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Gattefossé and BioMeca Develop a Model of 3D Dermal Microtissue
2021-03-15 18:47:10| Happi Breaking News
Study the elastic properties of skin in order to combat aging.
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Proposal for Funding Model for Auditors Office: Your Questions Answered
2021-03-11 21:00:07| PortlandOnline
The Auditor proposed a funding model to meet voters expectations from 2017 for a more independent Auditors Office. Council will hear the proposal Thursday, March 11, at 2 p.m., along with other items related to the Auditors Office budget.
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Oppo unveils Find X3 5G smartphone series with flagship Pro model
2021-03-11 13:42:00| Telecompaper Headlines
(Telecompaper) Oppo has unveiled the new Find X3 smartphone series. The flagship Find X3 Pro 5G smartphone and the more affordable Find X3 Neo 5G and Lite 5G models...
Operationalize machine learning with the Model Factory Framework
2021-03-10 16:19:53| The Webmail Blog
Operationalize machine learning with the Model Factory Framework nellmarie.colman Wed, 03/10/2021 - 09:19 Businesses increasingly rely on data to make decisions, as they attempt to re-create successes and avoid failures of the past. Traditionally, this means businesses have taken a reactive approach, where they make decisions for tomorrow, based on performance data from the past. But with machine learning, businesses can now harness their data to peek into possible future outcomes. From financial forecasting, churn prevention and predictive maintenance, to inventory management and simply identifying the next best action, machine learning is empowering businesses to make better-informed decisions. While machine learning is an incredibly powerful tool, implementing machine learning models for real-world application can be highly challenging. In fact, according to IDC, over a fourth of AI and machine learning initiatives fail. The culprits are multi-faceted: Lack of developer experience with machine learning Poor data quality and challenging operationalization Time-consuming processes, such as the need to repeatedly train new datasets Lack of a standardized set of best practices that integrate CI/CD, DevOps, DataOps and software engineering practices An abundance of tooling, processes and frameworks and data and operations teams that have their own, unique preferences In order to address these challenges and bridge the gap between teams, you need a standardized framework, agnostic of platform or tooling. The Model Factory Framework The Rackspace Technology Model Factory Framework is designed with all of these challenges in mind. It provides a coherent mechanism, so that your organizations data and operations teams can collaborate, develop models, automate packaging and deploy to multiple environments while preventing deployment delays, incompatibilities and other problems. Its a cloud-based machine learning lifecycle management solution an architectural pattern rather than a product. Also, since its open and modular, you can integrate it with AWS services and industry-standard automation tools such as Jenkins, Airflow, AWS CodePipeline for data processing. And given that the machine learning lifecycle is complex, with multiple building, training, testing and validation stages across data analysis, model development, deployment and monitoring the Model Factory Framework integrates Amazon SageMaker, an AI and machine learning services stack that includes: AI services that provide pre-trained models for ready-made vision, speech, language processing, forecasting and recommendation engine capabilities Machine learning services that provide pre-configured environments within which you can build, train and deploy deep learning capabilities into your applications The Amazon SageMaker stack also supports all the leading machine learning frameworks, interfaces and infrastructure options, for maximum flexibility. Key benefits of the Model Factory Framework The Model Factory Framework can help you cut the entire machine learning lifecycle from more than 25 steps, down to under 10. It further accelerates the process by automating handoffs between the different teams involved and by simplifying troubleshooting which it achieves by supplying a single source of truth for machine learning management. For data scientists, the Model Factory Framework provides a standardized model development environment, the ability to track experiments, training runs and resulting data, automated model retraining and up to 60% savings on compute costs through scripted access to spot instance training and hyperparameter optimization (HPO) training jobs in QA. For operations teams, the framework automates model deployment across development, QA and production environments. It also provides a registry for model version history tracking as well as tools for diagnostics, performance monitoring and mitigating model drift. For the organization, the framework provides a model lineage for governance and regulatory compliance, improves time to insights and accelerates ROI, while reducing effort to get machine learning models into production. Get started with the Model Factory Framework If you would like to learn about the Rackspace Technology Model Factory Framework in more detail and explore how it improves processes from model development to deployment, monitoring and governance download our whitepaper, Moving from machine learning models to actionable insights faster, where we explore: An overview of the machine learning lifecycle and its challenges How DevOps practices are misaligned to the machine learning lifecycle The Model Factory Framework overview, tools and processes How the Model Factory Framework cuts model deployment from 25 to as few as 10 steps Operationalize machine learning with the Model Factory FrameworkWhile machine learning is an incredibly powerful tool, implementing machine learning models for real-world application is tough. Discover how the Model Factory Framework addresses these challenges, while also accelerating the process reducing the entire machine learning lifecycle by 60%.Move from machine learning models to actionable insights, faster./lp/automating-production-level-mlops-aws-whitepaperDownload the whitepaper
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What is cybersecurity maturity model certification (CMMC) and why should you care?
2021-03-03 17:21:33| The Webmail Blog
What is cybersecurity maturity model certification (CMMC) and why should you care? nellmarie.colman Wed, 03/03/2021 - 10:21 If your organization works with the U.S. Department of Defense either as a prime contractor, subcontractor or supplier you need to prepare for major changes this year. Starting in 2021, some Department of Defense (DoD) contracts will require you to not only achieve a certain level of cybersecurity, but also have it certified by a third-party assessor. This certification program, known as Cybersecurity Maturity Model Certification (CMMC), will help ensure that everyone on a contract can keep the projects data secure. This is a shift from how the DoD has handled cybersecurity requirements in the past, when an organization often simply needed to self-attest that they were practicing essential cybersecurity hygiene. But given that cybercrime is predicted to cost $6 trillion globally in 2021, and continues to rise, these measures are not unexpected. So, how should you prepare? Although the DoD is still ironing out the details, you can start getting ready now by learning about the new requirements, getting to know the various certification levels and knowing who to reach out to for expert guidance. Who will need to be CMMC-certified? Its estimated that more than 300,000 organizations will require assessment and certification to one of the five CMMC levels. From small businesses providing HVAC maintenance to major defense contractors working on the newest military hardware, every member of the DoD supply chain will need to address CMMC. At the same time, that doesnt mean your entire organization needs to be compliant just the systems, processes and people involved in fulfilling the specific contract at the CMMC level in question. This is important, as the jump from Level 1 to Level 3 requires formal documentation and implementation of 113 additional security controls. However, the DoD estimates that most contracts will only require Level 1 certification. What are the CMMC levels? The level of compliance needed will vary based on the scope of work. So if you take the examples above, a business providing HVAC maintenance might just need to reach CMMC Level 1, while the company working on military hardware is probably looking at CMMC Level 5 certification. Lets take a brief look at what each level entails. Level 1: Basic cyber hygiene Safeguarding Federal Contract Information (FCI) At this level, you will be implementing basic cybersecurity best practices that every business should follow and that most suppliers have been required to follow since 2016, in accordance with FAR 52.204-21. For example, you will need to control and manage who has access to devices and data, establish strong password protections, implement firewalls, stay on top of software updates/patches and use antivirus protection. This helps protect Federal Contract Information (FCI), which is likely to be found in nearly every government contract. Level 2: Intermediate cyber hygieneTransitioning to protecting Controlled Unclassified Information (CUI) In Level 2, you will need to demonstrate that cybersecurity is not just practiced, but that you are effectively documenting, managing, reviewing and optimizing your practices. In doing so, youre preparing your organization to move ahead to Level 3. Level 3: Good cyber hygieneProtecting CUI Level 3 demonstrates that you are able to protect anything the government categorizes as CUI (i.e., information that is sensitive and not for public consumption, but not officially classified). It shows that you havent just implemented and documented the required cybersecurity practices, but that youre actively managing them as well. Level 4: Proactive cybersecurity Protecting CUI and reducing the risk of advanced persistent threats At this level, youre taking a more proactive approach to protecting the governments information. You will need to demonstrate that youre able to detect and respond to advanced persistent threats and adapt to their always-changing tactics, techniques and procedures. Level 5: Advanced/progressive cybersecurityIncreasing protection of CUI and further reducing the risk of advanced persistent threats Level 5 requires that you take a more advanced posture of proactive scanning and mitigation of advanced persistent threats standardizing and optimizing your processes, across your organization. While Level 4 can be more reactionary, Level 5 is more proactive. How can my business get CMMC certified? One of the keys to CMMC certification is the ability to break down and review your processes at every step, identify strengths and weaknesses, and develop remediation plans. If youve never done this before, reach out to an experienced Registered Provider Organization (RPO) who can help streamline your path to CMMC certification. The key is to reach out to an RPO early in the process, so you can avoid costly mistakes from the beginning and start with a strong foundation. As an authorized RPO, Rackspace Technology can help you achieve your certification faster, so you can remain competitive for DoD contracts moving forward. Were a leader in the government compliance enablement space, powering multiple FedRAMP and FISMA ATOs built on our managed service, and providing 24x7x365 hybrid-cloud management, operational support and security services as a packaged, on-demand, audited and pay-as-you-go service. Start your CMMC journey strong. Learn more about our CMMC certification services. What is cybersecurity maturity model certification (CMMC) and why should you care?Get to know about Cybersecurity Maturity Model Certification and how you can start getting ready now so you can remain competitive for Department of Defense contracts. Learn more about our CMMC certification services./compliance/cmmcGet started
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