What is DevOps?

DevOps is a software development methodology that combines software development (Dev) and IT operations (Ops) to improve collaboration, efficiency, and quality in the software development lifecycle.

Why We need DevOps?

We need DevOps because it allows organizations to deliver software faster and more reliably. By combining development and operations teams, organizations can automate processes, streamline workflows, and increase the speed of software delivery.

What is the Advantage of DevOps?

The advantages of DevOps include:

1. Increased collaboration and communication: DevOps promotes collaboration and communication between development teams, operations teams, and other stakeholders. This results in better alignment, shared goals, and faster resolution of issues.
2. Continuous delivery and faster time to market: DevOps enables teams to automate various stages of the software delivery process, such as building, testing, and deploying. This automation leads to faster, more frequent and reliable releases, ultimately reducing time to market.
3. Improved quality and stability: Continuous integration, automated testing, and deployment pipelines ensure that software changes are thoroughly tested and validated. This leads to higher quality releases with fewer bugs and reduces the risk of system failures.
4. Increased scalability and efficiency: DevOps practices emphasize scalability and efficiency by leveraging automation, infrastructure-as-code, and containerization. This allows for easier provisioning and scaling of resources to meet changing demands, ultimately optimizing resource utilization.
5. Enhanced customer satisfaction: DevOps focuses on delivering value to customers by rapidly responding to their needs and incorporating their feedback into new releases. This customer-centric approach improves satisfaction and loyalty.
6. Better risk management: DevOps practices encourage monitoring, logging, and continuous feedback loops, which enable teams to identify and resolve issues quickly. This proactive approach minimizes the impact of failures and reduces downtime.
7. Empowered and more motivated teams: DevOps eliminates silos and encourages cross-functional collaboration, empowering team members to contribute to different stages of the software lifecycle. This autonomy and shared responsibility result in higher engagement, motivation, and job satisfaction.

What is the feature of DevOps?

The key features of DevOps include:

• Continuous Integration (CI): DevOps emphasizes the practice of integrating code changes frequently and automatically, allowing teams to detect issues early.
• Continuous Delivery (CD): DevOps promotes the idea of delivering software updates frequently and reliably, ensuring that new features and bug fixes reach users quickly.
• Infrastructure as Code (IaC): DevOps encourages the use of code to manage and provision infrastructure, making it easier to replicate environments and reduce manual efforts.
• Automated Testing: DevOps advocates for the automation of testing processes, enabling teams to identify and fix issues quickly and efficiently.

Top 10 Use Cases of DevOps

1. Continuous Deployment: DevOps enables organizations to continuously deploy code changes to production, reducing the time between development and deployment.
2. Infrastructure Automation: DevOps allows for the automation of infrastructure provisioning and management, making it easier to scale and manage resources.
3. Continuous Monitoring: DevOps promotes continuous monitoring of applications and infrastructure, allowing teams to identify and address issues in real-time.
4. Microservices Architecture: DevOps is well-suited for organizations adopting a microservices architecture, as it enables teams to deploy and manage individual services independently.
5. Agile Development: DevOps aligns well with agile development methodologies, as it encourages collaboration, frequent feedback, and continuous improvement.
6. Release Management: DevOps helps organizations streamline the release management process, allowing for faster and more reliable software releases.
7. Cloud Migration: DevOps can facilitate the migration of applications and infrastructure to the cloud, enabling organizations to take advantage of cloud services and scalability.
8. Security and Compliance: DevOps incorporates security and compliance practices into the development process, ensuring that applications meet the necessary standards.
9. Containerization: DevOps supports the use of containerization technologies like Docker, making it easier to deploy and manage applications across different environments.
10. DevSecOps: DevOps can be extended to include security practices, resulting in DevSecOps, which integrates security into every stage of the software development lifecycle.

How to Implement DevOps?

To implement DevOps, organizations can follow these steps:

1. Assess the current state: Understand the existing development and operations processes, identify pain points, and define objectives for the DevOps implementation.
2. Build a DevOps team: Assemble a cross-functional team with members from development, operations, and other relevant departments to drive the implementation.
3. Define processes and tools: Establish the processes and tools needed for collaboration, automation, and monitoring. This may include version control systems, continuous integration and delivery tools, and infrastructure management platforms.
4. Automate processes: Identify opportunities for automation and implement tools and technologies that can automate tasks such as code deployment, testing, and infrastructure provisioning.
5. Foster collaboration: Encourage communication and collaboration between development and operations teams to break down silos and promote shared responsibility.
6. Measure and improve: Define metrics and key performance indicators (KPIs) to track the success of the DevOps implementation. Continuously monitor and analyze data to identify areas for improvement.

How to Get Certified in DevOps?

There are a number of certifications available for DevOps professionals. Some of the most popular certifications include:

• DevOps Engineer – Certified Professional (DevOps CERT): This certification is offered by the DevOps Institute and is designed for professionals with experience in DevOps. The exam covers a wide range of topics, including continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), and cloud computing.

• Certified DevOps Engineer – Associate (CDevOps-A): This certification is offered by the Linux Foundation and is designed for professionals with a basic understanding of DevOps. The exam covers topics such as automation, communication, and collaboration.

• Certified DevOps Engineer – Expert (CDevOps-E): This certification is also offered by the Linux Foundation and is designed for professionals with experience in DevOps. The exam covers a more advanced level of topics, such as security and compliance.

• Certified Kubernetes Administrator (CKA): This certification is offered by the Cloud Native Computing Foundation and is designed for professionals who want to manage Kubernetes clusters. The exam covers topics such as cluster administration, networking, and security.

• Certified Kubernetes Application Developer (CKAD): This certification is also offered by the Cloud Native Computing Foundation and is designed for professionals who want to develop applications for Kubernetes. The exam covers topics such as application development, deployment, and monitoring.

The best certification for you will depend on your experience level and career goals. If you are new to DevOps, then the CDevOps-A certification is a good place to start. If you have more experience, then the CDevOps-E certification or the CKA certification may be a better fit.

To become certified, you will need to take and pass an exam. The exams are typically offered online and are proctored. The cost of the exams varies depending on the certification.

To get certified in DevOps, Visit most popular website for providing DevOps certification Courses .

– DevOpsSchool.com
– scmGalaxy.com
– BestDevOps.com

How to Learn DevOps?

There are a number of ways to learn DevOps. Some of the most popular ways include:

• Taking a course: There are a number of online and in-person courses available for DevOps professionals. Example- DevOpsSchool.com , scmGalaxy.com , BestDevOps.com
• Reading books: There are a number of books available on DevOps.
• Attending conferences: There are a number of conferences held each year that focus on DevOps.
• Joining a community: There are a number of online communities for DevOps professionals.
• Getting hands-on experience: The best way to learn DevOps is by getting hands-on experience. This can be done by volunteering for a DevOps project or by working for a company that uses DevOps practices.

The post What is DevOps and Why We need DevOps? appeared first on Artificial Intelligence.

Organisations often block their own path towards scaling by delaying innovations purely out of their loyalty to their core businesses. Unfortunately, fixed organisational structures and legacy operating models result in frailty, disabling the sight of potential market changes. Enterprises hesitate to build products or services with new technology as they are unsure of whether the growth rates would satisfy their shareholders.

Today, to compet in a VUCA environment, businesses and their change agents must consider relooking and reconsidering how they engage and conduct business with their customers and leverage innovative technologies to enhance or introduce products/services to the market.

With rapidly changing consumer buying patterns and preferences, enterprises are increasingly focusing on digitization to evolve their business models, they are being mindful of the efficiency of business operating units, which enables them to pinpoint areas that need additional focus or restructuring quickly. Some of the possible initiatives organisations can opt for are:

• Growth hacking:Organizations need to be agile and iterative in their approach to keep up with the changes in the industry. Having a growth mindset can enable management, embrace challenges, show resilience while working through obstacles, and bounce back from impediments sooner, leading to overall higher achievement. Organizations backed by a growthhacking mindset will be ableto foster innovation and generate higher financial returns.
• Retooling organization:Companies should ‘avoid putting all eggs” in one basket regarding technology infrastructure. The focus should be on adopting technology like building a Lego structure, where individual components are replaced if the scalability and validity for the current and future needs of the business aren’t met.
•  Adopting new age innovation rather reinventing: Applications of AI is evolving within the industry at a fast paced, which enables organizations to evaluate quickly, adapt, pilot and scale within the organizations. Reinventing AI would mean wastage of resources of time, instead organization precious resources can be spent on identifying the right opportunity to evaluate and scale the benefits of AI.The global COVID-19 pandemic has crushed standards and redefined how business is conducted, affecting most enterprises in some way or another. At the same time, enterprises were already leveraging data science and AI in the past few years.  A significantly greater number of organizations are now looking for ways to harness them to reinvent themselves. Key-focused areas remain in strategy building, decision-making and governance setup, business planning and budgeting, funding decision making, managing performance and company culture, risk management, and more.For businesses, resiliency will become even more significant than efficiency as they move forward and data science will help companies maintain. For instance, retail stores and restaurants that were more dependent on brick-and-mortar sales before the pandemic had to make drastic changes to survive and sustain. While some were forced to shut shop, the rest kept steering ahead with new business models to adapt and thrive. Data science helped companies stabilise their organisations, build new processes, establish new communication channels and workflow, adapt to the remote working environment, recognise (and adapt to) changing consumer patterns and identify the emerging trends by using AI and machine learning.Traditionally, legacy companies used to focus only on their core business. With the new wave of transformation and new opportunity post the pandemic, these prominentestablished players are reinventing themselves and creating businesses in new areas with a very different mindset and culture than their traditional organizations.The new digital era demands asignificant change in traditional thinking and focusing on the practical approach of collaboration, competition, and innovationthat can combine data science, AI,and business acumen to conceive, build and bring new digital products to market at scale.

The post KEY TO SCALE IN VOLATILE MARKETS: RE-INVENTING BUSINESS MODELS WITH THE SUCCESSFUL DEPLOYMENT OF AI AND DATA SCIENCE BY ANEES MERCHANT EXECUTIVE VICE PRESIDENT – APPLIED AI & DIGITAL AT COURSE5 INTELLIGENCE appeared first on Artificial Intelligence.

Having machine learning in a company’s portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. 

According to PwC, AI’s potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company’s total funding to $47 million. 

For OctoML’s CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. “It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware. Even if your determined machine learning team has hurtled through this gauntlet, they still have to go through a whole different set of challenges to package and deploy at the edge,” explained Ceze.

A good performance in ML models requires long hours of manual optimizations. These long hours will then translate into hefty cloud bills. Added to this is the model packaging which varies with devices and platforms. According to Ceze, there are no modern CI/CD integrations to keep up with model changes.

“What good is an ML model if it isn’t fast? doesn’t scale? isn’t accurate enough? takes weeks to deploy? and costs too much?,” questioned Ceze as he made a case for OctoML. 

OctoML addressed these pain points with their open-source machine learning compiler framework Apache TV, which according to the team, has quickly become the go-to solution for developers and ML engineers to maximize ML model performance on any hardware backend. “With OctoML we are establishing the first Machine Learning Acceleration Platform that will automatically maximize model performance while enabling seamless deployment on any hardware, cloud provider, or edge devices,” said Ceze.

Be it MLOps or XOps, these services are designed to ease the developers of technical debt that these mega ML models accumulate with changing complexities. Apart from OctoML, there are a few other startups that have succeeded in convincing the investors. Let’s take a look at couple of them:

Verta 

Funding till date: $10 million

The team at Verta is building software for data science teams to address the problem of model management — how to track, version, and audit models used across products. Verta MLOps software supports model development, deployment, operations, monitoring, and collaboration enabling data scientists to manage models across their lifecycle. So far, the company has $10 million in funding and it promises to make robust, scalable, mature deployable models a reality.

“We’re obsessed with helping organizations get ML models into production because that’s the only way they can generate business value,” said the team at Algorithmia. Their enterprise MLOps platform manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively. Unlike inefficient and expensive do-it-yourself MLOps management solutions that lock users into specific technology stacks, Algorithmia automates ML deployment, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD systems, and provides advanced security and governance.

Today Algorithmia’s services are used by over 130,000 engineers and data scientists, including the United Nations, government intelligence agencies, and Fortune 500 companies.

“It’s [MLOps] going to be an essential component to enterprises industrializing their AI efforts in the future,” said Diego M. Oppenheimer, Algorithmia’s CEO in a recent interview with GitHub.

Databand.ai

Funding: $14.5 million

Databand brings in the similar flavor into the ML ecosystem. The team Databand is trying to solve the problems that arise due to increasing data workloads. The company founded by Josh Benamram, Victor Shafran and Evgeny Shulmanhelps helps data engineering teams catch data pipeline issues and trace the impact of those problems across end-to-end data flows. Databand’s platform includes an application for visualizing pipeline metadata, and an open source library for integrating with your Python, Java, Scala, or SQL data processes. Data pipeline monitoring is a key aspect of machine learning deployment. We can clearly see how targeting even a niche aspect of the whole ML deployment can land big investors.

Modern day software companies are in the process of or have already embraced machine learning as a key tool. Now they are at a crucial juncture where they can either leverage the MLOps services offered by these startups or build everything on their own. But, there are not many reasons why an organization looking to transition to ML will take the pain of MLOps. As companies look to leverage ML minus the deployment headache, niche players like OctoML will  continue to pop up. Even the latest Gartner survey lists scalability and acceleration of machine learning deployment as two driving forces that will continue to trend this year. According to Gartner, XOps— a variant of MLOps that deals with efficiencies in data, machine learning, model, platform will try to implement best DevOps practices and ensure reliability, reusability and repeatability. 

The post Machine Learning Deployment Is The Biggest Tech Trend In 2021 appeared first on Artificial Intelligence.

Machine learning: Accelerating your model deployment

Business models rely on data to drive decisions and make projections for future growth and performance. Traditionally, business analytics has been reactive – guiding decisions in response to past performance. But today’s leading companies are turning to machine learning (ML) and AI to harness their data for predictive analytics. This shift, however, comes with significant challenges.

According to IDC, almost 30% of AI and ML initiatives fail. The primary culprits behind this failure are poor-quality data, low experience and challenging operationalization. They also require a lot of time to maintain, since you need to repeatedly train ML models with fresh data through the development cycle, due to data quality degradation over time.

Let’s explore the challenges presented when developing ML models and how the Rackspace Technology Model Factory Framework simplifies and accelerates the process – so you can overcome these challenges.Machine learning challenges

Among the most difficult aspects of machine learning is the process of operationalizing developed ML models that accurately and rapidly generate insights to serve your business needs. You’ve probably experienced some of the most prominent hurdles, such as:

• Inefficient coordination in lifecycle management between operations teams and ML engineers. According to Gartner, 60% of models don’t make it to production due to this disconnect.
• A high degree of model sprawl, which is a complex situation where multiple models are run simultaneously across different environments, with different datasets and hyperparameters. Keeping track of all these models and their associatives can be challenging.
• Models may be developed quickly, but the process of deployment can often take months – limiting time to value. Organizations lack defined frameworks for data preparation, model training, deployment and monitoring, along with strong governance and security controls.
• The DevOps model for application development doesn’t work with ML models. The standardized linear approach is made redundant by the need for retraining across a model lifecycle with fresh datasets, as data ages and becomes less usable.

The ML model lifecycle is fairly complex, starting with data ingestion, transformation and validation so that it fits the needs of the initiative. A model is then developed and validated, followed by training. Depending on the length of development time, you may need to repeatedly perform training as a model moves across development, testing and deployment environments. After training, the model is then set into production, where it begins serving business objectives. Through this stage, the model’s performance is logged and monitored to ensure suitability.Rapidly Build Models with Amazon SageMaker

Among the available tools to help you accelerate this process is Amazon SageMaker. This ML platform from Amazon Web Services (AWS) offers a more comprehensive set of capabilities towards rapidly developing, training and running your ML models in the cloud or at the edge. The Amazon SageMaker stack comes packaged with models for AI services such as computer vision, speech and recommendation engine capabilities, as well as models for ML services that help you deploy deep learning capabilities. It also supports leading ML frameworks, interfaces and infrastructure options.

But employing the right toolsets is only half the story. Significant improvements in ML model deployment can only be achieved when you also consider improving the efficiency of lifecycle management across the teams that work on them. Different teams across organizations prefer different sets of tooling and frameworks, which can introduce lag through a model lifecycle. An open and modular solution – agnostic of the platform, tooling or ML framework – allows for easy tailoring and integration into proven AWS solutions. A solution such as this will allow your teams to use the tools they are comfortable with.

That’s where the Rackspace Technology Model Factory Framework comes in, by providing a CI/CD pipeline for your models that makes them easier to deploy and track.

Let’s take a closer look at exactly how it improves efficiency and speed across model development, deployment, monitoring and governance, to accelerate getting ML models into production.End-to-end ML blueprint

When in development, ML models flow from data science teams to operational teams. As previously noted, preferential variances across these teams can introduce a large amount of lag in the absence of standardization.

The Rackspace Technology Model Factory Framework provides a model lifecycle management solution in the form of a modular architectural pattern, built using open source tools that are platform, tooling and framework agnostic. It is designed to improve the collaboration between your data scientists and operations teams so they can rapidly develop models, automate packaging and deploy to multiple environments.

The framework allows integration with AWS services and industry-standard automation tools such as Jenkins, Airflow and Kubeflow. It supports a variety of frameworks such as TensorFlow, scikit-learn, Spark ML, spaCy, and PyTorch, and it can be deployed into different hosting platforms such as Kubernetes or Amazon SageMaker.Benefits of the Rackspace Technology model factory framework

The Rackspace Technology Model Factory Framework affords large gains in efficiency, cutting the ML lifecycle from an average of 15 or more steps to as few as five. Employing a single source of truth for management, it also automates the handoff process across teams, simplifies maintenance, and troubleshooting.

From the perspective of data scientists, the Model Factory Framework makes their code standardized and reproducible across environments, and it enables experiment and training tracking. It can also result in up to 60% of compute cost savings through scripted access to spot instance training. For operations teams, the framework offers built-in tools for diagnostics, performance monitoring and model drift mitigation. It also offers a model registry to track models’ versions over time. Overall, this helps your organization improve its model deployment time and reduce effort, accelerating time to business insights and ROI.Solution overview – from development and deployment, to monitoring and governance

The Model Factory Framework employs a curated list of Notebook templates and proprietary domain-specific languages, simplifying onboarding, reproduction across environments, tracking experiments, tuning hyperparameters and consistently packaging models and code agnostic to the domain.

Once packaged, the framework can execute the end-to-end pipeline which will run the pre-processing, feature engineering and training jobs, log generated metrics and artifacts, and deploy the model across multiple environments.

• Development: The Model Factory Framework supports multiple avenues of development. Users can either develop locally, integrate with Notebooks Server using Integrated Development Environments (IDEs) or use SageMaker Notebooks. They may even utilize automated environment deployment using AWS tooling such as AWS CodeStar.
• Deployment: Multiple platform backends are supported for the same model code and models can be deployed to Amazon SageMaker, Amazon EMR, Amazon ECS and Amazon EKS. Revision histories are tracked, including artifacts and notebooks with real-time batch and streaming inference pipelines.
• Monitoring: Model requests and responses are monitored for detailed analysis which enables the ability to address model and data drift.
• Governance: Data and model artifacts are clearly separated and access can be controlled using AWS IAM and bucket policies that control model feature stores, models and associated pipeline artifacts. The framework also supports rule-based access control through Amazon Cognito, traceability with Data Version Control, and auditing and accounting through extensive tagging.

Using a combination of proven accelerators, AWS native tools and the Model Factory Framework, companies can experience significant acceleration in model development automation, reducing lag and effort and experiencing improvements in time to insights and ROI.

If your organization is interested in utilizing the Model Factory Framework to simplify and accelerate your ML use cases, visit our AI and ML pages for further info, including customer stories, details of supported platforms and other helpful resources.

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As artificial intelligence (AI) emerges into the mainstream, there is misinformation and confusion about what it’s capable of and the potential risks it constitutes. Our culture is enriched with dystopian visions of human ruin at the feet of all-knowing machines. On the other hand, most people appreciate the potential good AI might do for the civilization through the improvements and insights it could bring.

Though computer systems can learn, reason, and act, these are still in their infancy. Machine learning (ML) needs massive datasets. Many real-world systems such as self-driven cars, a complex blend of physical computer vision sensors, complex programming for real-time decision making, and robotics are needed. For businesses that are adopting AI, deployment is more straightforward but enabling AI to access information and allowing any measure of autonomy brings serious risks that have to be considered.

What risks does AI cause?

Accidental bias is not new with AI systems, and programmers or specific datasets can entrench it. Unfortunately, if this bias leads to poor decisions and even discrimination, legal repercussions and reputational damage may follow. Flawed artificial intelligence design can also leads to overfitting or underfitting, while AI makes too particular decisions.

Establishing human oversight, stringently testing AI systems can mitigate those risks during the design phase. It is also possible by closely monitoring those systems when they are operational. Decision-making abilities must be measured and assessed to confirm that any emerging bias or questionable decision-making is addressed rapidly.

Although these threats are based on unintentional errors and failures in design and implementation, a different set of risks emerges when people intentionally try to subvert AI systems or wield them as weapons.

How can cyber attackers manipulate AI?

Misleading an AI system can be alarmingly easy. Attackers can manipulate the datasets to train AI, making subtle changes to carefully designed parameters to ignore increasing suspicion while slowly steering AI in the desired direction. Wherein attackers fail to access the datasets; they may employ evasion, tampering with inputs to vigour mistakes. These systems can be manipulated into misclassifications by modifying input data to make proper identification hard.

Though checking the accuracy of data and inputs may not prove possible, every effort should be made to harvest data from reputable and verified sources. Bake in the identification of oddity to empower AI so that it can identify malicious inputs. Also, isolate AI systems with preventive mechanisms that make it easy to turn off if things start to go wrong.

How could AI be weaponised?

Cybercriminals can also employ AI to seek assistance with the scale and effectiveness of their social engineering attacks. Artificial intelligence can learn to detect behaviour patterns, figuring out how to convince people that a video, phone call, or email is legitimate. It then can persuade them to compromise networks and hand over sensitive data. All the social techniques that cybercriminals are currently employing could be enhanced immeasurably using AI.

There is another scope to use AI to recognize new vulnerabilities in networks, devices, and applications as they emerge. The job of keeping information secure is made difficult because of brisk identifying opportunities for human hackers.

How to stimulate the company’s security using AI?

AI can be highly effective in monitoring network and analytics, setting up a baseline of normal behaviour, and flagging discrepancies in things such as server access and data traffic immediately. Detecting intrusions beforehand gives you the maximum chance of restraining the damage they can do. Initially, it may be useful to have AI systems flag abnormalities and alert IT departments to investigate. While AI leans and improves, it may be provide the authority to invalidate threats itself and refrain intrusions in real-time.

With a significant lack of information security, AI can shoulder some of the burdens and allow limited staff to focus on complex problems. As companies try to reduce costs, AI is turning into more attractive, aiming to replace people. It will benefit companies and improve with experience, but ambitious companies must plan to mitigate the potential risk of cyber-attacks now.

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Akamai today announced it is adding the ability to deploy microservices on its edge computing platform to enable developers to run latency-sensitive applications faster.

David Theobald, principal product manager at Akamai, said developers can now code dynamic content assembly at the edge to create microservices on the Akamai Intelligent Edge platform that include external requests and the ability to manipulate response bodies. That approach provides developers with the primitives required to run edge computing applications on the equivalent of a serverless computing framework, he said.

In addition, Akamai is adding EdgeWorker reporting and debugging tools for JavaScript applications deployed on the edge of its content delivery platform (CDN) and updated its application programming interfaces (APIs) to make it easier and faster to deploy “cloudlet” applications.

Finally, Akamai also has updated its Akamai Image and Video Manager software-as-a-service (SaaS) offering by adding a video status optimization API through which IT teams can track the status of videos as they are being optimized by the Akamai CDN.

While CDNs have been widely employed for decades, they are now evolving into platforms for deploying edge computing applications that are developed and deployed using best DevOps practices. Rather than having to replicate the IT infrastructure already put in place by a CDN provider, many organizations are opting to essentially consume IT infrastructure as a service as they deploy edge computing applications alongside existing web applications.

Competition is already fierce among CDN providers seeking to leverage points of presence around the globe to enable IT teams to deploy applications closer to the point where data is generated and consumed. Many of these edge computing applications are at the core of digital business transformation initiatives that require data to be processed in near real-time. As such, processing that data in a local data center creates too much latency.

Of course, cloud service providers have also extended the range of services they provide to include CDN services. It’s not clear to what degree IT organizations will prefer to leverage those cloud services versus the CDN capabilities many of them already rely on to deploy web applications.

Regardless of the approach, the number of IT teams building, deploying and maintaining their own edge computing infrastructure is likely to be reduced sharply in the wake of the COVID-19 pandemic. IT teams are trying to reduce the potential risk to their IT staff by limiting travel as much as possible, which makes putting IT personnel on a plane to install infrastructure an option of last resort.

Developers, in the meantime, are building more applications faster than ever, thanks mainly to DevOps processes that enable them to work from home. As the deployment backlog for edge computing applications builds, the need to rely more on external services becomes that much more pressing. It may be a while before most DevOps teams routinely view CDNs as just another target platform for deploying applications, but in many cases, that day is already here.

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The vast and intelligent connection of physical devices, known as the Internet of Things (IoT), is increasingly bolstering productivity and communication levels across businesses globally. This also enables various numbers of functions in enterprises. The augmenting use of IoT devices have taken both consumers and organizations by storm, unfortunately, it also possesses specific security concerns to them. Rather, an organization can only reap the promise of IoT’s capabilities if it contemplates the security elements as a vital component of their IoT deployment.

IoT-powered solutions cover a large area of businesses and commercial applications. Their use cases driven by the ability to connect, monitor, and control tens of millions of Internet-connected devices, exchange information, and take autonomous action based on continuous input. These applications and solutions are evident across diverse industries, ranging from manufacturing, healthcare, and transportation to oil and gas, utilities, and energy.

Many IoT devices encompass back doors and software flaws that make them vulnerable and easy to hack. On the other hand, several companies can face challenges of storing and managing the troves of data that these devices produce. In this case, enterprises need to figure out a way to store, track, assess and make sense of the vast amounts of data that will be generated. Thus, it will require a host of innovative solutions to meet these challenges and capitalize on the coming opportunities.

While it is anticipated that the world will have 43 billion IoT-connected devices by 2023, the technological advancements will make it easier to implement, opening the door for a wider variety of companies to benefit from its applications. Large businesses have already begun to invest their sizable resources in IoT technologies over the years ago. This level of uptake results in an impetus of the developing technologies that underpin the IoT.

Moreover, advanced principal technologies and a proliferation of devices have also assisted in fuelling the growth of IoT solutions. It is anticipated that investments in this technology will grow by 13.6 percent per year through 2022. And thanks to new sensors, more computing power, and reliable mobile connectivity, further growth will be evident in IoT in years to come.

An IoT platform not only serves as a middleware that connects devices and sensors but also delivers a large number of functions such as controllers and sensors, a communication network, a gateway device, interpreting and data analysis software along with end application services. The market for IoT will continue to grow as existing IT devices will need to be linked to the technology.

As a whole, IoT has an assortment of applications in enterprise settings, and its adoption is not limited to large companies. Early adopters have shifted beyond pilots to scale IoT solutions across their businesses. In fact, the technology has already made a significant rise in a number of applications across diverse sectors including Industry 4.0, connected cars, smart cities, smart homes, and digital health. IoT has been one such technology that is going to have an enormous impact not only today but also in the future.

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Humility in AI protects the quality of Decision systems

In DataRobot, Humble AI is a new feature that protects the quality of your predictions in circumstances where the model is not confident enough. With Humble AI, users make rules for models in deployment for forecasts made in real-time. These rules recognize conditions which indicate that a prediction may not be sure and trigger actions such as defaulting to a ‘safe’ prediction, overriding outlier values, or not predicting at all.

Even if Humble AI is a new concept, it is clear that it holds tremendous value for decision systems. It is particularly real-time decision systems where each decision counts and is urgent. Before we talk about the cost to business decision-making, let’s look at the three most common frameworks around AI and human intelligence working as a single integrated decision system.

Human-in-the-loop

In this process, the individual operating the system considers the recommendation of the AI system but takes the final call. For instance, though a doctor signs off on the definitive diagnosis and prognosis of kidney failure, they leverage an advanced visual artificial intelligence system to score the patient’s X-rays.

Human-out-of-the-loop

In this process, the AI system is entirely under control with zero human involvement. For example, an AI is running a real-time bidding (RTB) system creates instantaneous predictions around potential ad buys in milliseconds, without any human involvement.

Human-over-the-loop

An individual, in this process, supervises the system and can intervene whenever the AI system runs into an unexpected scenario and deviates from its performance standard. For instance, a regression model monitors manufacturing processes in real-time. As soon as an outlying prediction is triggered, a human operator is alarmed. And it turns off the system and investigates.

When it’s about choosing a level of automation for any given system, the resulting trade-off should be considered. On the edge of the spectrum, you have unbridled industrialisation that approaches maximum efficiency. However, it comes at the cost of losing the guardrails delivered by humans looped into the process. If your appetite is compact or you’re working in a highly regulated environment, the human-out-of-the-loop system may cause a serious business risk.

However, in some cases, such as real-time ad bidding, it’s impossible to get a human in the loop with decision-making capacity in a fraction of second. This is all accomplished based on a head-spinning amount of anonymized user information and quick behavioural patterns of each user. Such digital advertising systems can multi-task and effectively when a human is not in the loop. How do you mitigate risks when the decision window is unexpectedly small, and the decisions are extraordinarily complicated?

This is when the human-over-the-loop and humility in AI concept come into play. Human-over-the-loop system can balance between automation and human participation by enabling the system to perform in the fully automated mode with human intervention when required.

DataRobot MLOps currently supports a long-term oversight framework by uninterrupted collecting scoring data, predictions, and original outcomes to compare trained and installed models’ statistical properties. Wherein it can help detect the critical moment when retraining the model is necessary, humility in AI indicates that human intervention should also be an alternative on the level of an instantaneous individual prediction. However, there are a few primary questions in it.

How to understand when precisely human beings should be involved? It depends on the confidence level of the model’s prediction. The confidence level could be determined by monitoring:

Uncertainty in predictions: Predicted values are beyond the range of expected values

Outlying outputs: There are several features in the scoring data dissimilar to what the training model absorbed

Low monitoring regions: A categorical feature value is the one which a user has mentioned unexpected or inappropriate.

With DataRobotMLOps, capabilities such as no operation, overriding prediction, and discarding the prediction are baked into the Humble AI feature.

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Israeli deep learning startup DeepCube Ltd., which has built software to accelerate the process of running machine learning models in the data center and on edge devices, said today it has raised $7 million in a new round of funding.

The Series A round was led by Awz Ventures with participation from Koch Disruptive Technologies and Nima Capital, and brings DeepCube’s total amount raised to $12 million.

DeepCube, which is led by its co-founders Dr. Eli David (pictured) and Yaron Eitan, recently announced the launch of what it says is an industry-first software-based inference accelerator for deep learning, a subset of artificial intelligence that tries to mimic the way the human brain learns. The company reckons that its accelerator can dramatically improve deep learning performance on intelligent edge devices.

The problem that DeepCube is trying to solve is that deep learning deployments are still somewhat rare because of the size and speed of neural networks, plus their need for specialized hardware that’s not only expensive but also has serious compute and memory demands. Because of this, it says, deep learning remains very difficult and expensive to perform on edge devices, where resources are limited.

DeepCube’s software tries to fix all this by accelerating deep learning processes. It works by reducing the size of any deep learning model, including its training data, in a completely automated way and without any manual intervention.

The software can be deployed on central processing units, graphics processing units and application-specific integrated circuits, or ASICs. Those are computer chips that have been customized for a particular use, in both the data center and on edge devices.

With its software, DeepCube says it is able to deliver up to 10-times speed improvement and memory reduction, thereby enabling more advanced deep learning on any device.

DeepCube isn’t the only company in the deep learning acceleration business. For example, Nvidia Corp. has its Deep Learning Accelerator and Micron Technologies Inc. has its Deep Learning Accelerator platform. But DeepCube’s founder David told SiliconANGLE that DeepCube is unique in that it has created the only software framework that allows for both automatic optimization of any deep learning model, and over 10x inference speedup.

“The other so-called software accelerators do not algorithmically modify deep learning models, and so their speedup improvement is incremental, usually around 10%-30% at most,” David said. “DeepCube’s technology allows for aggressive automated restructuring of the deep learning model, so that it ends up being under 10% of its original size. Over 90% of the connections are removed.”

Awz Ventures founder and Managing Partner Yaron Ashkenazi said the inability to deploy deep learning at the edge, on small devices with minimal memory and processing power, has hindered adoption of the technology.

“DeepCube is the only company that has been able to demonstrate the necessary paradigm shift to change this,” Ashkenazi said. “DeepCube’s technology has the power to unlock truly autonomous decision-making in semiconductors, data centers and on edge devices, while improving speed and memory reductions. This is absolutely critical to the future of deep learning.”

Holger Mueller of Constellation Research Inc. told SiliconANGLE that one of the ultimate prizes of AI is to deliver deep learning capabilities that can change business outcomes.

“When software can learn by itself and change based on data, we will have achieved something critical with automation that can be done autonomously,” Mueller said. “Edge locations are of particular interest in deep learning automation because devices need to function without a human operator, and with power and bandwidth constraints.”

DeepCube said it will spend its new funds on research efforts and expanding the market for its software.

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Frost & Sullivan’s recent analysis, Global Big Data Analytics Market Fueling Artificial Intelligence, 2020, finds that the data security is a prime concern across sectors with the increasing deployment of the Internet of Things and proliferation of devices, which create copious amounts of data.

Globally, the BDA market is estimated to grow 4.5 times, garnering a revenue of $68.09 billion by 2025 from $14.85 billion in 2019, up at a staggering compound annual growth rate (CAGR) of 28.9%.

Additionally, amid the COVID-19 uncertainty, BDA continues to be a top deployment priority for enterprises as its use will help them remain competitive while accelerating innovation, especially in the healthcare sector to fight the coronavirus.

“Between the two major segments of the BDA market—data discovery and visualization (DDV) and advanced analytics (AA)—DDV is expected to become more mainstream as organizations realize the importance of data prepping, data management, and data visualization as the foundational building blocks for advanced analytics,” said Deviki Gupta, Information & Communication Technologies Senior Industry Analyst at Frost & Sullivan. “Going forward, AA’s growth is expected to rise dramatically after 2020 as use cases increase and customers grow more comfortable with data analytics overall.”

Gupta added: “From a regional perspective, North America and Latin America (NALA), led by North America, continue to be the largest contributors in the BDA market, followed by Europe, the Middle East, and Africa (EMEA), whereas Asia-Pacific (APAC) is the fastest-growing regional market for BDA. Further, from a vertical perspective, banking and financial services (BFS), government and intelligence (G&I), and retail segments that are focused on risk reduction, security, and drawing intelligence are the biggest revenue-contributing verticals.”As the market competition increases, BDA vendors look to diversify their product portfolios by offering edge analytics with the benefits of low latency and quick insights. This presents immense growth prospects:

• Package solutions to address industry-specific use cases such as defect detection and predictive maintenance analytics will be key to add value.
• Market participants should gain government support, which is a necessary foundational step for a lifelong learning record to exist.
• Professional development and customer support will be vital to driving the demand for BDA in healthcare.
• APAC, particularly China, is leading the manufacturing sector as well as the adoption of IoT devices. Thus, China should be a target for BDA vendors looking to expand in this market.
• Vendors must provide consultative services to help customers understand the correct software and hardware combinations to solve business problems.

Global Big Data Analytics Market Fueling Artificial Intelligence, 2020 is the latest addition to Frost & Sullivan’s Information & Communication Technologies research and analyses available through the Frost & Sullivan Leadership Council, which helps organizations identify a continuous flow of growth opportunities to succeed in an unpredictable future.

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