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Virtual Learning Best Practices
2021-04-23 00:26:33| PortlandOnline
We encourage you to think about which of your meeting and training strategies will translate well to a remote setting, which ones won't, and what new approaches you might want to incorporate. PDF Document, 334kbCategory: Onboarding
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Engineer, Machine Learning
2021-04-16 12:13:10| Space-careers.com Jobs RSS
ROLE DESCRIPTION SUMMARY The position is responsible for providing technical expertise for creating software to automate the generation of optimized solutions using a variety of algorithms and analytics best practices. The incumbent will develop code for an exciting project that will be key to plan and optimize SES satellite capacity for SESs most advanced fully digitized satellites, the new O3b mPOWER constellation and SES17 satellite to start. The position holder will leverage on a wide range of technical, mathematical, and analytical experience and will effectively communicate designs to management and to other organizational teams. The Machine Learning Engineer will provide organization leadership in the development of optimization systems. This role requires excellent communication skills, a strong foundation in computer science and mathematics and a wide range of experience. PRIMARY RESPONSIBILITIES KEY RESULT AREAS Develop highquality, comprehensive software designs and architectures to create satellite optimization systems Analyze engineering requirements and constraints to design, prototype and build data models and optimization solutions Provide technical guidance to the business on best use of constraint programming Ensure tools and techniques used are reliable and provide high quality results Evaluate a wide range of technologies as part of a solution design and document the resulting designs and conclusions Ensure reliability, maintainability, and security best practices are enforced Identify multiple technical solutions for a given problem and help document those solutions Provide crossteam guidance on machine learning best practices Effectively communicate designs and procedures in writing Test and peerreview proposed software implementations Keep up to date with latest technologies COMPETENCIES SelfStarter with a high level of personal accountability Ability to set priorities and focus Ability to take ownership and drive a task to conclusion without supervision Ability to work autonomously and independently, and to take initiatives when required Commitment to deadlines and willingness to meet tight development schedules Ability to work efficiently both autonomous and in interdisciplinary teams Excellent communication and presentation skills, ability to communicate clearly to technical and nontechnical audiences Excellent written communication skills Demonstrate effective intercultural awareness Proven mindset of helping others to succeedmentoring QUALIFICATIONS EXPERIENCE Masters Degree in Computer Science, Statistics, Data Science or equivalent qualifications At least 5 years experience in statistical, optimization or data science roles Expert in a use of advanced analytical techniques involving time series, constraint programming, and data streams Experience with machine learning and deep learning for time series is a plus Expert programming experience with Python and its statistical libraries Programming experience with C Proficient in large database interaction using SQL Preferred proficiency with Git Azure experience with Databricks Spark, PySpark and other platforms a plus Fluency in English, any other language is considered as an asset Willingness to travel internationally Apply HERE
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New machine learning method accurately predicts battery state of health
2021-04-12 11:55:38| Green Car Congress
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Learning to code 'will seriously change your life'
2021-03-26 01:22:50| BBC News | Business | UK Edition
Software developers are in high demand and well paid, but how do you break into the industry?
Four steps to AI and machine learning success
2021-03-16 16:16:15| The Webmail Blog
Four steps to AI and machine learning success nellmarie.colman Tue, 03/16/2021 - 10:16 AI and machine learning (AI/ML) are hot topics, as businesses bring together cloud-based compute, memory and networking with an explosion of new data. This powerful combination is helping businesses deliver superior customer-centric experiences, understand their business environment like never before and drive new levels of efficiency. But achieving these AI/ML-driven successes is tough. In a recent Rackspace Technology-sponsored study, only 17% of respondents report mature AI/ML capabilities. The majority of respondents (82%) are still exploring or are struggling to operationalize AI/ML models. Why AI and machine learning efforts fail According to the study, businesses are struggling with their AI/ML efforts for several reasons: Failure to get the right data to the right app or point-of-analysis in real time Your machine learning training is only as good as the data you feed into your AI/ML frameworks and intelligent applications. If the data is bad, old or incomplete, the training will be poor and the answers and results generated will be (at best) equal to the quality of the data and perhaps flat out wrong. Lack of organizational collaboration Designing the right machine learning training and AI algorithms requires a holistic understanding of the data and processes that youre automating, across organizational boundaries. This requires communication and buy-in across the business. Lack of collaboration often yields a poor implementation, lower-quality data and rejection of the applications/automation projects by key parts of the organization. IT and business process immaturity If your IT and business processes are not well formed, then its likely your data is not complete, and the AI/ML execution will be sub-par. Also, AI/ML is best served with rapid iterations and improvements in the data and algorithms something that happens most effectively in a DevOps culture. Lack of expertise in mathematics, algorithm design or data science and engineering Since AI and machine learning are built on high-quality, timely data and well-formed algorithmsrepresenting the best in processes and models of the real world skills are critical. And finding the talent is tough in todays market. But with the right AI/ML strategy, you can overcome these challenges. Lets dive deeper into how you can make this happen. Four steps to AI and machine learning success Step 1: Build the foundation You must start by preparing your data and applications to migrate to the right multicloud and data architecture environments. This includes getting to know and understand your current environment and requirements and defining a roadmap. Be sure that the data architecture supports the new application deployments appropriately, and that you can minimize ingress/egress fees while also maximizing performance and availability. This is also the stage when database transformations and data warehouse migrations are implemented. Step 2: Modernize the data architecture Defining the modern data architecture, strategy and roadmap drives the transition into this phase. Here, youll focus on modernizing your data architecture defining, designing and building the data fabric. This includes pipelines and integration, data lakes and warehouses, and the analytics platform. You can start on this while youre working on Step 1, or at least execute the migration with an eye toward data architecture modernization. Step 3: Set the stage for more innovation AI/ML prepares your organization for high-quality automation and predictive intelligence driving innovation to the next level. At this stage, youll be planning the data science by designing, training and deploying the models, and operationalizing machine learning (MLOps). This enables you to deliver greater value to the modern cloud and data architecture you built in Steps 1 and 2. Step 4: Build intelligent applications Finally, youre ready to start delivering strategic value and capability, where you can realize the full value of this new cloud-based data fabric youve created. You can employ intelligent applications that incorporate chatbot services, natural language processing, machine vision, recommendation engines, predictive maintenance and even actions and get value from IoT data. Its all possible now and forms a new foundation for your business. Expert guidance for your AI and machine learning journey When your data works harder for you, you can take your resources further, delivering intelligent applications, services and results. This, in turn, enables you to make smarter decisions, improve collaboration, deliver new revenue streams and business models, and transform customer experiences. Do you need help getting the right data to the right application at the right business moment, while delivering a new level of business insights? Our experts are here to help. Let our specialists help you harness the power of modern data architecture and AI. Four steps to AI and machine learning successOrganizations are using AI and machine learning to make smarter decisions, improve collaboration, deliver new revenue streams and transform customer experiences. But the journey to success is tough. Heres how to set yourself up for success. How are businesses investing in AI and machine learning?/solve/succeeding-ai-mlRead the report
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