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Machine learning: Accelerating your model deployment
2021-02-10 19:18:30| The Webmail Blog
Machine learning: Accelerating your model deployment nellmarie.colman Wed, 02/10/2021 - 12:18 Business models rely on data to drive decisions and make projections for future growth and performance. Traditionally, business analytics has been reactive guiding decisions in response to past performance. But todays leading companies are turning to machine learning (ML) and AI to harness their data for predictive analytics. This shift, however, comes with significant challenges. According to IDC, almost 30% of AI and ML initiatives fail. The primary culprits behind this failure are poor-quality data, low experience and challenging operationalization. They also require a lot of time to maintain, since you need to repeatedly train ML models with fresh data through the development cycle, due to data quality degradation over time. Lets explore the challenges presented when developing ML models and how the Rackspace Technology Model Factory Framework simplifies and accelerates the process so you can overcome these challenges. Machine learning challenges Among the most difficult aspects of machine learning is the process of operationalizing developed ML models that accurately and rapidly generate insights to serve your business needs. Youve probably experienced some of the most prominent hurdles, such as: Inefficient coordination in lifecycle management between operations teams and ML engineers. According to Gartner, 60% of models dont make it to production due to this disconnect. A high degree of model sprawl, which is a complex situation where multiple models are run simultaneously across different environments, with different datasets and hyperparameters. Keeping track of all these models and their associatives can be challenging. Models may be developed quickly, but the process of deployment can often take months limiting time to value. Organizations lack defined frameworks for data preparation, model training, deployment and monitoring, along with strong governance and security controls. The DevOps model for application development doesnt work with ML models. The standardized linear approach is made redundant by the need for retraining across a model lifecycle with fresh datasets, as data ages and becomes less usable. The ML model lifecycle is fairly complex, starting with data ingestion, transformation and validation so that it fits the needs of the initiative. A model is then developed and validated, followed by training. Depending on the length of development time, you may need to repeatedly perform training as a model moves across development, testing and deployment environments. After training, the model is then set into production, where it begins serving business objectives. Through this stage, the models performance is logged and monitored to ensure suitability. Rapidly Build Models with Amazon SageMaker Among the available tools to help you accelerate this process is Amazon SageMaker. This ML platform from Amazon Web Services (AWS) offers a more comprehensive set of capabilities towards rapidly developing, training and running your ML models in the cloud or at the edge. The Amazon SageMaker stack comes packaged with models for AI services such as computer vision, speech and recommendation engine capabilities, as well as models for ML services that help you deploy deep learning capabilities. It also supports leading ML frameworks, interfaces and infrastructure options. But employing the right toolsets is only half the story. Significant improvements in ML model deployment can only be achieved when you also consider improving the efficiency of lifecycle management across the teams that work on them. Different teams across organizations prefer different sets of tooling and frameworks, which can introduce lag through a model lifecycle. An open and modular solution agnostic of the platform, tooling or ML framework allows for easy tailoring and integration into proven AWS solutions. A solution such as this will allow your teams to use the tools they are comfortable with. Thats where the Rackspace Technology Model Factory Framework comes in, by providing a CI/CD pipeline for your models that makes them easier to deploy and track. Lets take a closer look at exactly how it improves efficiency and speed across model development, deployment, monitoring and governance, to accelerate getting ML models into production. End-to-end ML blueprint When in development, ML models flow from data science teams to operational teams. As previously noted, preferential variances across these teams can introduce a large amount of lag in the absence of standardization. The Rackspace Technology Model Factory Framework provides a model lifecycle management solution in the form of a modular architectural pattern, built using open source tools that are platform, tooling and framework agnostic. It is designed to improve the collaboration between your data scientists and operations teams so they can rapidly develop models, automate packaging and deploy to multiple environments. The framework allows integration with AWS services and industry-standard automation tools such as Jenkins, Airflow and Kubeflow. It supports a variety of frameworks such as TensorFlow, scikit-learn, Spark ML, spaCy, and PyTorch, and it can be deployed into different hosting platforms such as Kubernetes or Amazon SageMaker. Benefits of the Rackspace Technology model factory framework The Rackspace Technology Model Factory Framework affords large gains in efficiency, cutting the ML lifecycle from an average of 15 or more steps to as few as five. Employing a single source of truth for management, it also automates the handoff process across teams, simplifies maintenance, and troubleshooting. From the perspective of data scientists, the Model Factory Framework makes their code standardized and reproducible across environments, and it enables experiment and training tracking. It can also result in up to 60% of compute cost savings through scripted access to spot instance training. For operations teams, the framework offers built-in tools for diagnostics, performance monitoring and model drift mitigation. It also offers a model registry to track models versions over time. Overall, this helps your organization improve its model deployment time and reduce effort, accelerating time to business insights and ROI. Solution overview from development and deployment, to monitoring and governance The Model Factory Framework employs a curated list of Notebook templates and proprietary domain-specific languages, simplifying onboarding, reproduction across environments, tracking experiments, tuning hyperparameters and consistently packaging models and code agnostic to the domain. Once packaged, the framework can execute the end-to-end pipeline which will run the pre-processing, feature engineering and training jobs, log generated metrics and artifacts, and deploy the model across multiple environments. Development: The Model Factory Framework supports multiple avenues of development. Users can either develop locally, integrate with Notebooks Server using Integrated Development Environments (IDEs) or use SageMaker Notebooks. They may even utilize automated environment deployment using AWS tooling such as AWS CodeStar. Deployment: Multiple platform backends are supported for the same mode
Tags: model
learning
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Amazon worker fight: 'You're a cog in the machine'
2021-02-10 01:11:57| BBC News | Business | UK Edition
Activists are trying to unionise Amazon workers in Bessemer, Alabama, which would be a first for the US - if they succeed.
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amazon
fight
Autoplant the autonomous planting machine
2021-02-09 08:22:37| Industry Product and Service Suppliers | Latest Listings
Vinnova, Swedens Innovation Agency, together with the forest industry and researchers, invests 20 million SEK (approx. 2 million EURO) on development of an autonomous planting system, the Autoplant. The project shall improve forest planting with regard to precision, environmental impact and working environment. Autonomous planting machine Autoplant is the second step in a research program […] The post Autoplant the autonomous planting machine appeared first on Forestry.com.
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Nidec to acquire Mitsubishi Heavy Industries Machine Tool to support push into EV drives; E-Axle
2021-02-05 12:55:40| Green Car Congress
Tags: support
tool
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heavy
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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