DAS FEDERAL LLC

When Should You Choose Automation?

Posted on July 12, 2021

Automation is always assumed to be the most efficient option when it comes to tech. If you can automate a process, you can make it quicker. After all, it’s the uncomfortable truth that AI and robots can execute a lot of tasks a whole lot quicker than humans can.

Even in the case of testing, automation can be a fantastic tool. You can execute hundreds, even thousands, of tests at the click of a button. And with no margin for human error. But, it is important to know if automation is necessary or not in any given situation and it brings us to the following question:

When is the best time to choose automation?

The question was asked to tech professionals from across the globe and here’s what they said:

• When the cost makes sense: The bottom line is if you can save money and still deliver a quality product, cut costs where you can. That’s where automation comes into its own. But automation tools aren’t cheap, so the project size needs to be big enough to justify the cost. What’s more, the test needs to have the longevity required for the cost to make sense as well.
• When you are using repetitive tests: If you are running the same test again and again without changing it, the likelihood is it would be much more time-efficient to automate. That’s because a manual task being repeated regularly wastes your team’s valuable time, and is likely to lead to more errors due to lack of attention.
• When time will be saved: Every engineering and QA team needs more time. Especially when contending with lightning-quick SDLCs, any time-saving activity can have a huge impact on your team’s productivity.
• When quality is sure to be improved: Automation removes the possibility of human error. For that reason, in some scenarios quality can be dramatically improved by the use of automated testing. But you can also run hundreds of tests at once, meaning you will deliver a well-tested product that can be re-tested time and time again.
• When tests are run frequently: Having the capacity to run frequent tests at high volume just isn’t a possibility in many engineering teams. Manual testing can only go so far, especially for smaller teams with no in-house testing team. If you would like to scale up your testing capacity, speak to one of our growth experts today to see what we can do for your team.
• When you need to run multiple tests at once: Running the same manual tests simultaneously is no easy feat. The likelihood of a team having the capacity to run 100 tests at exactly the same time is low. Automation makes this task super-fast and enables teams to test rapidly without feeling the pressure.

Get started on automation with UiPath and DAS Federal, LLC

Automation definitely can improve quality, boost capacity and save your team time. UiPath has a vision to deliver the Fully Automated Enterprise™, one where companies use automation to unlock their greatest potential. UiPath offers an end-to-end platform for automation, combining the leading Robotic Process Automation (RPA) solution with a full suite of capabilities that enable every organization to rapidly scale digital business operations.

DAS Federal Partnered with UiPath aims to bring this AI Automation tool with robots to take care of your employees’ repetitive and time-consuming tasks to free them up for more important work and hence boost their performance and morale. For more information Contact: info@dasfederal.com

Machine-learning-based bio-computing (ML), Deep Learning (DL)

Posted on May 17, 2021

Machine learning (ML), especially deep learning (DL), is playing an increasingly important role in the pharmaceutical industry and bio-informatics. For instance, the DL-based methodology is found to predict the drug target interaction and molecule properties with reasonable precision and quite low computational cost, while those properties can only be accessed through in vivo/ in vitro experiments or computationally expensive simulations (molecular dynamics simulation etc.) before.

As another example, in silico RNA folding and protein folding are becoming more likely to be accomplished with the help of deep neural models. The usage of ML and DL can greatly improve efficiency, and thus reduce the cost of drug discovery, vaccine design, etc.

In contrast to the powerful ability of DL metrics, a key challenge lying in utilizing them in the drug industry is the contradiction between the demand for huge data for training and the limited annotated data. Recently, there is a tremendous success in adopting self-supervised learning in natural language processing and computer vision, showing that a large corpus of unlabelled data can be beneficial to learning universal tasks. In molecule representations, there is a similar situation. We have a large amount of unlabelled data, including protein sequences (over 100 million) and compounds (over 50 million) but relatively small, annotated data. It is quite promising to adopt the DL-based pre-training technique in the representation learning of chemical compounds, proteins, RNA, etc.

PaddleHelix, a machine learning-based bio-computing framework aimed at facilitating the development of vaccine design, drug discovery, and precision medicine. PaddleHelix is a high-performance ML-based bio-computing framework. It features large-scale representation learning and easy-to-use APIs, providing pharmaceutical and biological researchers and engineers convenient access to the most up-to-date and state-of-the-art AI tools. Code based on Python and C++. The possibilities are endless. Currently models include,

• Vaccine design
• Drug discovery
• Precision medicine

High Proficiency: PaddleHelix provide LinearRNA, a highly efficient toolkit for RNA structure prediction and analysis. LinearFold & LinearPartition achieve O(n) complexity in RNA-folding prediction, which is hundreds of times faster than traditional folding techniques.

Large-scale Representation Learning: Self-supervised learning for molecule representations offers prospects of a breakthrough in tasks with limited annotation, including drug profiling, drug-target interaction, protein-protein interaction, RNA-RNA interaction, protein folding, RNA folding, and molecule design. PaddleHelix implements various representation learning algorithms and state-of-the-art large-scale pre-trained models to help developers start from “the shoulders of giants” quickly.


Example: PaddleHelix provide frequently used components such as networks, datasets, and pre-trained models. Users can easily use those components to build up their models and systems. PaddleHelix also provides multiple applications, such as compound property prediction, drug-target interaction, and so on.

If you'd like to discuss more about ML & DL please reach out to info@dasfederal.com

Desma R. Balachandran
President
DAS Federal, LLC
info@dasfederal.com

Credits to publishing from:
MIT Technology Review
International Society for Computational Biology – Bioinformatics
GitHub Paddle Helix Opensource

Unlearning and Relearning with Digital Transformation

Posted on April 23, 2021

Digital Transformation across various industries accelerated in 2020 thanks to COVID-19. Not all industries adapted well to this as this transformation was more forced than a voluntary one. Although this digital transformation was quick and staggering, it provided us a chance to realize certain mistakes that were made and not to repeat them when redefining digitization in 2021. In 2021, Industries must unlearn the traditional ways and relearn how to optimize their business processes by integrating them with the leading technologies and embrace Digital Transformation. Factors that need to be focused on in 2021 for proper adaptation of Digital Transformation:

• Upskilling of Employees: Digital transformation can only occur with the support of a skilled workforce. During the pandemic, the sudden demand for digitization was not enough for industries to upskill their employees. In fact, unemployment sore higher than ever because many employees lost their jobs due to a lack of skill set necessary for the growing digitization. Learning from this, businesses must now constantly upskill their employees and invest in new training strategies to keep up with the rapid rate of digitization.
• Cybersecurity: With the pandemic, most businesses went online and thus the significance of Cybersecurity was highlighted. The shift to remote work ecosystems increased the data vulnerability across the web interfaces which led to the need for better security measures. Additional security measures should be incorporated using AI, digital identity verification, two-factor authentication, etc., to ensure cybersecurity.
• Automation: Automating Business Processes with Robotic Process Automation (RPA) should be adopted in industries for doing repetitive and mundane tasks rather than handing them over to employees. Using advanced automation technologies, businesses can have further growth and be more efficient. Businesses must invest more in Artificial Intelligence technologies which were highly adapted for a lot of applicational usage in the tech industry during the pandemic.

If you’re looking to Automate, provide CyberSecurity or Training for your Agency, then look no further. DAS Federal (Digital Applied Solutions) provides these High-Quality Services and will help your Agency take the next step in Digital Transformation. For more information contact: info@dasfederal.com

Streamlining your business Operations with NoOps

Posted on March 3, 2021

Traditional IT operations (ITOps) is the process of designing, developing, deploying and maintaining the infrastructure and software components of a specific product or application.

DevOps is a combination of practices and tools designed to increase an organization’s ability to deliver applications and services faster than traditional software development processes. DevOps is the beginning of a bigger, business-critical journey towards a more automated future through AIOps and, ultimately, NoOps.

AIOps introduces data science into the operating model by learning the behaviour of the systems, scaling according to the needs of a platform. Here, the concept of machine learning is introduced to expand the functionalities of DevOps.

Now, as part of a growing trend, CIOs are taking their automation efforts to the next level with serverless computing. In this model, cloud vendors dynamically and automatically allocate the compute, storage, and memory based on the request for a higher-order service (such as a database or a function of code). In traditional cloud service models, organizations had to design and provision such allocations manually. The end goal: to create a NoOps IT environment that is automated and abstracted from underlying infrastructure to an extent that only very small teams are needed to manage it.

NoOps is a fully automated, cloud-based approach that eliminates the operations team, aside from an integrated team focused on life cycle. The self-managed system requires resiliency and a high level of maturity with fail points that have already been addressed.

NoOps shouldn’t be considered similar to outsourcing of IT operations. NoOps is not a platform to be bought. It’s not about moving to SaaS or the cloud and expecting those vendors to run operations. It isn’t about a single technology play, either. It’s not the same as serverless technology, containers, Kubernetes or microservices but, these all do play a role in moving an IT shop further toward NoOps.

NoOps will also provide quick learning on drawbacks, customer and product needs. However, moving toward a self-maintained system requires upkeep for advances and innovation.

NoOps requires multiple technologies and more importantly a reworking of IT processes and workflows where automation, machine learning and even artificial intelligence eliminate not only repetitive tasks but higher-level tasks that workers now handle.

With the level of automation that NoOps provide, CIOs can then invest the surplus human capacity in developing new, value-add capabilities that can enhance operational speed and efficiency.

The future market for NoOps:

Like the other aspects of NoOps, cloud migrations might reduce operations requirements. But it’s probably because other areas of IT aren’t built with the NoOps goal: Hardware failures still aren’t considered a developer’s responsibility; engineers aren’t usually responsible for programming errors.

This all begs the question: Is NoOps designed to get the rest of the IT department to do things in the interest of IT operations, rather than leaving it as a problem for operations to resolve? And if so, shouldn’t we have been doing that all along?

Several cloud providers currently offer serverless platforms that can help enterprises move ever closer to a NoOps state. Amazon, Google, and Microsoft dominate today’s serverless market, but Alibaba, IBM, Oracle, and a number of smaller vendors are bringing their own serverless platforms and enabling technologies to market as well.

If you want to chat about this subject or any other thought provoking one, please get in touch with us at: info@dasfederal.com

Everything-as-a-Service (XaaS) Solutions Continue to Transform IT Enterprises

Posted on February 28, 2021

Organizations aim for more control over the enterprise IT they use and how they pay for it — and that demand has been met by a shift to service-based delivery models for providing products, capabilities, and tools.

What is XaaS? XaaS or Everything-as-a-Service is a new offering associated with the designation “as-a-Service” i.e., a service available through the internet using digital devices upon request. Unlike SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service), Everything-as-a-Service is not confined to digital products alone. You can get everything, from food to medical consultations, without leaving your home or office, by utilizing certain online services. Hence the “Everything” is in the name.

With the Pandemic still a threat, the needs for cloud and Everything-as-a-Service (XaaS) solutions have been intensified. But as XaaS gains momentum, competitive advantage may be threatened; adopters should leverage XaaS for advanced or emerging technologies to stay a step ahead. The use of XaaS to boost business agility, in addition to reducing costs and improving workforce efficiency, will continue to strengthen.

XaaS has also helped most of the traditional IT industries to create new business processes, products and services, and business models. It has even changed how we sell to our customers.

The future market for XaaS:

The combination of cloud computing and comprehensive, high-bandwidth, global internet access provides a productive environment for XaaS growth.

Some organizations have been hesitant to adopt XaaS because of security, compliance, and business governance concerns. However, service providers increasingly address these concerns, allowing organizations to bring additional workloads into the cloud.

Leading the way in technology, DAS Federal (Digital Applied Solutions) specializes in providing high quality Information Technology services and consulting to US Federal Agencies. For more information Contact: info@dasfederal.com