July 18, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Data cleaning is a crucial step that eliminates irrelevant data, identifies outliers and duplicates, and fixes missing values. It involves removing errors, inconsistencies, and, sometimes, even biases from raw data to make it usable. While buying pre-cleaned data can save resources, understanding the importance of data cleaning is still essential. Inaccuracies can significantly impact results. In many cases, before the removal of low-value data, the rest is still hardly usable. Cleaning works as a filter, ensuring that data passes through to the next step, which is more refined and relevant to your goals. ... At its core, data cleaning is the backbone of robust and reliable AI applications. It helps guard against inaccurate and biased data, ensuring AI models and their findings are on point. Data scientists depend on data cleaning techniques to transform raw data into a high-quality, trustworthy asset. ... Interestingly, LLMs that have been properly trained on clean data can play a significant role in the data cleaning process itself. Their advanced capabilities enable LLMs to automate and enhance various data cleaning tasks, making the process more efficient and effective.
Paravirtualization builds upon traditional virtualization by offering extra services, improved capabilities or better performance to guest operating systems. With traditional virtualization, organizations abstract the underlying resources via virtual machines to the guest so they can run them as is, says Greg Schulz, founder of the StorageIO Group, an IT industry analyst consultancy. However, those virtual machines use all of the resources assigned to them, meaning there is a great deal of idle time, even though it doesn’t appear so, according to Kalvar. Paravirtualization uses software instruction to dynamically size and resize those resources, Kalvar says, turning VMs into bundles of resources. They are managed by the hypervisor, a software component that manages multiple virtual machines in a computer. ... One of the biggest advantages of paravirtualization is that it is typically more efficient than full virtualization because the hypervisor can closely manage and optimize resources between different operating systems. Users can manage the resources they consume on a granular basis. “I’m not buying an hour of a server, I’m buying seconds of resource time,” Kalvar says.?
The challenge for service accounts is that MFA does not work, and network-level protection (IP filtering, VPN tunneling, etc.) is not consequently applied, primarily due to complexity and costs. Thus, service account key leaks often enable hackers to access company resources. While phishing is unusual in the context of service accounts, leakages are frequently the result of developers posting them (unintentionally) online, often in combination with code fragments that unveil the user to whom they apply. ... Now, Google has changed the game with its recent policy change. If an access key appears in a public GitHub repository, GCP deactivates the key, no matter whether applications crash. Google's announcement marks a shift in the risk and priority tango. Gone are the days when patching vulnerabilities could take days or weeks. Welcome to the fast-paced cloud era. Zero-second attacks after credential leakages demand zero-second fixing. Preventing an external attack becomes more important than avoiding crashing customer applications – that is at least Google's opinion.?
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On the load balancing front, Juniper has added support for dynamic load balancing (DLB) that selects the optimal network path and delivers lower latency, better network utilization, and faster job completion times. From the AI workload perspective, this results in better AI workload performance and higher utilization of expensive GPUs, according to Sanyal. “Compared to traditional static load balancing, DLB significantly enhances fabric bandwidth utilization. But one of DLB’s limitations is that it only tracks the quality of local links instead of understanding the whole path quality from ingress to egress node,” Sanyal wrote. “Let’s say we have CLOS topology and server 1 and server 2 are both trying to send data called flow-1 and flow-2, respectively. In the case of DLB, leaf-1 only knows the local links utilization and makes decisions based solely on the local switch quality table where local links may be in perfect state. But if you use GLB, you can understand the whole path quality where congestion issues are present within the spine-leaf level.”
AI platforms enable businesses to streamline operations and reduce costs by automating routine tasks and optimizing resource allocation. Predictive analytics, powered by AI, allows for proactive maintenance and issue resolution, minimizing downtime and ensuring continuous service availability. This is particularly beneficial for industries where uninterrupted access to cloud services is critical, such as finance, healthcare, and e-commerce. ... AI platforms are not only enhancing backend operations but are also revolutionizing customer interactions. AI-driven customer service tools, such as chatbots and virtual assistants, provide instant support, personalized recommendations, and seamless user experiences. These tools can handle a wide range of customer queries, from basic information requests to complex problem-solving, thereby improving customer satisfaction and loyalty. The efficiency and round-the-clock availability of AI-driven tools make them invaluable for businesses. By the year 2025, it is expected that AI will facilitate around 95% of customer interactions, demonstrating its growing influence and effectiveness.
Short-termism under pressure is a common mistake, resulting in an unbalanced strategy. How can we, as data leaders, successfully navigate such a scenario? “Working under pressure and with limited trust from senior management can force first-time CDOs to commit to an unbalanced strategy, focusing on short-term, highly visible projects – and ignore the essential foundation.” ... The desire to invest in enabling topics stems from the balance between driving and constraining forces. The senior management tends to ignore enabling topics because they rarely directly contribute to the bottom line; they can be a black box to a non-technical person and require multiple teams to collaborate effectively. On the other hand, Anne knew that the same people eagerly anticipated the impact of advanced analytics such as GenAI and were worried about potential regulatory risks. With the knowledge of the key enabling work packages and the motivating forces at play, Anne has everything she needs to argue for and execute a balanced long-term data strategy that does not ignore the “invisible” work required.