.

Smartphones and the internet have made data the currency of our times. Nearly every activity, whether it’s going for a run or watching TV, creates a digital trace to be tracked and analyzed.

Data’s influence on the hospitality industry is nothing new. But hoteliers still are grappling with the concept of big data, referring to the large volume of data that inundates businesses on a day-to-day basis. After all, it’s not the amount of data that’s important; it’s what you do with it that matters.

Tapping the power of big data can yield significant dividends for hoteliers. Here are four key areas that can benefit from it:

Hotel Revenue Management

Hotels can use big data to assist with revenue management strategies; it allows operators to more accurately anticipate levels of demand for rooms. Key performance metrics include data such as past occupancy rates and current bookings. Also, big data can be used to predict demand and future trends, which are important for executing revenue management strategies successfully. Evaluating factors such as weather, school holidays, etc., can help anticipate the next “big rush,” allowing hoteliers to staff accordingly, ensure exceptional guest experience, and operate at optimal efficiency.

Targeted Marketing

Big data can help hoteliers determine the optimal marketing spend across various channels to best reach different segments. It can provide a better understanding of different segments and their guests, which leads to increased engagement and greater exposure for hotel properties and their products. For example, if a hotel can determine that a specific target group books its reservations using social media channels, then marketing efforts can be focused on such platforms. Hotel data also is useful for uncovering marketing pitfalls. For example, when potential guests abandon the booking website, big data can help identify and fix the problem.

In a similar fashion, big data can identify characteristics that distinguish hotel guests – from business travelers to families going on annual vacations – and their content preferences. With knowledge about their behavior and stay expectations, marketing content can be tailored to result in greater engagement and higher conversion rates for promotions.

Guest Experience

Delivering exceptional experiences is essential to success. Guests have high expectations and demand personalized experiences. Loyalty to a brand/hotel can only be won by providing such service consistently. By creating robust guest profiles, hoteliers can achieve personalization and recognize guests as valued consumers. Analyzing guests’ feedback on social media also is an invaluable means to improve hotel operations and service. It’s a great avenue to address guests’ concerns and potential changes to services, whether it’s adding new ones, adjusting existing offerings, or discontinuing others.

Competitive Analysis

Hotel data can be used to get a clearer picture of the competition. Through social media and other platforms, hoteliers can learn about guests’ opinions about their experience at competitor properties. Such insight can help hoteliers capitalize on competitors’ shortcomings or shore up deficiencies at their own properties.

What’s to be gained from big data? Previously unimaginable insights that can change the fortunes of your hotel business. You certainly can’t extract that from oil.

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Its new DSaaS collaboration with AWS promises help with scalable analytics, helping healthcare organizations gain ground with pop health projects by offering insights into social determinants of health.

Change Healthcare on Tuesday announced a new cloud-based service, offered in collaboration with Amazon Web Services, to help health systems and life sciences organizations boost the effectiveness of care plans they design for patients – especially for underserved communities and vulnerable populations.

WHY IT MATTERS
The new DSaaS approach combines de-identified claims data, together with social determinants of health insights, to help offer tailor-made datasets for clients aiming to develop compliant analytics projects at scale, according to Change Healthcare.

The secure cloud service can help organizations with different goals measure the comparative efficacy of different interventions and therapies – all while maintaining focus on the unique impact social determinant factors can have on care and outcomes.

The manual use of regulated health data has traditionally been challenging: slow-going, with many privacy and other compliance risks. Change says that its collaboration AWS will offer agility, scalability and security – with DSaaS pre-integrating data and offering automated monitoring of compliance obligations.

Change Healthcare’s dataset – diagnoses, prescriptions, and SDOH information – is drawn from across the U.S. healthcare system. Each DSaaS instance is dedicated to the specific client, the company says, with the opportunity to add other datasets, analytic tools or methods as the project or use case requires.

THE LARGER TREND
Several high-profile customers are already making use of the DSaaS offering. Duke University School of Medicine has leveraged the cloud service to compare differences in COVID-19 disease progression depending on pre-existing conditions and various interventions for different ethnic and socio-economic subgroups.

“Our work on COVID-19 highlights how comparative effectiveness research needs to better incorporate ethnicity and social determinants of health to truly assess the real impact of therapies and interventions,” said Michael Pencina, vice dean for data science and IT at Duke University School of Medicine, in a statement provided by Change Healthcare.

Other recent initiatives involve Carnegie Mellon University, whose Delphi Research Group has been using the dataset, combined with data from other sources, to create an interactive COVID-19 map that tracks behaviors, treatments and diagnoses. 

And MITRE is using DSaaS to explore how the pandemic has impacted the larger healthcare system – using the dataset to uncover key trends, and gain insights into potential longer-term consequences for the healthcare ecosystem.

ON THE RECORD
“As much as 80% of our health and well-being is affected by social determinants, such as whether someone can access or afford medical care, their level of healthcare literacy, their access to transportation, and their food and housing vulnerabilities,” said Tim Suther, senior vice president of data solutions at Change Healthcare in a statement.

“Traditional comparative research fails to effectively account for these inequities. By integrating data beyond the clinical setting – in a way that supports privacy – we can understand how diverse life circumstances affect treatment efficacy. That understanding is key in improving outcomes and healthcare economics.”

“Providing secure access to comprehensive, linked healthcare datasets will enable life sciences organizations to personalize the patient experiences, support, and enable powerful population-level comparative research to improve precision medicine and personalized care, such as medication adherence, around the world,” added Wilson To, head of worldwide healthcare business development at AWS, in a statement.

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Having worked as a software developer and with software developers for over a decade now, one of the things I have learned to appreciate is just how much developers dislike inefficiency. Anything we can do to automate our jobs and make them faster and easier will inevitably be done. Think back to how much work it was to build and host your own website a decade ago versus now. The manual builds and deploy steps have been gradually replaced with automated builds, testing, and deployments across multiple environments with fantastic scalability. 

As a technology moves along the hype cycle into maturity, frameworks, tooling, and methodologies rise and fall until we begin arriving at the things that truly make technology useful and efficient. Machine Learning (ML) has seen an explosion of development in the last few years and shows no signs of slowing. Just as in other software development areas, machine learning is beginning to find its stride in the development track, making it much more accessible than ever before, thanks to MLaaS.

What is Machine Learning as a Service (MLaaS)?

So, what is machine learning as a service? Simply put, MLaaS is when you use someone else’s tooling and infrastructure to enable machine learning development or deployment, usually at a price. MLaaS is a more specific version of Software as a Service (SaaS). In the olden days, if you wanted network storage, you bought or built a server, put it in a server rack, and attached it to your network. Now you can pay to use someone else’s server and let them handle redundancy, scalability, and maintenance, so you don’t have to.

Using these other servers is where much of the efficiency in as-a-service offerings come from; they help customers accelerate solutions. Economies of scale often make this solution faster to set up, easier to maintain, and generally more cost-effective over time. In the same way, if you wanted to do machine learning development a few years ago, you had to jump through several hoops. First, you needed to hire a machine learning expert. Second, put $2000+ into a high-end GPU-packed Linux box. Third, try to piece together several disparate frameworks and tooling, hoping you didn’t have any conflicting dependencies. Lastly, wrangle your data into some custom format until you could get a model training. While that may still be the right solution in some cases, for many, we have much better options now.

ML Services

Not surprisingly, the most prominent players in the cloud computing industry are also some of the most prominent MLaaS space players. Amazon Web Services, Google Cloud Platform, and Microsoft Azure are updating and releasing new and improved machine learning tooling at a lighting fast pace.

The types of services that these cloud providers offer include:

• Virtual machines for training models
• Data storage
• Data versioning
• Data labeling/ground-truthing tools
• Hosting options for models
• Pre-trained models for deployment such as:
  • Models for fraud detection
  • Models for detecting various objects in images
  • Models for doing sentiment analysis on text
  • Recommendation engines
  • Anomaly detection
• Development environments for data scientists and software developers

On top of these general offerings, we see a surge in particular offerings targeted at use cases in certain industries. One example is Amazon’s Lookout for Equipment. This offering is targeted at the industrial sector, which has generally struggled with adopting machine learning. This industry’s struggle is partly due to the lack of experts available to get companies started and the high cost of entry into the ML space. Specific services like these reduce the need for in-house expertise, lower the barrier to entry, and start at a low cost. AWS has gone so far with this that they offer devices, such as Monitron, that work with their cloud infrastructure to reduce these barriers to entry further. 

Along with the big names in cloud computing, we see very specialized companies entering this space and providing solutions that were hard to imagine 5 years ago. One great example of this is Edge Impulse. They are focused on bringing machine learning to edge devices, which has traditionally been incredibly difficult and required both a high level of expertise in embedded systems and machine learning. With their services, what used to take weeks of development time can be reduced down to days or even hours. 

With these types of technologies, it is no wonder that companies are further embracing machine learning into the future. A recent article in Forbes highlights some of the significant shifts in the industry and points to some of the challenges for ahead companies. 

With everyone scrambling to get a piece of the pie, it can leave companies with a lot of questions, including:

• Should I use a service platform or do the work in-house?
• If I do use a platform, which one is the best?
• Should I use a pre-canned specific solution or do something more general?
• How expensive is all of this?

While we can’t answer every question in this post, let’s look at a few high-level things to consider.

How to Get Started with MLaaS

There are a few high-level trade-offs to be considered.

• Generally, an MLaaS solution improves speed but tends to decrease flexibility in frameworks, versions, or the ability to adapt and tweak models to get the best solution.
• Depending on the amount of training you need to do, sometimes building in-house infrastructure may be a cheaper option.
• While MLaaS solutions tend to improve the speed of getting started, they can also be slower during actual development due to the large amounts of data moving around the cloud.
• Some solutions will promise the world but need to be vetted by someone with some machine learning experience and domain knowledge of your problem. Be wary of silver bullet sales pitches.
• Make sure you are considering the full machine learning process. If the service doesn’t work with the way you collect and store data, that is a problem. The ability to easily deploy a trained model from this service is an important consideration. If you have no way to monitor your model’s performance, that can be a significant issue for your solution’s long-term success.

There are also a few high-level questions to answer to help decide how to move forward. 

How Unique Is the Problem?

Some problems in machine learning are pretty well understood and have solutions to them. If you are looking to find people in an image, implement fraud detection, or recommend products to a user, you can probably find something off the shelf that will help you accelerate your solution quickly. However, if you work in a unique domain, such as optimizing feeding patterns on your grasshopper farm, you may struggle to find a solution that cleanly fits your needs. The more specific the service offering is, the more closely your problem will need to match it. More general services, such as using Amazon SageMaker to create your model from scratch, will take more time and expertise but will ultimately be more flexible.

Is In-House Technical Expertise Available?

If you have a team of data scientists and developers already working on the problem, their expertise may be able to provide better solutions at a lower cost than trying to move to an MLaaS solution. This is particularly true if the problem you are trying to solve has many nuances or needs a lot of flexibility. Often, an in-house expert will be able to quickly assess a service to know if it will work in your particular environment. If you don’t have this expertise, it will likely be wise to engage a third party to evaluate the right solution.

Is There In-House Infrastructure?

If you already have many in-house infrastructures to support data storage, training, and deployment, it is probably worth leveraging that. However, if you want to integrate some of this into other machine learning services, be careful about which tools allow external integration types. This can be a major headache, even if a solution claims that it offers 3rd party integration. Many times, these integrations can be cumbersome, buggy, and fragile. 

Cloud or Edge: Where Should the Model Run?

This question is going to drive a lot of decisions. Generally, running models in the cloud is easier. However, it comes with a lot of limitations that may or may not be an issue. For instance, if you have a model that inspects the quality of a part on a manufacturing line, you may not have enough time for data to be collected, sent up to the cloud, processed, and sent back while still maintaining your cycle time. If this is a safety-critical application, you can’t depend on a wireless connection all the time to get results. While cloud providers are working hard to make sure their services can move into this domain, it may be better to find tooling specifically targeted to your edge application. 

How Sensitive Is the Data or Application?

If you are working with highly sensitive data, this needs to be a significant consideration on how you choose to work with machine learning. Cloud platforms are becoming more and more secure and providing better options for end-to-end security than ever before. However, anytime data moves from one location to another, there is always increased risk. Each service being considered should be carefully scrutinized to know if or how it should be used in your scenario.

Will the Problem Statement Change Significantly in the Future Roadmap?

Rarely do you train up a model that will work perfectly from now into eternity. Inputs change. Business problems change. Customer’s needs change. Tying yourself to a very specific machine learning service might work great today but could be a hindrance down the road. Although we can’t predict the future, having a good roadmap of where you think your product or problem is going can help you make informed decisions today. 

Once you’ve answered some of these questions, you are ready to explore the options that are out there. Keep in mind that there will be trade-offs with any service that you use. Understanding what you are gaining and what you are losing is key to finding the right solution.

Set Yourself Up for Machine Learning Success

To set yourself up for success in machine learning:

• Adopt a fail-fast methodology. What is the bare minimum you can try to see if a particular service will fit your need? Experiment quickly and move on quickly if things aren’t going in the right direction.
• Take advantage of free tier offerings, trials, and demos. Most machine learning service providers want you to buy their products and try to make the barrier to entry lower through low to no-cost trial periods. Try it out. If you don’t like it, try something different.
• Never trust a machine learning sales pitch. Machine learning can often be a black box that feels like magic. It can often be too easy for a sales demo to cherry-pick the right data to make their service look even more magical. Whenever you can, try the product for yourself and look for successful real-world use cases.
• Think about your problem holistically. If you are using multiple models, make sure the service you choose will support all of them. If you need other services like monitoring, data storage, or an API to hit a machine learning endpoint, it is probably better to choose a platform that provides all of these things, so you don’t have to learn more technologies and maintain different accounts.
• Try to understand what you don’t know and don’t be afraid to ask for help. If you don’t know how something works, it is better to understand it sooner rather than later. A few hours with an expert consultant could save you thousands or hundreds of thousands of dollars down the road. Know your limits and approach problems humbly.

Learn from Experience

Based on the trends over the last few years and the projections moving forward, I suspect many more machine learning services will hit the market in 2021 and beyond. Some will last. Some will fail. Navigating through all of them can be a big undertaking. Finding the right solution could be just what your business needs to get to market sooner or the golden ticket that sets you apart from the competition. There are risks, but the market is showing that there is also great reward. Picking the right service or set of services will start you off on the right foot and offer much greater efficiency than trying to do it yourself. 

The post What to Know About Machine Learning as a Service in 2021 appeared first on Artificial Intelligence.

The report contains an overview explaining Data Science and Machine Learning Service Market on a world and regional basis. Global Data Science and Machine Learning Service market report is a definitive source of information and provides the latest market research, evolving consumer trends with actionable information about new players, products, and technologies. Our analysts have statistical data to provide information about the statistical report, including the factors that drive and impede the market growth.

The study is an integrated effort of primary and secondary research. The report provides an overview of the key drivers affecting the generation and growth limitation of Data Science and Machine Learning Service market. In addition, the report also examines competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new products in the world market. The past trends and future prospects presented in this report make it very comprehensible to market analysis. Furthermore, the latest trends, product portfolio, demography, geographic segmentation, and market regulatory framework Data Science and Machine Learning Service were also included in the study.

Description:

• Data Science and Machine Learning Service is the process of manipulating a manufacturer’s product return
• Data Science and Machine Learning Service Market Competitiveness by Major Manufacturers/ Key Player Profile:
  DataScience.com

• ZS
  LatentView Analytics
  Mango Solutions
  Microsoft
  International Business Machine
  Amazon Web Services
  Google
  Bigml
  Fico
  Hewlett-Packard Enterprise Development
  At&T

Market Segment according to type covers:

• Consulting
  Management Solution

Market segment by applications may be broken down into:

• Banking
  Insurance
  Retail
  Media & Entertainment
  Others

Fundamental Highlights

• Primary strategies of key players
• Global elements driving the market
• Rising and advanced markets
• A comprehensive description of the international competitors
• Market kinetics impacting the global market
• Assessment of niche business areas
• Elements compelling or restraining the market growth
• Market share analysis

And More…

The following section also highlights the supply-to-consumption gap. In addition to the above data, the growth rate of Data Science and Machine Learning Service market in 2026 is also explained. Moreover, consumption charts by type and application are also given.

Purpose of Studies:

World Market Report Data Science and Machine Learning Service Industry primarily covers 10 sections in the table as follows: –

• Industry Overview of Data Science and Machine Learning Service covers: – Definition, Provisions, Classification, Characteristics, and Applications
• Data Science and Machine Learning Service Manufacturing Cost & Price Structure Analysis includes: – Raw Material and their Suppliers, Manufacturing Cost Structure Analysis, Sales Price Structure Analysis, Break Even Analysis, and Process Analysis.
• Production Description:- Capacity and Commercial Production Date of Data Science and Machine Learning Service Major Manufacturers in 2018, Distribution of Manufacturing Plants, R&D Status and Technology Source and Analysis of Raw Materials Sources.
• Global Data Science and Machine Learning Service Overall Market Overview includes: – Comprehensive Market Analysis ranging from Production to turnover.
• Data Science and Machine Learning Service Regional Market Analysis contain:-The marketplace is analyzed across 4 regions: North America, Asia-Pacific, Europe, and RoW.
• Global 2015-2020 Data Science and Machine Learning Service Segment Market Analysis (by Type):- Sales and Factors responsible for Sales Growth
• Global 2015-2020 Data Science and Machine Learning Service Segment Market Analysis (by Application) covered:- Application by end-use, Consumer Analysis
• Leading Manufacturers Analysis of Data Science and Machine Learning Service around the globe includes:- Analysis on each Company Profile, Product Picture and Stipulation, Sales, Ex-factory Price, Revenue, Gross Margin Analysis, Business Region Distribution Analysis
• Development Trend of Data Science and Machine Learning Service Market Analysis: – Data Science and Machine Learning Service Market Trend Analysis, Market Size (Volume and Value) projection, Regional Market Trend, Market Trend according to Product Type and Applications.
• Data Science and Machine Learning Service Marketing Type Analysis comprises: – Regional Market, International Market, Home and Host Country Competitors of vital international competitors.

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The world is moving toward microservices-based applications. Service mesh is emerging as one of the main architectures to deploy and manage microservices environments, because of the benefits it brings with advanced traffic management, holistic observability and better security. Microservices communicate with each other through APIs, so securing communications between the individual services is becoming more important than ever and has to be addressed.

Mutual TLS (mTLS) secures communication between microservices in a service mesh. It uses cryptographically secure techniques to mutually authenticate individual microservices and encrypt the traffic between them.

Why mTLS?

According to Google, 90% of internet traffic is encrypted to prevent eavesdropping and man-in-the-middle attacks. Yet many cloud native application deployments today do not have encrypted communications between microservices, based on the weak assumption that traffic inside the cluster is secure and not susceptible to attacks. This is a risky assumption. Not only should communications between microservices be secured, but many regulations (like GDPR and HIPAA) also recommend end-to-end encryption to protect all data in transit.

In this era of zero-trust security, each individual microservice communication (request-response) must be authenticated, authorized and encrypted. Here’s why:

• Authentication uniquely identifies each microservice and ensures that a rogue microservice cannot access your sensitive data.
• Authorization determines which microservices can communicate with each other. You wouldn’t want the microservice that handles your company’s credit card processing to communicate with the microservice that manages the door badge reader for your office building.
• Encryption not only prevents third parties from intercepting and viewing your data in transit, but also thwarts man-in-the-middle attacks. You definitely don’t want credit card data to be visible to unauthorized entities on the network.

As companies move towards zero-trust security, mTLS provides a cryptographically secure way to authenticate, encrypt and enforce communication policies between microservices.

What Is mTLS?

Mutual TLS (or mTLS) refers to transport layer security that uses a two-way encrypted channel between the server and client. Today, mTLS is the preferred protocol for securing communications among microservices in cloud native applications.

While transport layer security (TLS) has been used to secure traffic between clients and servers on the internet for many years, it typically uses unidirectional identification — where a server presents a certificate to prove its identity to a client. A basic example of this one-way authentication is when you access your bank account online. The server sends your computer a certificate to prove it is actually the bank you are connecting to. That same certificate includes a public encryption key that is used to create a cryptographically secure encrypted link between you and the bank over which data passes.

Mutual TLS extends the client-server TLS model to include authentication of both parties. Where the bank relies on other, application-specific mechanisms to confirm a client’s identity — such as a user name and password (often accompanied by two-factor authentication) — mTLS uses x.509 certificates to identify and authenticate each microservice. Each certificate contains a public encryption key and an identity, and is signed by a trusted certificate authority who proves that the certificate represents the entity presenting it.

In mTLS, each microservice in a service mesh verifies the other’s certificate and uses the public keys to create encryption keys unique to each conversation. This enables the communications between pairs of microservices to be authenticated and encrypted.

How mTLS Works in a Service Mesh

What we have learned at Citrix, is, at a high level, the process of authenticating and establishing an encrypted channel using certificate-based mutual authentication in a service mesh involves the following steps:

1. Microservice A sends a request for the certificate of microservice B.
2. Microservice B replies with its certificate and requests the certificate of Microservice A.
3. Microservice A checks with the certificate authority that the certificate belongs to Microservice B.
4. Microservice A sends its certificate to microservice B and also shares a session encryption key (encrypted with the public key of microservice B).
5. Microservice B checks with the certificate authority that the certificate it received belongs to microservice A.
6. With both microservices mutually authenticated and a session key created, communication between them can be encrypted and sent via the secure link.

The Role of the Service Mesh Control Plane for mTLS

Istio is perhaps the most well-known, feature-rich and mature service mesh control plane that provides secure service-to-service communication, without the need for any application code changes. From an mTLS perspective, Istio and all service mesh control planes must offer:

• A certificate authority that handles certificate signing and management.
• A configuration API server that distributes communication policies (such as authentication policies, authorization policies and secure naming information) to the proxies.

The control plane distributes the certificates and authorization policies to the sidecars. When two microservices need to communicate, the sidecars establish a secure proxy-proxy link and are responsible for encrypting the traffic through it.

The Role of Sidecars for mTLS

While it is possible to define communication security policies and carry out authentication and encryption in the application microservices themselves, it requires implementing authentication mechanisms, defining authorization policies, and traffic encryption in the code of each microservice.

This is inefficient because you must write these into each and every microservice, you must update it when the application changes, and you need to test it on every release to ensure that the new code does not break the communication. This can be a burden on developers, leads to errors and prevents them from focusing on code that implements business logic. In a service mesh, the overhead of securing communications is offloaded to sidecars proxies, like Citrix ADC CPX or Envoy, that sit alongside each microservice.

When two microservices need to communicate, it is the sidecars that establish the mTLS connection through which encrypted traffic will flow. The sidecars exchange certificates and authenticate each other with the certificate authority. They check the authorization policies in the configuration pushed by the control plane, to see if the microservices are allowed to communicate. If they are, the sidecars will establish a secure link using a generated session key, so that all the data between the microservices will be encrypted. The actual microservice application code itself is not affected. Sidecars, therefore, make application development agile and more efficient.

Why Non-mTLS Communication Is Still Important

Sometimes it is important for microservices to communicate with external sources or microservices that may not have mTLS enabled, or may not be part of the same mTLS ecosystem. In these cases, data must be sent in plain text over an unencrypted and/or unauthenticated channel.

Microservices may need to make or receive API calls to other applications, which may be owned by a different app team who are not in a position to enable mTLS — or even an external third party. Similarly, microservices may need to send telemetry data to a non-mTLS observability stack — after all, every SRE needs telemetry data to gain visibility for root cause analysis and troubleshooting.

Furthermore, as multicluster deployments become more popular, there will be an increase in the number of mTLS “mismatches” — as some clusters will have it enabled and others not.

Investigate your environment for where a microservice may need to accept both mTLS and non-mTLS traffic, so you can plan proactively.

Implementing mTLS in a Service Mesh

There are many service mesh control planes with varying levels of maturity and unique features. When it comes to mTLS, all service meshes work on the same principles to secure communications between microservices. Many service meshes offer a solid mTLS baseline, but they differ in their overall capability and the way they are deployed. You need to be aware of how your chosen service mesh control plane implements mTLS and what features are implemented by default, or you risk breaking your applications.

Istio, for example, is advanced and flexible with its mTLS implementation. It offers granular levels to define the extent of your mTLS deployment. Mutual TLS can be set specific to a service, across a namespace, or over the entire service mesh — obviously, Istio selects the narrowest matching policy for each service.

This granularity enables you to assign namespace ownership to different organizational groups and lets them define their own mTLS settings. That said, each group needs to be mindful of the level of mTLS restriction they deploy — especially for microservices that communicate externally.

Watch Out for mTLS Defaults: Don’t Break Your Application While Trying to Secure It

You should pay attention to how your service mesh implements mTLS by default. Istio supports three mTLS modes that enable you to control how microservices communicate in a service mesh:

1. Permissive: Proxies will accept both mTLS and plain text traffic.
2. Strict: Proxies accept only mTLS traffic.
3. Disable: Mutual TLS is disabled.

Sensibly, Istio configures each proxy to use mTLS in permissive mode by default, which allows a service to accept both plain text and mutual TLS traffic. This flexibility is a best practice for all service mesh implementations because it lets microservices accept non-mTLS traffic from other sources so that you do not break the applications.

Permissive mode helps you get started with mTLS with less risk of breaking your applications because you can deploy, test communications and tighten security incrementally. This is extremely useful during workload migrations, because it allows microservices that cannot use mutual TLS to be moved into the mesh and still communicate.

Be aware that permissive mode is a great default, but it does actually weaken your security posture because it opens a door for plain text communication with other sources. While it may be tempting to implement strict mTLS from the start because it is more secure, it is a strategy that requires meticulous planning, full visibility, and analysis of your communication flows. There are many things that can break applications when you move to strict mode. For example:

• Microservices without sidecars will not complete an mTLS handshake; you may have to add a sidecar to those microservices without one.
• Incorrect naming of service ports will cause sidecars to reject mTLS requests; pay extra attention to Istio’s precise naming convention of $protocol-$service.

Be Aware of mTLS Differences in Various Service Mesh Control Planes

Of course, Istio is not the only service mesh to offer mTLS to secure communications — others offer similar functionality, but there are differences.

Red Hat OpenShift is based on the Istio control plane and has similar mTLS features, including granular implementation and Permissive mode by default, but replaces the underlying BoringSSL with OpenSSL.

LinkerD also offers mTLS, which by default is automatically enabled for HTTP-based communication between meshed pods via the LinkerD proxies. While LinkerD acknowledges some gaps in its mTLS offering, the latest 2.9 release addresses some of them and extends mTLS protection to all TCP connections — which is a big step on the road to zero-trust communications.

In the Kuma service mesh, mTLS is not enabled by default. When it is enabled, every connection between data plane proxies is denied by default. While this is a laudable security stance, it does mean that you have to explicitly allow connection using the TrafficPermissions feature. That said, Kuma lacks the breadth of features for secure communications that Istio offers and it will take some development for Kuma to catch up.

Amazon Web Services‘ AWS App Mesh also supports encryption between microservices. You can use AWS Certificate Manager or bring your own. AWS App Mesh supports “strict” and “permissive” modes.

Meeting Your mTLS Requirement

Mutual TLS is a critical component of zero-trust networking and is vital to secure the communications between the microservices in your service mesh. Implementation, however, is not entirely straightforward. You need to be aware that microservices often communicate with non-mTLS entities and you should make allowances accordingly. You should choose the communication mode carefully by weighing convenience versus security. Lastly, whichever service mesh control plane you choose, pay attention to the specific implementation for mTLS — they are not all the same.

Proper planning prevents poor performance. It’s no different for mutual TLS.

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ANN ARBOR, Mich., Jan. 24, 2020 /PRNewswire/ — Researchers will now be able to uncover new connections and make new discoveries using ProQuest’s TDM Studio service, a pioneering end-to-end solution for text and data mining. TDM Studio puts the power of text and data mining directly into the researcher’s hands, from their initial idea to their final output.

With this new solution, creating a content set has been reduced to hours, rather than the months required with traditional approaches. TDM Studio gives researchers the freedom to use the content, methods and tools they prefer – and to collaborate on projects both within and outside their university.

TDM Studio unlocks a vast collection of current and historical ProQuest content (including news, journals dissertations and theses, primary sources and more) for TDM. Researchers also have the option to incorporate content from other sources, and to utilize their preferred methods with open source programming languages such as R and Python – along with methods provided by ProQuest – for analysis and visualization.

“The use of text and data mining in academia is enabling researchers in all disciplines to make breakthroughs that have never before been possible,” said Mindy Pozenel, Director of Product Management for ProQuest TDM Studio. “With its flexibility and ease of use, TDM Studio helps researchers bypass the cumbersome mechanics of text and data mining and get straight to the point: answering their research questions. TDM Studio has also been designed as an ideal solution for teaching and learning.”

Librarians can use TDM Studio to further leverage their existing wealth of content, creating more ways to partner with research teams and enhance teaching and learning.

Caleb Rawson, Assistant Professor of Accounting at the University of Arkansas, is using TDM Studio for a project that analyzes how public firms communicate information and how that information is reported in the media. “One of my greatest research challenges is working with a corpus of millions of news articles, organizing the relevant content and matching it to other data,” he said. “TDM Studio is helping me find patterns in data in a way that’s consistent, reliable, and in a way that makes sense.”

“The use of text and data mining greatly increases the number of important questions we can answer,” said John Eric Humphries, Assistant Professor of Economics at Yale University, who is using TDM Studio to analyze dissertations for a project related to human capital. “ProQuest has been an amazing partner in setting up a TDM infrastructure that is both powerful and easy to use.”

“In the medical field, we often don’t have the manpower to go through tedious manual processes to analyze data, which can be frustrating,” said Sunmoo Yoon, Associate Research Scientist at Columbia University, who is working on a project that uses Twitter to provide culturally sensitive support to dementia caregivers. “By text mining a large corpus of material, TDM Studio is helping us learn the right words and phrases to help target caregivers with self-care and self-management messages, reducing their risk of loneliness and depression.”

In addition to Yale, Columbia and the University of Arkansas, nine other institutions globally partnered with ProQuest and contributed to the development of TDM Studio. Several early-access customers are already using TDM Studio, and ProQuest is currently recruiting additional early users.

ProQuest TDM Studio will launch in the second quarter of 2020. Learn more about TDM Studio on our website.

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In its Crossing the Quality Chasm: A New Health System for the 21st Century report, the Institute for Healthcare Improvement (IHI) identifies six distinct “Aims for Improvement” addressing the divide between good health care and the health care that people actually receive. Patient-centered service is a leading focus.

Donald M. Berwick, MD, MPP, former president and chief executive officer of IHI and former administrator of CMS said, “We are guests in our patients’ lives instead of hosts in our healthcare organizations.” Being guests in the lives of patients means being welcomed in, not waited for. This requires that we prioritize patient convenience–something artificial intelligence (AI) in healthcare can help deliver.

Multiple office visits for checkups and routine care are being replaced with remote monitoring using advanced human-generated data devices.  When this monitoring is combined with online consultations, guided by AI, providers can save time and improve the accuracy of their diagnoses. From eye and CT scans, X-rays and mammograms, to predicting ovulation cycles, automation and robotics not only expedite medical procedures but also improve outcomes.

Another objective in the IHI report states that healthcare should occur in a timely fashion. “We think waiting is defect,” Berwick wrote. “At least non-instrumental waiting, waiting that no one intends or that doesn’t carry any information with it. So we’re asking for less waiting in our system — for both patients and those who give care — as a centralized characteristic of the system.”

Not all procedures in everyday healthcare delivery require human interaction and interpretation. Continuing to operate under this assumption is placing an increasing burden on the existing care delivery systems. AI, virtual reality, and robotics not only enable quicker remote diagnosis and monitoring, but they can also be used directly for treating anxiety, stress, and reducing pain. The more accessible this “virtual” healthcare becomes to patients, the greater the number that can be served within a given time period.

Reinforce Doesn’t Mean Replace
In spite of the hype, appropriately leveraging technology does not mean physicians and caretakers will be replaced by robots altogether, as some fear. These still-essential, highly skilled workers are merely afforded more time to dedicate to sophisticated, more personalized procedures and processes. More time to use their human neural networks to the fullest potential.

“When I grow up I want to be a doctor because I want to lose my soul to notification-clicking and note-writing,” Curai data scientist, Jen Jen Chen facetiously wrote in a Medium post. Her thesis supporting the idea that data and AI can eliminate monotony in delivering healthcare.

The best use of AI is to assist skilled healthcare delivery professionals to perform at the top of their credentials. Integrate many sources of real-world data and then one can use AI to understand if a patient is in trouble, often before they are symptomatic. This integration of human capability with AI is essential to achieve optimal outcomes in a system as complex as healthcare.

The Inclusive Future for Everyone
Those months-out appointments for healthcare specialists can become a thing of the past with virtual care. No longer does a rare disease specialist need to travel from institution to institution to tend to appointments. AI can afford doctors the ability to spend more important, one-on-one time with patients only when it’s needed.

In a world of grueling Sci-Fi movies and robot world domination, the initial idea of robots and AI doing the work of doctors can be a fearful one. But the reality of robotic and AI-enhanced doctors isn’t nearly as creepy as tiny robot snakes that slither through your brain. Artificial intelligence and robotics represent some of the greatest leaps and bounds to bridging the chasm between desired and actual healthcare delivery, and they needn’t be feared.

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Tech Mahindra plans to cut service delivery time for clients by reusing some standard components it has built up over the years. The move is expected to improve its productivity and margins, a senior executive said.

Tech Mahindra, among the top five Indian IT services exporters, has been building a library of project-specific intellectual property on a common platform over the past few years.

“You have to build IP to gain value and bring in knowledge in a reusable fashion. It has been happening with libraries, but not with end-to-end outcomes. So, out of 20 CRM projects, if the top five are repeated, can I cull out that knowledge so that the next project can be rolled out faster?” Abhijit Lahiri, head of new age delivery at Tech Mahindra, said.

At the base of this system is a central microservices portfolio enabled service, which is built upon microservices and open architecture. It connects six other marketplaces, which together forms the basis of the new system. These are upskilling as a service, capability as a service, digital inside continuous delivery, design thinking as a service and active workplace programme.

The company hopes to reduce time

to market and improve margins, and do more with the same number of resources or people.

Building IP is a key step towards that, increasingly important as contract values across the industry start moving down.

Once a particular software has been delivered, it becomes easier to replicate it on another project, with only the project specific customisation needing to be done separately.

“The biggest advantage is that you’ve already delivered the software and know what works. And then can build on that,” Lahiri said.

The company has already started shifting programs to this platform, which then learns from the available data and throws up trends on the overall marketplace.

“Everything we do to win a bid or project is captured in some form continuously by the AI engine, which then also forecasts what we need and what portfolio is running faster in the market,” he said.

This information can then be communicated to the sales team and help them pitch better for projects.

The company will face some challenges, including convincing clients to share IP, but says customers from non-competing industries could even stand to benefit through knowledge sharing.

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WebPurify has launched a limited beta release of Optical Character Recognition Profanity Filter Service. It combines WebPurify’s Optical Character Recognition technology with its profanity filter solution into a single API.

The software can detect profane text in memes and other images in blogs, forums, social media apps, children’s sites, in-game chats, interactive agency campaigns and more. According to WebPurify, the proliferation of memes and images have made it more difficult to filter out profanity, as many optical character recognition programs can only extract text from documents.

According to WebPurify, its text extraction technology is trained on user-generated content, making it an effective tool for scanning content such as images and memes for offensive language. Clients can also customize a “block and allow” list to address any additional concerns.

“We initially created this as an assistant to our live image moderation teams,” said Jonathan Freger, founder and CTO of WebPurify. “It was such a successful addition to our moderations platform that we decided to make it publicly available.”

While there are many systems that identify profanity to filter it out, Deep Analysis founder Alan Pelz-Sharpe said doing it at the OCR level is not common.

“There are many other vendors that analyze and extract text from images and memes, though they don’t necessarily utilize OCR,” he said. “Today, newer vendors tend to also leverage machine learning, computer vision and artificial intelligence.”

Pelz-Sharpe added that products like WebPurify or Microsoft Azure Content Moderator are good first steps in stopping profanity before it is published or distributed.

“The bigger problem is in capturing the context of a message and the words used within that context,” he said. “That’s a bigger challenge to resolve.”

Additionally, Pelz-Sharpe said identifying text in images is not an exact science but continues to improve.

“In truth, though, analyzing text in images remains tricky and inconsistent,” he said. “The increased use of deep learning and advanced AI techniques is improving the accuracy rate.”

According to Freger, the OCR Profanity Filter Service will be generally available in early January.

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