Kushagra Sharma's Blog Posts
Kushagra Sharma is a Product Manager at Cisco working on AIOps products that help teams operate complex systems with intelligence and scale. He focuses on translating customer problems into practical, data-driven solutions at the intersection of AI, reliability, and software platforms. Passionate about clear thinking and strong product craft, Kushagra writes to share insights on technology, product strategy, and building systems that actually work in the real world.
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Unlocking the Power of the Cisco Time Series Model with Splunk DSDL
Splunk released Cisco Time Series Model, an open-weight Transformer-based foundation model for time-series analysis, available on Huggingface. In this blog post, we explore how the model is seamlessly integrated with your data in Splunk leveraging the Splunk App for Data Science and Deep Learning.
Why Staying Flexible Is the Only Way to Master AI
Learn how AI-ready companies stay agile amid uncertainty and turn risk into opportunity with smart governance, culture, and scalable strategies.
Top Ransomware Attack Types in 2026 and How to Defend
Learn about ransomware and its various attack types. Take a look at ransomware examples and statistics and learn how you can stop attacks.
Announcing the General Availability of Splunk POD: Unlock the Power of Your Data with Ease
Splunk POD is designed to simplify your on-premises data analytics, so you can focus on what really matters: making smarter, faster decisions that drive your business forward.
Introducing the Cisco Time Series Model: Unlock Machine Data Insights with Gen AI
The Cisco Time Series Model is open-weight time series foundation model that can be easily deployed for reliable forecasting across observability and security operations, automations, and agentic workflows.
MachineGPT, Agentic AI, and the New Foundation for Digital Resilience
MachineGPT is foundational to the rise of Agentic AI in the enterprise, which is poised to fundamentally reshape digital operations – and it's advancing faster than we expected.