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The Top Five Reasons People Quit Their Jobs

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The Top Five Reasons People Quit Their Jobs
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What’s the most common reason to quit a job? From COVID shutdowns, to a booming hiring market in certain sectors, to an epidemic of tech/media/real estate layoffs, it’s been a rollercoaster for members of the American workforce. 

A new survey reveals employees’ reasons for being fed with their jobs up after a tumultuous few years.

Reasons to quit a job, and hopes for the future

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The machine learning certifications tech companies want

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The machine learning certifications tech companies want
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In addition, the certification exam evaluates a candidate’s ability to implement strategies for deploying machine learning models. Finally, candidates are assessed on their ability to build monitoring solutions to detect data drift. Individuals who pass the certification exam can be expected to perform advanced machine learning engineering tasks using Databricks Machine Learning.

Google Cloud Professional Machine Learning Engineer

A professional machine learning engineer builds, evaluates, produces, and optimizes machine learning models using Google Cloud technologies and has knowledge of proven models and techniques, according to Google Cloud. The Google Cloud Professional Machine Learning Engineer certification ensures a machine learning engineer can handle large, complex datasets and create repeatable, reusable code; consider responsible AI and fairness throughout the machine learning model development process; and collaborate closely with other job roles to ensure long-term success of machine learning-based applications.

The Google Cloud Professional certified machine learning engineer also must have strong programming skills and experience with data platforms and distributed data processing tools, Google Cloud says. This professional is also expected to be proficient in the areas of model architecture, data and machine learning pipeline creation, and metrics interpretation. By training, retraining, deploying, scheduling, monitoring, and improving models, the machine learning engineer designs and creates scalable solutions.

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The cloud reaches its equilibrium point

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The cloud reaches its equilibrium point
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As such, rather than measuring plans, it’s worth checking on where workloads actually go. According to that same Barclays CIO survey, the answer is cloud.

Cloud spend and workload location 2022-2024.

Barclays CIO Survey

That’s a lot of cloud movement, but with all that interest in repatriation, it may simply signal that, as Coté posits, we’re due to settle into prolonged equilibrium, with plenty of workloads moving to the cloud but many staying on-premises. Underlying it all may be that enterprises don’t have the right people to refactor applications for the cloud. Or, rather, that the teams that got them where they are may not be the right teams to take them where they need to go. That’s a people problem, as most technology issues are, and change will take decades.

This is why, for most workloads, the answer to whether it goes to the cloud or stays on premises is, “It depends.” That is, it depends on the people involved, and not necessarily the workload itself.

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Overcoming data inconsistency with a universal semantic layer

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Overcoming data inconsistency with a universal semantic layer
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Once the underlying data and semantics are established, the universal semantic layer must be integrated with data consumers, such as generative AI, BI, spreadsheets, and embedded analytics. Cube Cloud is a universal semantic layer platform offering numerous prebuilt integrations and a robust API suite so enterprises can model data once and deliver it anywhere. It also offers a host of developer tools to make it easier to collaborate and build data models, set up caching and pre-aggregations, and maintain data access controls.

Benefits of a universal semantic layer

With a universal semantic layer, data teams have more governance and control, and — if implemented correctly — end users get more value from data and fewer misunderstandings among teams. This enhances efficiency and ensures that all data consumption places are working with the same, accurate data. So, no matter if the data is being used by a person looking at a dashboard, or a large language model that is giving someone answers to questions, the data is consistent.

All of this makes it easier for data teams to quickly deliver data to the various consumers they work with internally and externally. Data teams can easily update or define new metrics, design domain-specific views of data, and incorporate new sources of raw data. They also can enforce governance policies, including access control, definitions, and performance.

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Google releases differential privacy software for JVM

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Google releases differential privacy software for JVM
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Google has released PipelineDP4j, a differential privacy solution that allows developers to execute highly parallelizable computations using Java as the baseline language, while providing guarantees that personal information is kept private and secure.

PipelineDP4j opens the door for new applications of differential privacy by reducing the barrier of entry for developers already working in Java, Google said. The software executes on the JVM, can be used with Java, Kotlin, or Scala, and supports distributed data processing frameworks such as Apache Beam and, coming soon, Apache Spark.

 PipelineDP4j is intended for use by all developers, regardless of differential privacy expertise, Google said. Differential privacy is a PET (privacy-enhancing technology) serving as a framework for analysis of data sets in a privacy-preserving way to ensure that personal information is never released, according to Google.

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Overlooked cloud sustainability issues | InfoWorld

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Overlooked cloud sustainability issues | InfoWorld
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Cloud sustainability is a funny topic. It’s like dental care; everyone agrees it’s important, but who wants to discuss the particulars? It’s much more fun to talk about the new “shiny keys” of IT: AI and its endless possibilities. Cloud sustainability is so five minutes ago. However, like dental care, ignoring the issues will not make them go away.

AI growth was limited this year by the AI chip shortage. With the chip issue resolved, AI will accelerate the sustainability crisis due to its demands on our power grids that will outpace supply. Fortunately, we’re not at that tipping point—yet.

Looking in the wrong places

In my podcast discussions and videos on cloud and sustainability, which are becoming more frequent due to the rise of generative AI in the cloud, people tend to focus primarily on data center topics such as power and server efficiency. We need to understand that the power production and consumption side of things is not where the most improvements can be made.

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Download the AI in the Enterprise (for Real) Spotlight

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Download the AI in the Enterprise (for Real) Spotlight
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Download the November 2024 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World.

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Python is the most popular language on GitHub

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Python is the most popular language on GitHub
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October was an extra busy month in the world of Python, as all signs point to its unstoppable ascent. As reported on InfoWorld, Python is not only the language of choice for AI, but recently beat out JavaScript as the most used language on GitHub. That is no small feat. In more technical news, if you haven’t yet checked out the free-threaded, “no-GIL” build of Python, we encourage it—and we have the guide to get you started. We’ve also updated our introduction to parallelism in Python, which now covers Python threads, subprocesses, the new free-threaded build in Python 3.13, and subinterpreters.

Top picks for Python readers on InfoWorld

Python overtakes JavaScript on GitHub
Another rising star is Jupyter Notebook, also gaining traction on everyone’s favorite code-sharing site.

Python threading and subprocesses explained
Learn how to choose between threads or processes for parallel operations in Python, and find out what the free-threaded build in Python 3.13 brings to the table.

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What is cloud computing? Everything you need to know

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What is cloud computing? Everything you need to know
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Edge computing considerations

You often see edge computing incorrectly described as an alternative to cloud computing. Edge computing is about moving compute to local devices in a highly distributed system, typically as a layer around a cloud computing core. There is typically a cloud involved to orchestrate all of the devices and take in their data, then analyze it or otherwise act on it. 

To the cloud and back – why repatriation is real

While public cloud offers scalability and flexibility, some enterprises are opting to return to on-premises infrastructure due to rising costs, data security concerns, performance issues, vendor lock-in, and regulatory compliance challenges. While the public cloud offers scalability and flexibility, on-premises infrastructure provides greater control, customization, and potential cost savings in certain scenarios leading some technology decision-makers to consider repatriation. However, a hybrid cloud approach, combining public and private cloud, often offers the best balance of benefits.

More specific reasons to repatriate including the following:

  • Unanticipated costs, such as data transfer fees, storage charges, and egress fees, can quickly escalate, especially for large-scale cloud deployments.  
  • Inaccurate resource provisioning or underutilization can lead to higher-than-expected costs.
  • Stricter data privacy regulations require organizations to store and process data within specific geographic boundaries.  
  • For highly sensitive data, companies may prefer to maintain greater control over security measures and access permissions. 
  • On-premises infrastructure can offer lower latency, particularly for applications requiring real-time processing or high-performance computing.  
  • Overreliance on a single cloud provider can limit flexibility and increase costs. Repatriation allows organizations to diversify their infrastructure and reduce vendor dependency.  
  • Industries with stringent compliance requirements may find it easier to meet standards with on-premises infrastructure.  
  • On-premises environments offer greater control over hardware, software, and network configurations, allowing for customized solutions.  
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Benefits of cloud computing

The cloud’s main appeal is to reduce the time to market of applications that need to scale dynamically. Increasingly, however, developers are drawn to the cloud by the abundance of advanced new services that can be incorporated into applications, from machine learning to internet of things (IoT) connectivity.

Although businesses sometimes migrate legacy applications to the cloud to reduce data center resource requirements, the real benefits accrue to new applications that take advantage of cloud services and “cloud native” attributes. The latter include microservices architecture, Linux containers to enhance application portability, and container management solutions such as Kubernetes that orchestrate container-based services. Cloud-native approaches and solutions can be part of either public or private clouds and help enable highly efficient devops workflows.

Cloud computing, be it public or private or hybrid or multicloud, has become the platform of choice for large applications, particularly customer-facing ones that need to change frequently or scale dynamically. More significantly, the major public clouds now lead the way in enterprise technology development, debuting new advances before they appear anywhere else. Workload by workload, enterprises are opting for the cloud, where an endless parade of exciting new technologies invite innovative use.

SaaS has its roots in the ASP (application service provider) trend of the early 2000s, when providers would run applications for business customers in the provider’s data center, with dedicated instances for each customer. The ASP model was a spectacular failure because it quickly became impossible for providers to maintain so many separate instances, particularly as customers demanded customizations and updates.

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Salesforce is widely considered the first company to launch a highly successful SaaS application using multitenancy — a defining characteristic of the SaaS model. Rather than each Salesforce customer getting its own application instance, customers who subscribe to the company’s salesforce automation software share a single, large, dynamically scaled instance of an application (like tenants sharing an apartment building), while storing their data in separate, secure repositories on the SaaS provider’s servers. Fixes can be rolled out behind the scenes with zero downtime and customers can receive UX or functionality improvements as they become available.

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Grounding with Google Search available in Google AI Studio, Gemini API

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Grounding with Google Search available in Google AI Studio, Gemini API
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Google Cloud has made Grounding with Google Search available in the Google AI Studio and in the Gemini API. The new feature enables developers to get more accurate and fresh responses from Gemini generative AI models, the company said.

In addition to more accurate responses, the model returns links to the grounding sources and search suggestions that point users to search results that correspond to the grounded responses.

Grounding with Google Search is supported for all generally available versions of Gemini 1.5 models, Google Cloud said. It can be accessed in Google AI Studio under the “Tools” section or in the Gemini API by enabling the google_search_retrieval tool. Users can test grounding for free in Google AI Studio. With the API, developers can access the tool with the paid tier of $35 per 1,000 grounded queries.

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Is now the right time to invest in implementing agentic AI?

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Is now the right time to invest in implementing agentic AI?
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What makes agentic AI autonomous or able to take actions independently is its ability to interpret data, predict outcomes, and make decisions, learning from new data — unlike traditional RPA, which falters when encountering unexpected data, said Cameron Marsh, senior analyst at Nucleus research.

This adaptive nature of agentic AI, according to Chada, can help enterprises increase efficiency by handling complex, variable tasks that traditional RPA can’t manage, such as the roles of a claims adjuster, a loan officer, or a case worker, provided that it has access to the necessary data, workflows, and tools required to complete the task.

Software vendors are already touting agentic AI offerings with access to those resources, including the likes of Salesforce’s Agentforce, Microsoft’s Copilot-based autonomous Agents, ServiceNow’s AI Agents, Google’s Vertex AI Agent Builder, Amazon Bedrock Agents, and IBM’s watsonx Agent Builder, with more are likely to follow.

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