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Organizations still struggle to advance digital transformation. A common language that transcends technology could be the key to strategic transformation. By Rich Nanda, Ragu Gurumurthy, Sam Roddick, Deborah Golden.

From disruptors and disruptive tech to pandemics, political unrest, and climate change, winning the future depends on adaptation. To survive and thrive, leaders should determine how to maintain a competitive advantage and enable an ability to win in a way that doesn’t just withstand change but embraces it to generate new strategic possibilities.

The article then dives into:

• Framing the digital transformation conversation
• Five imperatives to drive digital transformation
• Putting the imperatives to work: Align strategy and drive transformation
• Change, compete, win: Getting to value with the five imperatives
• A language for today’s transformation and tomorrow’s

Our digital imperatives can enable organizations to drive transformations that align to their overarching ambition while remaining open to future strategy changes. They acknowledge the importance of AI, cloud, and cybersecurity today but leave room to evolve toward “horizon next” technologies, avoiding the trap of leaping at every shiny new technology. Ultimately, they help design-adaptive business processes and technology architectures (modular “capability stacks”) that embrace constant change and reconfiguration in the face of ongoing disruption and risk with the goal of compatibility for multiple possible futures. Excellent read!

NetOps teams have quickly learned the benefits of hosting applications in the cloud. But before they migrated or adopted a few SaaS applications, they knew in the back of their minds that monitoring performance would be difficult. A tiny voice was asking, “How will we monitor packet loss and connection latency, hop-by-hop, when using cloud applications?”. By Kevin Woods, Head of Product Marketing.

The article will walk you through the following:

• Packet loss causes problems with cloud applications
• What causes packet loss?
• Detecting packet loss
  • Using ping on the command prompt
  • By polling all the SNMP devices on the network
  • Packet capture
  • TCP traceroute
• How to monitor packet loss and latency in the cloud
• Establishing baselines and setting thresholds for packet loss and latency

Typically, poor connections at the physical layer, such as bad cables or connectors, are the problem. Congestion in the form of high-connection utilization or an overworked router in the path is another common source of trouble. Good read!

Linux has been supporting Full Disk Encryption (FDE) and technologies such as UEFI SecureBoot and TPMs for a long time. However, the way they are set up by most distributions is not as secure as they should be, and in some ways quite frankly weird. In fact, right now, your data is probably more secure if stored on current ChromeOS, Android, Windows or MacOS devices, than it is on typical Linux distributions. By Pid Eins.

The article content covers:

• The strange state of authenticated boot and disk encryption on generic Linux distributions
• The basic technologies
  • LUKS/dm-crypt/cryptsetup
  • UEFI SecureBoot
  • TPMs
• How Linux distributions use these technologies
• Attack scenarios
• Are we safe?
• Can we do better?
• In detail

The most basic attack scenario to focus on is probably that you want to be reasonably sure that if someone steals your laptop that contains all your data then this data remains confidential. Because distributions set up disk encryption the way they do, and only bind it to a user password, an attacker can easily duplicate the disk, and then attempt to brute force your password.

Article also points reader in the direction how we can do full disk encryption better on general Linux distro. Every single component of the boot process and OS needs to be authenticated, i.e. all of shim (done), boot loader (done), kernel (done), initrd (missing so far), OS binary resources (missing so far), OS configuration and state (missing so far), the user’s home (missing so far). Very interesting read.

Component composition is undoubtedly where React shines. Being able to freely compose larger views from smaller, flexible building blocks is great. But you have to be careful because it’s easy to get it wrong (or at least suboptimal). By Tomasz Gil.

Containment is a concept where components don’t know their children ahead of time. They are simply containers for other elements. This is especially common for components like Sidebar or Dialog that represent generic “boxes”.

The article then dives into example component code and explains:

• Blurred responsibility
• Longer path to understanding the code
• Poor performance
• Hard to extend and reuse
• What’s containment in React

Simply by moving the component up we solved all problems we’ve identified - made the code easier to reason about, more flexible, and performant. Composition is a really powerful mechanism, but you have to be cautious to do it right. Good read!

Build your own machine and install KNIME, Jupyter-Notebook and Tableau to be fully equipped for all data science and deep learning tasks. By Dennis Ganzaroli, Data Scientist and Head of Report & Data-Management in a big Telco in Switzerland.

Do you want to get started with data science but lack the appropriate infrastructure or are you already a professional but still have knowledge gaps in deep learning? Then you have two options: Rent a virtual machine from a cloud provider like Amazon, Microsoft Azure, Google Cloud or similar. Or build your own physical machine and install the right software.

The author tried both options but in the end the decision to build his own rig was the better one, and these are the reasons why:

• Costs savings
• More power and resources
• Your machine can also be used for other tasks

A study from Bizon-Tech shows that a pre-build using 1 GPU is up to 10 times cheaper and those with 4 GPUs are up to 21 times cheaper within 1 year compared to web-based services. And when it comes to storage capacity, prices for web services go through the roof above a certain size.

The article also covers:

• Choosing the right system and software
• Building the machine
• Installation of the software

We should strongly go for a NVIDIA graphics card since all current state of the art frameworks (be it Keras, TensorFlow, PyTorch or any other library) fully support NVIDIA’s CUDA SDK, a software library for interfacing to GPUs. Another important point are Tensor Cores. Tensor Cores accelerate matrix operations, which are foundational to deep learning, and perform mixed-precision matrix multiply and accumulate calculations in a single operation. Excellent source of information for personal computing builders!

In this article we’ll explore the world of messaging APIs and protocols. We’ll identify why developers depend on them and focus on the capabilities of some of the most commonly used messaging APIs and protocols. By Callum Jackson, Richard J. Coppen.

A protocol is just a set of rules that computers follow, allowing them to interact with each other, and with the outside world, in some predictable, deterministic way. At the lowest level, multiple protocols are being employed right now to transmit this text to your browser. Most developers never need to worry about this low-level stuff, and most of the time it is completely transparent to the end user. This is only because both ends have agreed – or maybe assumed – that a particular convention is being followed to share data. As in this case, your browser was expecting HTTP or HTTPS protocol over a TCP/IP socket to deliver some HTML-encoded data that creates some human-readable web content.

The article also mentions:

• Enterprise messaging
• Enterprise messaging protocols
• Choosing your enterprise messaging APIs and protocols
  • Proprietary Protocols
  • MQTT
  • AMQP
  • STOMP

In this article we explained the difference between messaging APIs, protocols, and clients. Easy read!

TLS, or transport layer security, and its predecessor SSL, which stands for secure sockets layer, are web protocols used to protect and encrypt traffic over a computer network. By Brian Boucheron and Jeanelle Horcasitas.

A self-signed certificate will encrypt communication between your server and any clients. However, because it is not signed by any of the trusted certificate authorities included with web browsers, users cannot use the certificate to validate the identity of your server automatically. A self-signed certificate may be appropriate if you do not have a domain name associated with your server and for instances where the encrypted web interface is not user-facing. If you do have a domain name, in many cases it is better to use a CA-signed certificate. You can find out how to set up a free trusted certificate with the Let’s Encrypt project.

The article describes step by step process:

• Creating the SSL certificate
• Configuring Nginx to use SSL
• Adjusting the firewall
• Enabling the changes in Nginx
• Testing encryption
• Changing to a permanent redirect

With TLS/SSL, servers can send traffic safely between the server and clients without the possibility of the messages being intercepted by outside parties. The certificate system also assists users in verifying the identity of the sites that they are connecting with. Good read!

Few Enterprise IT departments would dare select something else than AWS, Azure or GCP as their “infra baseline”.The Big three were careful to provide similar cloud offerings, but different enough so that regardless of which one you would choose, you would be signing off for a vendor lock-in from day one. By Veselin Pizurica.

Enterprise with an open API that is based on software components and services that are compatible with all clouds. Still, many people would argue that the lock-in is not as bad as it sounds. And that’s a fair point.

When making the vendor selection, big enterprises always ask themselves questions along these lines:

• Do we care about portability?
• Do we ever consider moving from one cloud to another?
• Are we required to provide solutions across different cloud platforms?
• Is there a strategic aspect we need to consider to be able to run our solution on-prem, or on custom clouds where being agnostic is important?

If the answer on any of these questions is affirmative, enterprises still need to go over “the build vs. buy” decision. Thousands of blogs and articles were written on how to build an automation stack on top of kafka, mongo, etc.. It all sounds very simple till it isn’t. To learn more follow the link to the full article. Very interesting read!

Every single company is looking for a chance to leverage breakthrough innovation to transform the market. We get inspired by companies such as Apple, Amazon, and Tesla, all of which have utilized cutting-edge technology or business models to achieve breakthrough innovation. By Heather Bakire.

From its name, it sounds like breakthrough innovation happens by chance, a sudden breakthrough that changes everything. But breakthrough innovation hardly ever happens overnight. Instead, it’s the result of careful planning and commitment to the long haul. In order to give your company the best chance at achieving breakthrough innovation in your industry, you need to strategize on how to build an innovation framework that takes into account your company’s goals and resources.

The article then covers:

• The four types of innovation
• Building an innovation framework for breakthrough innovation
  • Identify a problem
  • Propose solutions to the problem
  • Choose the best solution
  • Scale the solution to reach breakthrough innovation

Once you have the groundbreaking solution, it’s time to make it the focus of your company. You’ll probably need to have supporting innovations to make the breakthrough. For example, the light bulb needed an electric grid for it to become a breakthrough innovation. Interesting read!

TensorFlow.js, TensorFlow’s JavaScript counterpart library for training, executing, and deploying machine learning models in the browser. In an attempt to make TensorFlow.js accessible to developers with limited or no ML experience, the library comes with several pre-trained models that work out of the box. By Juan De Dios Santos Rivera.

A pre-trained machine learning model is ready-to-use machine learning you don’t have to train. TensorFlow.js includes 14 that suit a variety of use cases.

The arrticle is split into these sections:

• Prerequisites
• Creating the app’s interface and importing the required libraries
• Predicting with the pre-trained model
• Pushing the App to GitHub
• Deploying the web application in DigitalOcean App Platform

In this tutorial, you have built and deployed a web application that uses a TensorFlow.js pre-trained model. Your Question and Answer web app takes as input a passage along with a question and uses a pre-trained BERT model to answer the question according to the passage. Good read!