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IBM Data Engineering

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This Professional Certificate is for anyone who wants to develop job-ready skills, tools, and a portfolio for an entry-level data engineer position. Throughout the self-paced online courses, you will immerse yourself in the role of a data engineer and acquire the essential skills you need to work with a range of tools and databases to design, deploy, and manage structured and unstructured data. By the end of this Professional Certificate, you will be able to explain and perform the key tasks required in a data engineering role. You will use the Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You will work with Relational Databases (RDBMS) and query data using SQL statements.


Tech jobs: These are the 10 most in-demand developer, cybersecurity and cloud roles

ZDNet

Despite fears of a looming recession and hiring freezes at a number of major tech companies, demand for tech-based roles continues to run high. Most companies have been forced to increase their reliance on – and investment in – technology over the past two and a half years. That's left them with a number of gaps in the workforce to fill, whether in IT security, software development, IT support or data analysis. ZDNet takes an in-depth look at key trends in software development and how developers are changing the tech industry. Some tech professionals are seeing greater demand than others.




GitHub Copilot, Microsoft's AI pair-programming service, is generally available

ZDNet

Microsoft's GitHub Copilot pair-programming service is generally available to all developers as of June 21. Microsoft launched GitHub Copilot in preview last year. GitHub Copilot suggests code to developers right in their editors, with the AI component acting like a pair-programming assistant. Suggestions are meant to match a project's context and style conventions and allow developers what to accept, reject or edit. GitHub Copilot is a Visual Studio Code extension plus a back-end service.


Exclusive Interview with Dmitry Petrov, Co-founder, and CEO, Iterative

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As the machine learning market catches up with the competition, the ML engineers would need tools that can evolve beyond catering to the basic needs of an ML team, to make it easier and faster to develop models and enable collaboration. Iterative develops open-source tools for developers to build and deploy models to specialized software that can speed up the training process. Analytics Insight has engaged in an exclusive interview with Dmitry Petrov, Co-founder, and CEO of Iterative. Iterative's mission is to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools. Our tools are Git-native to bridge the gap between software engineering and machine learning so that these two sides of the ML to production pipeline can happen collaboratively, efficiently, and reproducibly.


Law Smells - Artificial Intelligence and Law

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In modern societies, law is one of the main tools to regulate human activities. These activities are constantly changing, and law co-evolves with them. In the past decades, human activities have become increasingly differentiated and intertwined, e.g., in developments described as globalization or digitization. Consequently, legal rules, too, have grown more complex, and statutes and regulations have increased in volume, interconnectivity, and hierarchical structure (Katz et al. 2020; Coupette et al. 2021a). A similar trend can be observed in software engineering, albeit on a much shorter time scale.



Why Python?

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Moreover, Python has various excellent libraries and frameworks that save time and effort.


Could No-Code Enable Everything Ops?

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It feels like DevOps principles are permeating every discipline, creating new buzzwords by the minute. This "JargonOps" is clearly encouraged by marketing campaigns (and bloggers, wink, wink). Yet, the phrases do depict a real trend: all industries are getting an efficiency overhaul in the wake of increased automation. As I've covered before, low-code and no-code tools lower the barrier to entry to application development, enabling field experts to construct workflows as they see fit. For tech-savvy non-engineers, this could be a huge boon to transform copy-and-paste stopgaps into efficient workflow automations.