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AI and machine learning are revolutionizing modern businesses heres how to get ahead

2021-02-04 22:08:23| The Webmail Blog

AI and machine learning are revolutionizing modern businesses heres how to get ahead nellmarie.colman Thu, 02/04/2021 - 15:08 Fierce competition means every business must adapt to succeed. AI and machine learning have emerged as modern, vital ways for organizations to get ahead. Many businesses today prioritize data, analytics and AI/machine learning projects to power new business models, enhance product and service offerings, improve efficiency, drive revenue and deliver superior customer experiences. But analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and machine learning projects will be successfully deployed in production by 2022. Less than a fifth will move piloted AI projects into production without delays caused by a range of problems from technical skills gaps and lack of IT/business process maturity, to insufficient organizational collaboration. For example, these businesses may not have expertise in mathematics, algorithm design or data science and engineering. Or their data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any organization looking to advance in the market and derive value from AI and machine learning. This combination of pressure and challenges can overwhelm your business, especially if youre at the start of your AI and machine learning journey. So lets dig into why your business should make the effort and how doing so might require different skills sets and data from what you might think. What are AI, machine learning and deep learning? Lets start with the basics. When a machine completes tasks based on a set of stipulated rules that solve problems, were into the realm of artificial intelligence. This might include understanding and interpreting natural language, recognizing when objects move and providing intelligent answers. Business benefits follow, such as analyzing data sets that are too large for humans to process, answering questions in real time that draw from existing data and experiences, and automation that can reduce costs and boost productivity. Machine learning is a discipline within the AI domain. It enables machines to learn by themselves using data. They use this knowledge to make increasingly accurate predictions and drive actions. For this to happen, you need a model thats trained on existing data, after which point it can process additional data and make predictions. Throughout the process, its important to track and understand your model, building quality and eliminating bias. Finally, deep learning is a subfield of machine learning. It structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own. The many use cases of AI and machine learning Weve so far explored AI, machine learning and deep learning in the abstract, but in what specific ways can they benefit your business? Answering questions, thereby improving customer support and buying journeys, through faster, higher-quality answers and experiences. Speech recognition, including text-to-speech and speech-to-text translation, enabling you to work with voice/audio data more widely and productively. Document summaries that effectively extract key concepts to use in countless ways, improving productivity and document data use. Image recognition for biotech, satellite/drone imagery and face recognition, to quicken emergency responses and prevent crime. Image processing to improve the presentation and utilization of images through enhanced resolution and colorization. Data classification in medicine, to yield better diagnoses, faster and more targeted treatments and health preservation. Superior search to get customers to what they want more rapidly, whether thats a product recommendation or a web page. Strategic analysis that can be a boon to the games industry, driving more challenging and educational entertainment. Financial and logistical forecasting to improve financial management, planning and resource allocation/utilization. AI and machine learning require skills and data, but not what you think If youre looking to machine learning and deep learning but have concerns about your existing data, be mindful that they dont always need massive data sets. While completely new models with no data nor training do require tens of thousands to millions of data points, trained models exist that can give a project leader a head start. Even if you have just 100 or so examples for a specific use case, building on a general models foundation could yield more accurate results than human experts would provide. Additionally, its worth thinking differently about hiring for the delivery of AI/machine learning enabled applications and solutions. Theres an assumption you need PhD-level data scientists. Although they do add value and can be necessary in some circumstances, existing staff can often be trained in about 100 hours, building on high-school math and a year of coding experience. With modern tools on AWS or Google Cloud including AutoML, they can build the solutions you need. In all, its as much about changing your mindset as anything else. You must think about what AI and machine learning can bring to your business and the most effective way to achieve that, thereby keeping your company ahead. Machine learning is today driving change in thinking of data as code where machine learning uses data to write the program, which is the output. This methodology coupled with the tools and education I mentioned earlier set the stage for many more people collaborating to fashion a new generation of intelligent solutions that will revolutionize business for years to come. For more information on AI and machine learning, check out our panel discussion, which dives deep into these topics. The discussion covers: toolsets and methodologies; capabilities and constraints; data, computer and expertise requirements; examples of successful applications; and how to get started. AI and machine learning are revolutionizing modern businesses heres how to get aheadDiscover why AI and machine learning are worth the effort, and why they require different skills sets and data from what you might think.Discover how businesses are using AI and machine learning.https://www.brighttalk.com/webcast/17680/420320Watch the presentation

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AWS re:Invent 2020 Recap: Machine Learning Keynote

2020-12-10 19:13:50| The Webmail Blog

AWS re:Invent 2020 Recap: Machine Learning Keynote nellmarie.colman Thu, 12/10/2020 - 12:13 While this years AWS re:Invent may be entirely virtual, AWS has not disappointed this Data Scientist in the slightest. The long list of new releases in AWS machine learning stack will undoubtedly benefit all users ranging from novices to experts in the field. During Tuesdays Machine Learning Keynote, Swami Sivasubramanian, VP of Amazon Machine Learning, structured his message around three tenets which, together, give builders the freedom to invent. Under each tenet, he announced the new releases for machine learning, while also explaining how they fit together with the events other announcements helping the audience weave together the bigger picture. Provide firm foundations The first tenet, Provide firm foundations, was the basis for the first announcement: faster distributed training with Amazon SageMaker. Using Habana Gaudi processors from Intel, AWS will soon offer EC2 instances built for machine learning (ML) training yielding a performance increase of up to 40% over current GPU-based EC2 ML training instances for training deep learning workloads. This helps provide a firm foundation upon which developers can build and deploy machine learning, faster, while also reducing costs. Create the shortest path to success The second tenet, Create the shortest path to success, got the audience (particularly football fans like me) excited, as Sivasubramanian shared how AWS and the NFL are collaborating to achieve game and player simulation that can predict, treat and ultimately prevent player injury. In the spirit of creating the shortest path to success for critical projects like this, AWS announced Amazon SageMaker Data Wrangler. This is a tool that Im particularly eager to experiment with, as AWS suggests its a huge time saver for data transformation and discovery. Im also pleased to see that Data Wrangler will soon integrate with Snowflake, MongoDB and Databricks historically, AWS required AWS databases to seamlessly leverage their tools. Another time saver is Amazon SageMaker Clarify, Amazons bias detection tool across the entire ML workflow. Not only does bias detection save time, if done well it improves overall model quality, flagging any drift that may occur as models age. The next SageMaker release that I plan to utilize as an education tool is model profiling for Amazon SageMaker Debugger. This capability maximizes resources for training, GPU, CPU, network and I/O memory, by analyzing resource utilization and then making recommendations on how to adjust. (Wow!) We also learned about Amazon SageMaker Edge Manager, a new feature that manages and monitors machine learning models across fleets of smart devices up to 25 times faster when compared to hand-tuning said models. Expand machine learning to more builders The third tenet, Expand machine learning to more builders, is a principle Im particularly passionate about. AWS has attempted to achieve this by releasing Amazon Redshift ML which Im enthusiastic to test out. Its crucial to be able to more-easily experiment and deploy machine learning models however, Im wary of the suggestion that certain experts are no longer required in the process. Selecting, refining and deeply understanding a model is fundamental to extracting the most value possible from the output. As a launch partner for Redshift ML, Rackspace Technology can help you make the most of this new feature: At Rackspace Technology we help companies elevate their AI/ML operations. Were excited about the new Amazon Redshift ML feature because it will make it easier for our mutual Redshift customers to use ML on their Redshift with a familiar SQL interface. The seamless integration with Amazon SageMaker will empower data analysts to use data in new ways, and provide even more insight back to the wider organization. Nihar Gupta General Manager for Data Solutions, Rackspace Technology AWS continues to pave the way for streamlined development and deployment. Im thrilled to see the increasing number of capabilities across platforms. As a Data Science consultant, I am constantly interacting with different frameworks and infrastructure. AWS is my go-to solution for model development and as systems are increasingly compatible, the more I can focus on model refinement. This years re:Invent is a three-week event, all virtual, and free. To watch the event live or view recordings, register here and dont forget to visit the Rackspace Technology virtual booth to learn more about our new AWS solutions and enjoy an immersive interactive experience. AWS re:Invent 2020 Recap: Machine Learning KeynoteGet up-to-speed on the latest AWS machine learning announcements from AWS re:Invent 2020. Explore re:Invent announcements and updates.https://events.rackspace.com/reinventFind out more

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Machine Learning Is Changing the Future of Software Testing

2020-12-03 14:00:00| TechNewsWorld

Machine learning, which has disrupted and improved so many industries, is just starting to make its way into software testing. Heads are turning, and for good reason: the industry is never going to be the same again. Let's delve into the current state of affairs, and explore how ML techniques are radically changing the software testing industry.

Tags: software future learning machine