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IndustryVoice: Its Time To Rethink Your Business: Incorporating Drones Into Your Solar Energy Operations
2021-07-28 20:00:00| OGI
Solar companies are realizing the benefits drones are bringing to their operations.
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Smart farms: applying new technology to your barns
2021-07-27 16:05:00| National Hog Farmer
Take incremental improvement opportunities on your operation as they come along.
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What can AI and machine learning do for your business?
2021-07-19 20:25:41| The Webmail Blog
What can AI and machine learning do for your business? nellmarie.colman Mon, 07/19/2021 - 13:25 As businesses look for new ways to stay ahead of the curve, theyre turning to AI and machine learning. But whats realistic, and whats just hype? And how can AI and machine learning actually benefit your business? In our latest Cloudspotting podcast episode, hosts Alex Galbraith and Sai Iyer, who are both Solutions Architects at Rackspace Technology, are joined by AI & U podcast host Mark McQuade, Practice Manager, Data Science and Engineering at Rackspace Technology to discuss AI and machine learning. Tune in to hear about the following topics: How the data explosion is helping businesses Exploring the meaning of AI and machine learning AI capabilities for business use, for example churn prediction Using chatbot technology to increase business efficiency Advice on getting started in a career in AI and machine learning How edge technology improves smart device performance The benefits to humanity of data democratization Sai begins by describing how businesses are exploring the opportunities to monetize with data. We've had discussions with customers talking about the explosion of data and data analytics. We've had customers asking us, how can they enhance their products? How can they expand their applications? And how to use predictive programming? With businesses changing how they use data, Mark explains what this looks like as he defines AI and machine learning. Machine learning is a subset of AI, and deep learning is a subset of that. It's computers making predictions without being explicitly programmed to do so using historical data and maybe new data. What does AI mean to me philosophically? Its the possibilities of being able to do something that you could have never imagined doing 20 years ago. One particularly interesting area is voice communication, as Mark says, The next frontier of communication is voice. Everyone's using an Alexa, Google Home or a voice enabled device. Voice is becoming readily available and that's the way the world is going with everything being voice enabled. Another area that is gaining traction is edge technology, with Alex sharing how he is securing his home. I've recently put in a camera system but I didn't want all my footage sent up to the cloud, or permanently streaming. ML is built into my cameras so it recognizes vehicles and people. Pre-processing at the edge means keeping only the data points you need. That is far more valuable and cost effective in the long run. Mark goes on to explain how Rackspace Technology used machine during the pandemic. We used data sources that were freely available on the internet, like mobility data, to predict hospitalizations and deaths in New York State. We outperformed the Institute for Health Metrics and Evaluation (IHME) modelling. We were doing something for the better, the right kind of good. So that was extremely exciting. Alex elaborates on the theme of humanity benefitting from technology. The democratization of data is really key. Insights can allay people's fears. A really simple example, my mum lives on the west coast of Scotland and was concerned about the local COVID rate spiking. My brother went to the Scottish Government website to show her the data and show her that there is nothing to worry about. "For us, as businesses or organizations, to get insights from data and share that with the wider world, and for people to take advantage of that information is just fantastic. I think it's something we're probably going see more of. People will become their own data scientists at home." What can AI and machine learning do for your business?From edge technology to chatbots, there are many ways your business can use AI and machine learning to do more.Learn MoreCloud InsightsChris SchwartzWhat can AI and machine learning do for your business?July 19, 2021 Teaser FlagBlogInsights ImageRackspace-Blog-Image-Cloudspotting-AI-ML-ITT-TSK-5260-480x360.jpgSolutions TaxonomyArtificial Intelligence & Machine Learning
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Wastequip WRX Provides Utility and Convenience To Your Fleet
2021-07-06 21:12:00| Waste Age
Last year, Wastequip, the leading North American provider of end-to-end waste equipment, introduced Wastequip WRX, a turnkey center for installation, parts and service for waste-handling equipment. Wastequip WRX (pronounced Wastequip works) s
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Speeding up the machine learning lifecycle to get more from your data
2021-07-02 21:20:27| The Webmail Blog
Speeding up the machine learning lifecycle to get more from your data nellmarie.colman Fri, 07/02/2021 - 14:20 Businesses are realizing the value of using machine learning models to drive better outcomes. Harnessing the predictive power of your data with machine learning models is becoming more critical to business operations, yet 60% of machine learning models never make it to production. Where is it all going wrong? Widespread struggles with AI and machine learning We conducted a global study in December 2020 and January 2021 on AI and machine learning adoption, usage, benefits, impact and future plans. The study surveyed 1,870 IT leaders in various industries across the Americas, Europe, Asia and the Middle East. The study revealed that the majority of respondents (82%) are still exploring how to implement AI or struggling to operationalize AI and machine learning models. The research also showed that, on average, companies have four AI and machine learning R&D projects in place and we know from speaking to customers that most organizations are investing in research and development into model development. However, the disconnect between the operations or data ops teams and the machine learning engineers or data science teams means that many of the models never make it to production. There are often issues around deployment, automation and scalability of machine learning models. The challenges of operationalizing machine learning models Data science teams often face challenges in how they manage models as they pass through different stages of the machine learning workflow. Getting machine learning models swiftly from a development environment to production is not an area of expertise for data scientists. A DevOps or infrastructure team would be better equipped to deliver on the reproducibility of models and predictions. It can be difficult to reproduce a models output when moving it from one environment to another as it requires careful tracking of library versions, data sets, diagnostics, performance monitoring and model drift. Another common problem is that models tend to multiply into different environments and become difficult to keep a track of. Data scientists create domain-specific models and run many experiments, first starting in a development environment, and then moving them along the chain into a testing environment. This results in multiple models running simultaneously across different environments, using different data sets and different hyperparameters. So this makes it almost impossible to track a model's lineage. One of the most important aspects of governance and regulatory compliance (especially if you're dealing with any kind of auditors) is tracking and explaining everything your model is doing or has done. DevOps is not enough The DevOps culture and application lifecycle management have become a standard in the IT industry over the last decade. It emerged to fill the gap between an organization's ability to develop application code and the way to efficiently deploy, test, scale, monitor and update workloads. Mature CI/CD pipeline needs are largely addressed in application development by standardized tools and best practices that are already in place. Unlike application development, where quality comes from the code itself, the quality of a machine learning model comes largely from the data features used to train it. The importance of these data features cannot be understated as their quality drives your machine learning models performance. And it's worth mentioning that machine learning models are still in their operational infancy. Additionally, data might change daily, and data that was used for predictions that you did for today might be significantly different from the data used for model training a month ago. In this case, the production model needs to be retrained and go back into the development phase. So as a result, a machine learning models lifecycle is significantly different from an application lifecycle. We had a customer in the fraud space who wanted to push production models every 24 hours to account for new threats. The customer would retrain and redeploy their model every day to be able to account for any drift in data. That's impossible to do without a mature solution in place. Introducing the Model Factory Framework The machine learning lifecycle is complex. There are many steps to an entire machine learning lifecycle such as data ingestion, data analysis, data transformation, data validation, data splitting, model building, model training and model validation. And with all these steps there are associated challenges. This is why we developed the Rackspace Technology Model Factory Framework. The Model Factory Framework is built on AWS, using open source tools that enable rapid development, training, scoring and deployment models. The Model Factory Framework was built to address any problems you face when taking machine learning models from development to production. The Model Factory Framework simplifies the whole machine learning lifecycle which usually has over 25 steps and can take months to 10 or so steps which can be completed within a matter of weeks. Learn more about 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 view our webinar, Automating Production Level ML Operations on AWS. In this webinar we'll cover: Introduction to MLOps Foundations powered by Model Factory The gap between the Data Scientists and ML Operations The distinction between MLOps and DevOps Architecture patterns necessary for elements of effective MLOps How a model factory architecture holistically addresses CI/CD for ML Speeding up the machine learning lifecycle to get more from your dataDiscover how a model factory framework can simplify your entire machine learning lifecycle, cutting the time required from months to weeks. Automating Production Level ML Operations on AWShttps://www.brighttalk.com/webcast/17680/463764Watch the webinar
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