Jheel Patel

Cloud. Automation. AI.

Cloud & Automation

Cloud computing is revolutionizing industries by providing scalable, cost-efficient access to computing and advanced technologies like AI, big data, and IoT. While adoption presents challenges—legacy systems, security, and integration—a strategic approach ensures seamless transformation. With expertise in AWS and Google Cloud, I design secure, scalable, and innovative cloud solutions that align with business objectives, driving efficiency, agility, and long-term success in the digital era.

How I can help you…

Site Reliability

I provide expert services in Logging & Observability, Infrastructure Automation, and Performance Tuning, ensuring optimal system reliability, efficiency, and high availability. With a strong focus on Site Reliability Engineering (SRE) best practices, I specialize in implementing Proactive Monitoring based on the Four Golden Signals—Latency, Traffic, Errors, and Saturation—to enhance system performance and detect issues before they impact users.

Infrastructure Management & Modernization

I provide end-to-end Infrastructure Management and Cloud Modernization services, ensuring scalable, resilient, and high-performance cloud environments. My expertise includes Cloud Architecture Design, Infrastructure Provisioning, Automation & Orchestration, and Cloud Wellness Assessment, enabling businesses to optimize their cloud infrastructure for efficiency and reliability. I specialize in Cloud Modernization through Containerization, Serverless Computing, Cloud-Native Development, and Reactive Architecture, ensuring seamless scalability and agility.

Cost Optimization

Studies show that up to 32% of an organization’s cloud spend is wasted, highlighting the need for effective cost optimization strategies. I provide services to maximize cloud efficiency and reduce unnecessary expenses, including Utilization & Resource Assessment, Committed Usage & Savings Plans, Storage Archiving & Lifecycle Policies, and Idle Resource Automation. By implementing these strategies, I help businesses optimize resource allocation, automate cost-saving measures, and ensure their cloud infrastructure operates at peak efficiency without overspending.

Security

My expertise includes Identity & Access Management (IAM) to ensure secure user authentication and authorization, Network Security and Next-Gen Firewall (NGFW) Infrastructure Deployment for advanced threat prevention, and Web Application Firewall (WAF) implementation to protect web applications from cyberattacks. I also conduct Security Assessments to identify vulnerabilities and enhance system resilience, along with Data Security & Encryption to protect sensitive information. With a proactive approach to cybersecurity, I help businesses stay secure in an evolving digital landscape.

AI

Machine Learning (ML) and Deep Learning (DL) are the most popular approaches to implementing AI due to their wide applicability and ability to generalize across various tasks involving objects such as images, language, audio, symbols, and reasoning. The intelligent behavior enabled by ML and DL has pushed the boundaries of what is possible with AI, saving significant time and resources.

There are several factors to consider when deciding whether to adopt AI, but two key drivers stand out.

  • First, if a process or task is time-consuming and resource-intensive, and AI can automate it at a justifiable cost, it can significantly reduce manual effort. In many cases, AI adoption not only saves resources but also enhances the process itself or even creates new value-added services, expanding your business offerings.
  • Second, if your business has access to valuable data, leveraging Machine Learning (ML) and Deep Learning (DL) can extract meaningful insights, drive informed decision-making, and unlock new growth opportunities.

How I can help you…

Problem Discovery

Identify a problem or a business opportunity that can be effectively addressed using AI. This may involve automating a process that requires intelligent behavior, extracting valuable insights from data, or leveraging AI to introduce a novel solution. The key is to ensure that AI adoption is justifiable in terms of efficiency, cost, and potential impact.

Data Exploration & Feature Engineering

If the problem requires Machine Learning (ML) or Deep Learning (DL), data is essential for training or fine-tuning the model. Typically, data exploration and modeling go hand in hand. In some cases, the chosen AI models are pre-trained and may not require training data. However, when data is necessary, exploration and feature engineering play a critical role in assessing the feasibility of the AI solution.

Modeling

Once the problem is defined and relevant data is available, the next step is to determine the most suitable ML or DL approach. This involves identifying the AI tasks that best map to the problem, such as classification, regression, forecasting, clustering, recommendation, or generative modeling. Depending on the task and data availability, appropriate models—such as logistic or linear regression, k-means clustering, decision trees, random forests, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), ensemble models, transfer learning, or Large Language Models (LLMs)— or preexisting services/agents can be evaluated and selected.

Implementation & Operations

Once the data and AI models are finalized, the implementation phase begins. This may involve setting up ETL (Extract, Transform, Load) pipelines for data processing and cleaning, building model training and testing infrastructure, and deploying inference services for real-time or batch predictions. With the availability of cloud-based AI services, implementation and operations (MLOps) can be efficiently managed on a scalable and well-architected infrastructure, ensuring seamless integration into business workflows.