Nexus is a repository manager functions as stores, organizes, and distributes Artifacts needed for the development.

This tool is one of the finest ways to continuously control binary, packages and build Artifacts. But this repository is not something like a Git repository where you store the source code, this repository is basically for packages, binaries that you build, like apt packages, yum packages, android packages, NPM packages, Docker packages, etc.

There are two types of version one is the community version and 2^nd is the pro version which you called a professional version. The professional version is for paid and the enterprise community version is free with very limited functions.

From Nexus, developers can fully control access to, and deployment of, every Artifact in a company from a single location, making it easier to allocate software.  Internally, you configure your build to issue Artifacts to Nexus, so they come out there to rest developers. Nexus Repository helps organizations build better, safer software even quicker for free. It’s the only repository manager with free support for all popular formats, like Java, Docker, JavaScript, Ruby, Python, NET, GitLFS, etc.

Why Nexus is used for?

Basically, it is repository management and is used to store any kind of packages.  But there is another use of nexus as well, which is the availability of packages.

When you store the packages in the tools like nexus, they will be available to the entire team. Because of this coordination time is reduced.

Now the security of the packages is very important here. Security of packages means only a few persons should be having the right access or few CI tools. Most of the times CI tools will have the right access to make the nexus. But the rest of the people will not have the right access because the security of the package is also a major concern.

Now if you look at the kind of places where we deploy our applications, most of the applications we are using everywhere are cloud-based applications like making my trip, yatra.com, Swiggy, Ola, FB, etc, software as a service, so security is very important.

If one wrong package gets deployed into the system, operations, production, which will create a lot of problems so not only the support security and runtime security is important but package security is also important.  So that’s one of the reasons where we can use nexus where the security can be enforced.

Storage over the period of time will die but if you store the packages in tools like nexus and Artifactory, etc. they will make sure that the packages they store in a sort of object format, they maintain the integrity of packages, so that way it will not get corrupted over the period of time, so that is a thing which will be taking care by tools is nexus. 

What are the benefits of nexus?

Given below are few points on the benefits of nexus are–

• Speedier Builds

• Your own bandwidth savings

• Predictability and Stability

• Ability to deploy 3rd-party Artifacts

• Control and Auditing

• Ability to Host Internal Repositories

• Ability to Host Public Repositories
• Easy administration and management

Who should enroll for Nexus Certification Course?

The person who wants to be a professional on nexus tool and wants to learn what and how repository manager works as nexus and to take the advantage of nexus advanced features who let a user search, use fields, use lookups, and create basic statistical reports and dashboards in the Nexus Intermediate Enterprise or Nexus Intermediate Cloud Platforms.

 It enhances the value of your resume and you can get leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after the successful completion of training and practical-based projects.

What are the alternatives of Nexus?

Given below are the few tools which is an alternative to Nexus.

• Archiva
• Nuget gallery
• PopScrap Pro
• ScrapRight
• ScrapYardPro
• RecyclerGuard
• RazorERP
• RecycleManager
• Makor ERP
• Waste Logics
• Waste & Recycling One
• JFrog Artifactory.
• CircleCI.
• Postman.
• GitHub.
• Amazon Elastic Container Registry (ECR)
• Jenkins.
• CloudBees CI.
• Github Package Registry.

What are the pre-requisites to learn Nexus

      For essential nexus training

• Basic understanding of Linux/Unix system concepts
• Familiarity with Command Line Interface (CLI)
• Familiarity with a Text Editor
• Experience with managing systems/applications/infrastructure or with deployments/automation

For intermediate nexus training

• An understanding of basic marketing concepts.
• Should have a keen interest in engaging in social media constructively.
• Enthusiasm and commanding communication skills

For advanced nexus training

• Basic knowledge of Elastic search/Nexus and would like to expand on their knowledge
• Basic Knowledge of JSON
• Basic of Linux and windows

How to learn Nexus?

Through a certified course and training from well-trained trainers and from a popular and well-known platform like DevOpsSchool which makes the trainee is a perfect use of this Nexus tool. 

DevOpsSchool is the best supply of Nexus courses, training, and certification. Their Nexus trainers and specialists are highly educated with a good rich experience in the Software industry. DevOps has captured a stimulating position within the IT world and has become an important part of software engineering methodology. For enhancing the software quality, reducing cost, and increasing the agility for a quicker release, DevOps and Nexus is playing a very important role therefore finding the best Nexus trainers and training seller is very necessary.

The Nexus courses are designed in certain methods where participants can get advantages with a lot of content in less span of time. DevOpsSchool gives its services of Nexus training and courses online and classroom mode in India and abroad. DevOpsSchool tends to run several public classrooms training programs within Bangalore, Hyderabad, Pune, Mumbai, India, Netherlands, etc.

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A new artificial intelligence framework could speed the development of vaccines to treat new COVID-19 mutations.

An artificial intelligence method can help combat emerging COVID-19 mutations by identifying the best potential vaccines to treat the virus, according to a study published in Scientific Reports. 

As COVID-19 begins to mutate in populations globally, scientists are concerned that the mutations will minimize the effectiveness of vaccines that are now being distributed. Recent variants of the virus in the UK, South Africa, and Brazil seem to spread more easily, which have the potential to lead to more hospitalizations and deaths. 

Researchers from the University of Southern California (USC) Viterbi School of Engineering set out to develop a new artificial intelligence method to combat emergent mutations of COVID-19 and accelerate vaccine development. 

The team used data from a bioinformatics database called the Immune Epitope Database (IEDB), in which scientists around the world have been collecting data about the coronavirus and other diseases. 

IEDB contains over 600,000 known epitopes from some 3,600 different species, along with the Virus Pathogen Resource, a complementary repository of information about pathogenic viruses. 

The newly-developed artificial intelligence method is designed to speed the analysis of vaccines and focus on the best potential preventive medical therapy. 

The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified. This can give humans a significant advantage over evolving mutations, with the model accomplishing vaccine design cycles that once took months or years in a matter of seconds or minutes. 

When applied to the virus that causes COVID-19, the AI tool quickly eliminated 95 percent of the compounds that could have possibly treated the pathogens and identified the best options.

The AI-assisted method predicted 26 potential vaccines that would work against coronavirus. Of these, scientists identified the best eleven from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell.

Vaccines target the region, or epitope, of the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate.

“This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety,” said Paul Bogdan, associate professor of electrical and computer engineering at USC Viterbi and corresponding author of the study. “Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world.”

With the new AI framework, the team can also construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour. In comparison, current processes to control the virus require growing the pathogen in a lab, deactivating it, and injecting the virus that caused a disease. The process can take more than a year as the virus continues to spread.

Researchers expect that if the virus causing COVID-19 becomes uncontrollable by current vaccines, or if new vaccines are needed to deal with other emerging viruses, their AI method can help develop other preventive mechanisms quickly.

For example, researchers in the study used only one B-cell epitope and one T-cell epitope, whereas applying a bigger dataset and more possible combinations could help develop a more comprehensive and faster vaccine design tool. 

The study estimates that the model can perform accurate predictions with more than 700,000 different proteins in the dataset.

“The proposed vaccine design framework can tackle the three most frequently observed mutations and be extended to deal with other potentially unknown mutations,” Bogdan said.

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KPMG in India has entered into an alliance with Analytics Vidhya, one of the world’s leading data science community and knowledge portals to deliver a range of virtual sessions on multiple data science tools and technologies.

As per KPMG’s India CEO Outlook 2019 Report, 89 percent of CEOs in India agree that data scientists are the most effective group of their workforce, among all other categories. With more advanced predictive analytic tools and technological innovations in the future, CEOs in India should be able to strike a good balance and combine data-driven analytics with their intuitions to identify new growth opportunities.

This indicates that organisations are heavily focusing on building and nurturing talent for data science and AI, as most of these future-oriented skills are in short supply and high demand. Therefore, KPMG in India and Analytics Vidhya would be together launching various courses that will be designed for working professionals and students who aspire for Data Science, Machine Learning and AI careers. KPMG in India will conduct industry-oriented sessions on data science & machine learning that would include industry /function specific use cases.

“Data Science technologies can give organisations the ability to fully capitalise on the use of the data they generate every day around them. We are thrilled to collaborate with Analytics Vidhya, where we will jointly offer learning programmes that are a mix of Business and Data/AI technologies. This will be a differentiated offering as KPMG in India brings a strong practitioner’s view on how some of these technologies are getting adopted and impacting business growth, efficiencies and risks. With the combined skillsets of both – business and technology, one is well positioned for lucrative work opportunities,” said Sachin Arora, Partner, IT Advisory, KPMG in India

“I am very excited to announce our collaboration with KPMG in India. Data Science and Data Engineering are the fastest growing technologies today and our collaboration with KPMG in India would take us a step forward in building the next-generation data science ecosystem in India. Through this collaboration, we will be able to offer highly industry-relevant content and best industry practices to our community and data science professionals across the globe.” said Kunal Jain, Founder and CEO, Analytics Vidhya

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Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning tool that can adapt when and how often it defers to human experts based on factors such as the expert’s availability and level of experience.

In healthcare, the use of artificial intelligence and machine learning has become more widespread, but experts have continuously cautioned against the use of advanced, autonomous tools in patient care. The industry has taken a hybrid approach to AI, developing tools that can work in tandem with human physicians and help make more informed clinical decisions.

The challenging part of these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isn’t always a question of who does a task better, because if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help.

Dig Deeper

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CSAIL researchers trained a machine learning system on multiple tasks, including looking at chest X-rays to diagnose specific conditions such as lung collapse and an enlarged heart. In the case of an enlarged heart, the team found that their human-AI hybrid model performed eight percent better than either could on their own.

“In medical environments where doctors don’t have many extra cycles, it’s not the best use of their time to have them look at every single data point from a given patient’s file,” said PhD student Hussein Mozannar, lead author of the study. with David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science.

“In that sort of scenario, it’s important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary.”

The system has two parts: a classifier that can predict a certain subset of tasks, and a rejector that decides whether a given task should be handled by either its own classifier or the human expert. Through experiments on tasks in medical diagnosis and text and image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and far fewer training data samples.

“Our algorithms allow you to optimize for whatever choice you want, whether that’s the specific prediction accuracy or the cost of the expert’s time and effort,” said David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science.

“Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa.”

The team has yet to test the system with human experts, and instead developed a series of synthetic experts so that they could tweak parameters such as experience and availability. In order to work with a new expert it’s never seen before, the system would need some minimal onboarding to get trained on the person’s particular strengths and weaknesses.

Going forward, the team plans to test their approach with real human experts, such as radiologists for X-ray diagnosis. Researchers will also explore how to develop systems that can learn from biased expert data, as well as systems that can work with several experts at once. For example, in a hospital setting the machine learning tool could work with different radiologists who are more experienced with different patient populations.

“There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability,” said Sontag, who is also a member of MIT’s Institute for Medical Engineering and Science. “We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms.”

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Microsoft announced July 8 a new artificial intelligence tool for its cloud platform Azure that allows developers to analyze unstructured medical data, including clinical notes, clinical trial protocols and medical publications. 

Microsoft’s Text Analytics for health allows researchers, data analysts and medical professionals to detect words and phrases from unstructured text and connect them to relevant healthcare and biomedical concepts, such as diagnoses, medication names and treatments. Users can extract more than 100 types of personally identifiable information, including protected health information, in unstructured text. 

The AI tool also links entities to medical ontologies and coding systems, such as the Unified Medical Language System, to detect connections between medical concepts mentioned in text. For example, the tool can be used to find the relationship between a medication name and the dosage associated with it.  

Medical researchers and data analysts can use the tool to create analytics on historical medical data, develop prediction models and match patients to clinical trials. Seattle-based Allen Institute for AI used it to develop a COVID-19 search engine allowing researchers to more quickly analyze coronavirus information. The institute, founded by Microsoft co-founder Paul Allen in 2014, partnered with the University College London to review medical research reports. 

“We have been partnering with engineers at Microsoft and data scientists to build a ‘living’ reviews system – that automatically identifies relevant research for reviews as they are published,” James Thomas, a professor at University College London, stated in a news release. “Text Analytics for health provides a powerful tool for extracting insights from clinical literature, with rich support for a wide range of healthcare terminology so that we can more quickly and accurately identify relevant information.” 

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TLDR: Python is one of the coding languages any programmer needs to know, so the Coding with Python Ultimate Training can be the guidebook for anyone to help pick it up quickly.

Working from home is great for convenience, but with so many potential distractions, it isn’t always the best for productivity. So if you’re going to spend all this time at home learning something new, it stands to reason you not only want to develop a skill that’s useful, but also one that isn’t overly complicated that you can process fairly quickly.

Many developers will tell you that description fits Python programming to a T. Python is versatile, straight-forward and about as simple to learn as one of the world’s most powerful coding languages could ever hope to be. 

Right now is the perfect time to understand Python and all its applications with the training in the Coding with Python: The Ultimate Training for Aspiring Developers Bundle. It’s currently $49.99, over 90 percent off from TNW Deals.

Over six courses including over 90 hours of instruction, you’ll dive into Python and get the full spectrum experience, everything from ground-level basics for the first timers, progressing all the way up to hands-on training with advanced Python coding techniques that can ultimately help you code to the language’s fullest potential.

The Complete Python Course: Learn Python by Doing and Build 8 Python Web Apps start students the right way, introducing fundamental Python commands as you start working on actual real-life app projects like building a blog site with registration requirements as well as a web store price tracker.

A pair of courses covering REST APIs also delve into the mechanics of app construction using simple tools like Flask. Meanwhile, there’s also training in GIT to track any and all potential coding changes problems; and tactics for automating the testing on your Python builds to make sure you get everything right the first time.

The full collection of coursework usually retails for almost $700, but by getting in on this offer now, the entire package is available for only $49.99 while this deal lasts.

The post Python is a tool you need in your developer skillset, and this 6-course package can be your introduction. appeared first on Artificial Intelligence.

December 24, 2019 – Researchers at Purdue University have created a new framework for mining data to train machine learning models used in drug development.

Using machine learning for drug development requires researchers to create a process for the computer to extract needed information from a pool of data points. Drug scientists have to pull biological data and train the software to understand how a typical human body will interact with the combinations used to form medications.

Drug discovery researchers developed the Lemon framework, which helps drug researchers better mine the Protein Data Base (PDB), a comprehensive resource with more than 140,000 biomolecular structures and with new ones released every week.

“PDB is an essential tool for the drug discovery community,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science who works with other researchers in the Purdue Institute for Drug Discovery and led the team that created Lemon. 

“The problem is that it can take an enormous amount of time to sort through all the accumulated data. Machine learning can help, but you still need a strong framework from which the computer can quickly analyze data to help in the creation of safe and effective drugs.”

The Lemon software platform mines the PDB within minutes. The platform also allows users to write custom functions, include it as part of their software suite, and develop custom functions in a standard manner to generate unique benchmarking datasets for the entire scientific community.

The Lemon platform was originally designed to create benchmarking sets for drug design software and identify the lemons, or the biomolecular interactions that can’t be modeled well, in the PDB.

“Experimental structures deposited in PDB have resulted in several advances for structural and computational biology scientific and education communities that help advance drug development and other areas,” said Jonathan Fine, a PhD student in chemistry who worked with Chopra to develop the platform.

“We created Lemon as a one-stop-shop to quickly mine the entire data bank and pull out the useful biological information that is key for developing drugs.”

As machine learning becomes more integral to the healthcare industry, researchers have attempted to improve the accuracy and efficiency of these algorithms. Recently, a team at Penn Medicine discovered a once-hidden, through-line between two widely used predictive models that could increase the accuracy of machine learning tools. The discovery could expand the use of machine learning throughout healthcare and other industries.

“The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning,” researchers wrote in a study published in Proceedings in the National Journal of Sciences (PNAS).

“Despite recent efforts to formalize the concept of interpretability in machine learning, there is considerable disagreement on what such a concept means and how to measure it.”

Additionally, MIT researchers recently developed an automated system that can gather more data from images used to train machine learning models, including algorithms that can analyze brain scans to help treat and diagnose neurological conditions.

“We’re hoping this will make image segmentation more accessible in realistic situations where you don’t have a lot of training data,” said first author Amy Zhao, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and Computer Science and Artificial Intelligence Laboratory (CSAIL).

“In our approach, you can learn to mimic the variations in unlabeled scans to intelligently synthesize a large dataset to train your network.”

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Turns out people are not that great at tracking thousands of variables

American upstart Databricks, established by the original authors of the Apache Spark framework, reckons its open-source machine-learning management engine MLflow is ready for prime time.

The released version 1.0 of the platform focuses on core API components. It improves the handling of metrics and search functionality, and adds support for Hadoop as an artifact store, in addition to the previously supported Amazon S3, Azure Blob Storage, Google Cloud Storage, SFTP, and NFS.

It also adds an experimental Open Neural Network Exchange (ONNX) model flavour, and a CLI command for building a Docker image capable of serving an MLflow model.

And finally, there’s Windows support for the MLflow client – in the unlikely event data scientists decide to opt for something other than Linux.

MLflow enables data scientists to track and distribute experiments, package and share models across frameworks, and deploy them – no matter if the target environment is a personal laptop or a cloud data centre.

The company launched the alpha version of MLflow project last year at the Spark + AI Summit.

Multiple code approaches

The basic machine learning life cycle – taking raw data, preparing it, training your model and deploying it – is full of variables and fraught with complications. It can involve hundreds of different open source tools and frameworks, each with dozens of configurable parameters.

Facebook, Google and Uber have all built their own proprietary tools to deal with this complexity.

MLflow was designed to take some of the pain out of machine learning in organizations that don’t have the coding and engineering muscle of the hyperscalers. It works with every major ML library, algorithm, deployment tool and language.

One of the project’s goals is to improve collaboration between data scientists and engineers that deploy their creations in production.

In a true open source fashion, MLflow users didn’t wait for a stable release to start experimenting: Databricks says the platform has already been deployed at thousands of organizations to manage their machine learning workloads, and the company is offering it as a managed service.

Group effort

Databricks might have started the project, but today, it has more than 100 contributors, including a few from Microsoft.

“People are excited about having an open-source project in this space,” Mattei Zacharia, co-founder and chief technologist of Databricks, told El Reg last year.

“They’re excited about having an ML platform – it’s something that resonates with them, and that many wanted to build already – and having one that is a community effort will be much better than what any company could build on its own.”

The next major addition to MLflow will be a Model Registry that allows users to manage their ML model’s lifecycle from experimentation to deployment and monitoring.

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