MLOPS Training in Hyderabad
with
100% Placement Assistance
MLOPS Training in Hyderabad
Batch Details
| Trainers Name: | Dr. Raju |
| Trainers Experience: | 13+ Years |
| Next Batch Date: | 22nd Sept(offline),15th Sept(online) |
| Training Modes: | Online and offline Training (Instructor-Led) |
| Course Duration: | 3 months(offline & online) |
| Call us at: | +91 81868 44555 |
| Email Us at: | brollyacademy@gmail.com |
| Demo Class Details: | ENROLL FOR FREE DEMO CLASS |
MLOPS Course Content
Far far away, behind the word mountains, far from the countr
- What is MLOPS
- Different Stages in MLOPS
- ML Project Life Cycle
- Job Roles in MLOPS
ies Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast
- What is Development Stage of an ML Workflow
- Pipelines and Steps
- Artifacts
- Materializers
- Parameters and Settings
- Stacks & Components
- Orchestrators
- Artifact Stores
- Flavor
- ML Server Infrastructure
- Server deployment
- Mata Data Tracking Collaborations
- Dashboards
MLOPS Training In Hyderabad
Key Points
- The MLOps training course at Brolly Academy offers comprehensive insights into integrating machine learning with operations for smoother deployment and management.
- The course covers key MLOps concepts such as CI/CD pipelines, model monitoring, versioning, and automated workflows to help streamline ML processes.
- Students will learn how to build and maintain scalable machine learning pipelines using popular tools and platforms like Kubernetes, Docker, and Jenkins.
- The training includes hands-on projects and practical exercises that provide real-world experience in deploying and managing ML models.
- Brolly Academy provides expert trainers with industry experience who guide learners through the latest MLOps practices and tools.
- The course emphasizes cloud-based MLOps, with modules covering the use of AWS, Azure, and Google Cloud Platform for model deployment and management
- Participants will gain a solid understanding of DevOps and DataOps principles as they apply to the field of machine learning.
- Brolly Academy offers flexible learning options, including online, classroom, and self-paced formats, catering to different learning needs.
- Upon completion, students receive a certification that adds value to their professional profile, demonstrating expertise in MLOps.
- The institute provides dedicated placement support to help students secure jobs in the field of MLOps, leveraging partnerships with top companies in the industry.
About MLOPS Training in Hyderabad
MLOps, or Machine Learning Operations, is a set of practices and tools that combine Machine Learning (ML) and DevOps to automate and streamline the lifecycle of ML models, from development to deployment and monitoring. It aims to bridge the gap between data science and IT operations by ensuring that machine learning models are reproducible, scalable, and reliable. MLOps encompasses a wide range of tasks, including data preprocessing, model training, version control, continuous integration and deployment (CI/CD) of models, monitoring of model performance, and retraining.
The implementation of MLOps practices ensures that ML models are not only developed but also successfully integrated into real-world applications with continuous improvement. It focuses on automating the entire ML pipeline, from data collection and preparation to model development, testing, deployment, and monitoring. This automation helps in reducing manual intervention, ensuring consistency, and speeding up the model deployment process. MLOps also facilitates collaboration among teams, enhances model reproducibility, and provides transparency into model decisions, making it easier to manage and audit machine learning systems.
Brolly Academy in Hyderabad offers comprehensive MLOps training designed to equip learners with the skills needed to efficiently manage and deploy machine learning models in production environments. The course covers essential MLOps concepts, including CI/CD pipelines, model monitoring, version control, and the use of tools like Docker, Kubernetes, and Jenkins. The training is led by industry experts and includes flexible learning options, along with placement support to help students advance their careers in the growing field of MLOps.
What is MLOPS?
- MLOps stands for Machine Learning Operations, which combines machine learning and IT operations.
- It helps manage the entire process of building, testing, and deploying machine learning models.
- MLOps ensures smooth collaboration between data scientists and IT teams to create reliable models.
- It focusses on automating tasks like model training, testing, deployment, and monitoring.
- MLOps makes sure models are always up-to-date and performing well with continuous updates.
- It uses tools like MLflow and Kubeflow to manage the machine learning workflow efficiently.
- MLOps helps track and control different versions of models and data to avoid errors.
- It improves the speed and quality of machine learning projects, helping businesses get better results.
Why is MLOPS Used?
- MLOps is used to automate the process of building, testing, and deploying machine learning models, saving time and effort.
- It helps in managing the entire machine learning lifecycle, ensuring models are developed and deployed more smoothly.
- MLOps improves collaboration between data scientists, developers, and IT teams, leading to better and faster results.
- It ensures the reliability and stability of models by continuously monitoring their performance in production.
- MLOps helps manage and track different versions of models and data, reducing errors and confusion.
- It makes it easier to update models with new data, ensuring they stay accurate and relevant over time.
- MLOps helps detect and fix issues faster, reducing the risk of model failures in real-world applications.
- It supports scaling machine learning solutions across an organization, making them more effective for business needs.
Course outline
- The MLOps training course at Brolly Academy begins with an introduction to MLOps concepts, including its importance and key practices in machine learning deployment.
- Learners will be taught how to set up CI/CD pipelines for machine learning models to automate the building, testing, and deployment processes.
- The course covers containerization and orchestration techniques using Docker and Kubernetes to ensure scalable and efficient model deployment.
- Students will learn about model versioning and tracking using tools like MLflow and DVC to manage different model versions and experiments effectively.
- There is a dedicated module on monitoring and managing model performance in production, including setting up alerts and retraining models when necessary.
- The training includes hands-on sessions for integrating popular ML tools with cloud platforms like AWS, Azure, and Google Cloud for seamless operations.
- Participants will explore data governance, data pipelines, and data preprocessing techniques essential for maintaining data quality in ML systems.
- The course concludes with real-world projects where learners apply MLOps techniques to deploy and manage machine learning models, providing practical experience.
Learning Modes MLOPS Training in Hyderabad
Offline Training
- Face-to-face interactions with instructors for personalized learning.
- Hands-on exercises and practical applications to solidify concepts.
- Group discussions and collaborative activities to foster teamwork.
- Immediate feedback from instructors to help learners correct mistakes.
Self Placed
- Flexible learning schedule allowing learners to progress at their own speed.
- 24/7 access to video lectures, tutorials, and other resources.
- Multimedia content, including interactive videos and quizzes.
- Self-assessment tools to monitor understanding and progress.
Corporate Training
- Tailored programs designed to meet specific organizational needs.
- Training sessions can be conducted on-site or through virtual platforms.
- Inclusion of real-world projects for hands-on experience relevant to the business.
- Daily recordings of sessions available for later review and reinforcement.
Why Choose us?
Experienced Faculty
Brolly Academy has a team of industry professionals with extensive experience in MLOps, ensuring high-quality instruction and up-to-date knowledge.
In-Depth Course Content:
The training program covers a broad range of MLOps topics, from CI/CD pipelines to model monitoring and cloud integration, providing a thorough understanding of the field.
Practical Projects
Students engage in real-world projects that allow them to apply MLOps concepts in practical scenarios, enhancing their learning experience and skill set.
Various Formats
Brolly Academy offers multiple learning formats, including online, classroom, and self-paced options, catering to different schedules and preferences.
Modern Infrastructure
The academy provides access to advanced tools and technologies, including cloud platforms and containerization tools, ensuring a hands-on learning experience with current industry standards.
Career Assistance
Brolly Academy offers dedicated placement support, helping students connect with potential employers and advance their careers in the MLOps field.
Strong Network
The academy’s connections with leading tech companies and industry experts provide valuable networking opportunities and insights into industry trends.
Small Class Sizes
The training sessions feature smaller class sizes, allowing for personalized attention and a more interactive learning environment.
Ongoing Resources
Students receive access to additional learning materials and resources even after the course, supporting continued development and knowledge enhancement.
Market Trend in MLOPS
01.
MLOps is rapidly gaining traction as organizations increasingly seek to integrate machine learning into their operations for better efficiency and scalability.
02.
The demand for MLOps professionals is growing, driven by the need for experts who can manage and streamline ML model deployment and maintenance.
03.
Companies are investing heavily in MLOps tools and platforms to automate model pipelines and reduce time-to-market for machine learning applications.
04.
There is a significant shift towards cloud-based MLOps solutions, with providers like AWS, Azure, and Google Cloud offering comprehensive tools for model management.
05.
The adoption of MLOps practices is expanding across various industries, including finance, healthcare, and retail, as companies leverage ML for data-driven decision-making.
06.
Continuous integration and continuous deployment (CI/CD) practices are becoming standard in MLOps, helping organizations automate the lifecycle of machine learning models.
07.
The trend towards using containerization technologies like Docker and orchestration tools like Kubernetes is growing, facilitating scalable and efficient ML model deployment.
Student Testimonials MLOPS Training
Priya
@Priya
I was impressed by the depth of the MLOps training at Brolly Academy. The instructors were knowledgeable, and the flexibility of the course format allowed me to balance my learning with work commitments. I highly recommend it for anyone looking to advance in MLOps.
Anita
@Anita
Brolly Academy’s MLOps training provided me with the practical skills and knowledge needed to manage machine learning models effectively. The hands-on projects and expert instructors made the learning experience both comprehensive and enjoyable.
Rajesh
@Rajesh
The MLOps course at Brolly Academy was a game-changer for my career. The course content was detailed, and the real-world applications we worked on were incredibly useful. The training has significantly improved my ability to deploy and maintain ML models.
Sandeep
@Sandeep
Brolly Academy’s MLOps training exceeded my expectations. The course was well-structured, and the hands-on experience with tools like Docker and Kubernetes was invaluable. I feel much more confident in my MLOps skills now.
Neha
@Neha
The MLOps training at Brolly Academy was thorough and practical. The focus on real-world projects and the support from industry experts made it a fantastic learning experience. It has equipped me with the skills to tackle complex ML deployment challenges.
Pranav
@pranav
MLOPS Course Certification
Certifications in MLOps validate an individual’s expertise in managing and deploying machine learning models efficiently, integrating them with operational systems, and ensuring their performance and scalability. These certifications typically cover key areas such as CI/CD pipelines, model monitoring, versioning, containerisation, and cloud-based MLOps solutions. They are valuable for demonstrating proficiency in MLOps practices and enhancing career opportunities in a rapidly growing field.
Relevant certifications for MLOps include:
- Google Professional Machine Learning Engineer: Focuses on designing and managing ML models on Google Cloud, including aspects of MLOps.
- Microsoft Certified: Azure Data Scientist Associate: Covers implementing and managing machine learning solutions on Azure, including MLOps practices.
- AWS Certified Machine Learning – Specialty: Validates expertise in designing, deploying, and managing ML solutions on AWS, with an emphasis on MLOps.
- Advanced Natural Language Processing (NLP) for Generative AI
- DataRobot MLOps Certification: Focuses specifically on the deployment and operationalization of machine learning models using the DataRobot platform.
Skills Developed Post MLOPS Training
- Proficiency in building and managing CI/CD pipelines for efficient machine learning model deployment.
- Ability to use containerization tools like Docker and orchestration platforms like Kubernetes for scalable model management.
- Understanding of model monitoring techniques and performance tuning to ensure reliability in production environments.
- Knowledge of integrating machine learning models with cloud platforms such as AWS, Azure, and Google Cloud for seamless operations.
- Experience in versioning and tracking machine learning models using tools like MLflow and DVC.
- Proficiency in automating data pipelines and workflows to streamline ML processes and reduce manual intervention.
- Ability to implement best practices for data governance and quality management within ML systems.
- Knowledge of real-world applications and hands-on experience with deploying and maintaining machine learning models effectively.
Tools covered in MLOPS Training in Hyderabad
- Docker
- Kubernetes
- Jenkins
- MLflow
- DVC (Data Version Control)
- Terraform
- Grafana
- Prometheus
- AWS SageMaker
- Azure Machine Learning
- Google AI Platform
Job opportunities after MLOPS course
MLOps Engineer
Machine Learning Engineer
Data Engineer
DevOps Engineer
ML Operations Manager
Cloud Engineer
Data Scientist with MLOps Expertise
AI/ML Solutions Architect
Machine Learning Platform Engineer
Placement program of MLOPS Training
The placement program offered as part of the MLOps training is designed to help students transition smoothly into the job market. It includes personalized career counseling, resume building, and interview preparation tailored to the MLOps field. The program also connects students with potential employers through networking events, job fairs, and direct referrals, increasing their chances of landing relevant positions in the industry.
Brolly Academy is committed to ensuring that its MLOps training graduates are well-prepared for their careers. With a strong focus on industry connections and job placement support, the academy provides valuable resources and guidance to help students secure positions in leading companies. The academy’s comprehensive placement program, combined with its expert training and practical experience, equips students with the skills and opportunities needed to succeed in the competitive MLOps job market.
Pre-requisites
MLOPS course In Hyderabad
Basic understanding of machine learning concepts and algorithms is required to grasp MLOps practices effectively.
Familiarity with programming languages such as Python or R is essential for implementing and managing ML models.
Knowledge of cloud platforms like AWS, Azure, or Google Cloud is helpful for deploying and scaling machine learning solutions.
Some experience with version control systems like Git and basic DevOps concepts is beneficial for working with CI/CD pipelines.
Frequently Asked Questions - MLOPS Course
MLOps stands for Machine Learning Operations, a set of practices that combines machine learning and DevOps to automate and streamline the deployment, management, and monitoring of machine learning models.
MLOps training is ideal for data scientists, machine learning engineers, DevOps professionals, and anyone interested in managing and operationalizing machine learning models in production environments.
Basic understanding of machine learning concepts, programming skills (preferably in Python), familiarity with cloud platforms, and some knowledge of version control systems are recommended.
The course covers CI/CD pipelines, model monitoring, version control, containerization with Docker, orchestration with Kubernetes, cloud-based MLOps solutions, and practical deployment of machine learning models.
Training is typically offered in various formats, including online, classroom, and self-paced options, to accommodate different learning preferences and schedules.
Upon completion, you may receive a certificate of completion from the training provider, and you can also pursue relevant industry certifications such as AWS Certified Machine Learning – Specialty or Google Professional Machine Learning Engineer.
MLOps training can enhance your skills in managing and deploying machine learning models, making you a valuable asset to organizations looking to operationalize their ML systems and improving your job prospects in a growing field.
You can pursue roles such as MLOps Engineer, Machine Learning Engineer, Data Engineer, DevOps Engineer, and AI/ML Solutions Architect, among others, depending on your background and interests.
Yes, many training programs, including those offered by Brolly Academy, provide job placement support, including resume building, interview preparation, and connections to potential employers.
