Full time Internship Training

DevOps SRE:

DevOps and Site Reliability Engineering (SRE) are related but distinct approaches to software development and operations. DevOps focuses on breaking down silos between development and operations teams to improve the entire software development lifecycle, emphasizing automation and collaboration. SRE, on the other hand, applies software engineering principles to operations, focusing on ensuring system reliability and performance through metrics like SLIs/SLOs and error budgets. While DevOps is a broader philosophy, SRE provides specific practices to achieve reliability within that framework. 

Here's a more detailed breakdown:

  • Focus: End-to-end software lifecycle, including development, testing, deployment, and monitoring. 
  • Goal: Accelerate software delivery, improve collaboration, and automate the software delivery pipeline. 
  • Key Principles: Collaboration, automation, continuous integration/continuous delivery (CI/CD). 
  • Practices: Infrastructure as code, automation tools (e.g., Jenkins, Docker, Kubernetes), and monitoring. 
  • Focus: Ensuring system reliability, availability, latency, performance, and capacity.
  • Goal: Balancing feature development velocity with system reliability.
  • Key Principles: Service Level Indicators (SLIs), Service Level Objectives (SLOs), error budgets, and automation.
  • Practices: Monitoring, incident response, automation, and capacity planning. 
  • SRE can be seen as a practical implementation of DevOps principles, focusing specifically on reliability. 
  • DevOps provides the broader context, while SRE offers the specific practices and tools to achieve reliability within that context. 
  • They often work together, with DevOps teams focusing on building and deploying software, and SRE teams ensuring its reliability in production. 
  • Both share the goal of improving the overall software development and delivery process and reducing organizational silos. 

Think of DevOps as the “what” and “why” of improving software delivery, and SRE as the “how” of ensuring reliability within that process. They are complementary practices that, when combined, help organizations deliver high-quality software efficiently and reliably. 

LLM DevOps, or LLMOps, refers to the application of DevOps principles and practices to the development and deployment of Large Language Models (LLMs). It involves managing the entire lifecycle of LLMs, from data preparation and model training to deployment, monitoring, and continuous improvement, ensuring they are reliable, efficient, and scalable. 

Here’s a breakdown of key aspects:

  • Large Language Models are AI models trained on massive datasets of text and code, enabling them to understand, generate, and manipulate human language. 
  • They are used in various applications, including chatbots, content generation, code completion, and more. 
  • LLMs, while powerful, introduce complexities in the development lifecycle. 
  • LLMOps provides a structured framework for managing these complexities, similar to how DevOps manages software development. 
  • LLMOps aims to streamline the process, improve collaboration, and ensure the quality and reliability of LLMs. 
  • Data Management:  Handling large volumes of data for training and fine-tuning LLMs, ensuring data quality and governance. 
  • Model Development:
    Utilizing tools and techniques for efficient model training, evaluation, and experimentation. 
  • Model Deployment:
    Implementing strategies for deploying LLMs to various environments (cloud, on-premise) and managing their lifecycle. 
  • Monitoring and Maintenance:
    Tracking performance metrics, identifying issues, and ensuring continuous improvement of LLMs. 
  • Automation:
    Automating repetitive tasks in the LLM pipeline, such as data preprocessing, model testing, and deployment. 
  • Collaboration:
    Fostering collaboration between data scientists, DevOps engineers, and other stakeholders. 
  • Increased Efficiency: Automating tasks and streamlining workflows to accelerate the development and deployment process. 
  • Improved Scalability:
    Managing and scaling LLMs to handle increasing workloads and user demands. 
  • Enhanced Collaboration:
    Facilitating better communication and collaboration between different teams involved in the LLM lifecycle. 
  • Reduced Risk:
    Minimizing risks associated with deploying and maintaining complex AI models. 
  • Better Model Quality:
    Ensuring higher quality and more reliable LLMs through continuous monitoring and improvement. 
  • LangChain: An open-source framework for building applications with LLMs, enabling complex workflows and integrations. 
  • Azure Machine Learning Prompt Flow: A platform for developing and iterating on LLM-based applications. 
  • DevOps Platforms: Tools like GitLab and ServiceNow can be integrated to manage the DevOps lifecycle of LLMs. 

LLMs can automate various DevOps tasks, such as:

  • Generating code and documentation.
  • Automating infrastructure provisioning and management.
  • Troubleshooting operational issues.
  • Improving security scanning and vulnerability detection.
  • Optimizing resource utilization. 
  •  Any degree graduates can apply.
  • Must have passed out from 2018 – 2025 graduates and Percentage is not mandatory.
  • Must agree with the company terms and conditions to act professionally and develop the career.
  • Must be able to work anywhere in Karnataka companies in top or reputed companies in every district.
  • Fresher Level – (0-1 Year): Min 5,00,000 lakhs per year – 6,00,000 lakhs per year 
  • Mid-Level – (1 – 3 year): Min 6,00,000 lakhs per year – 9,00,000 lakhs per year 
  • Senior Level – (3 – 6 year): Min 9,00,000 lakhs per year – 12,00,000 lakhs per year 
Internship Training Hrs./Day: 8 hrs./day (Morning – 4 hrs. & Evening – 4hrs.)
Days in a week: Monday, Tuesday, Thursday & Friday 
Mock Interviews (By MNC companies Staff): Every Wednesday & Saturday: Min 20 mins – Max 30 mins 
Mock Aptitude & Coding tests: Wednesday & Saturday: Min 1 hr. – Max 2 hrs.
Recap of the Training Sessions: Wednesday: 3hrs/day
Internship Practical Training Mode: Online Virtual
 
* Trainee must have a high-end internet connection during the training period, attend the internship training and present the min 90 % of whole attendance, follow the strict discipline during the training period, complete the practice, assignments and must be updated on time to develop the skills, pass the exams and mock interviews with a percentage of 90% to clear the main four interviews.
* Trainee should be well behaved at all the activities and become a professional at the end of the training.
* Trainees will be selected easily and must join after receiving the joining letter from the company.
 
Internship Training Fees: INR 60,000 Inclusive 18% GST 
Payment Options:
 
Option 1: Without Educational Loan
Installment 1: Down payment of 20%: INR 12,000 at the time of enrollment 
Installment 2: 50% of the remaining balance during 1st week of Internship Training: INR 24,000
Installment 3: Within 21 days from the date of second installment: INR 24,000
No Cash Accepted, Only Company Transaction online payment. 
 
Option 2: With Educational Loan
Apply for the Bajaj Finance in educational training loan process and must have a minimum credit score of 700+ to get approval for the 80% amount of INR 48,000.
Once Approved, Down payment of 20%: INR 12,000 at the time of enrollment and loan processed amount is credited to the company account.
You need to repay back the loan amount in 8 months for a monthly EMI of INR 6,000/month.
 
Once the payment is done, Invoice GST will be shared to you. 

Client Hiring Details:

  • We’re associated with fortune 500 companies and the interviews will be scheduled immediately after completion of the training within a week.
  • With over 15 mock interview calls with MNC staff, you’ll ease the interviewer and grab the opportunity to work at high scale in reputed companies.

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