MLOPS Training in Hyderabad
with
100% Placements Assistance
- Online | Life Time Video Access
- |
- 2-3 Month |
- 20+ Industry Projects
MLOPS Training in Hyderabad
Batch Details
| Trainers Name | Madhu, Dr.Prasad |
| Trainers Experience | 12+ Years, 20+ Years |
| Next Batch Date | 25th Oct 2024(Offline), 25th Oct 2024(Online) |
| Training Modes | Online and Offline Training (Instructor Led) |
| Course Duration | 3 Months(Offline & Online) |
| Call us at | +91 9052144555 |
| Email Us at | brollyai.com@gmail.com |
| Demo Class Details | Click here to chat on WhatsApp |
MLOPS Training in Hyderabad
Why choose us?
- Cutting Edge Industry Ready Curriculum
- Expert Trainer from MNCs with 20+ Years Experience
- Starts from Foundation Level Training (NON CODERS ALSO ELIGIBLE)
- Adaptive Study Methods
- Life Time Access to Videos & LMS
- Learn Real-World Practical Implementation
- Special Doubt Clarification Sessions
- Personalized One - One Mentorship
- Covers most Advanced Topics that industry demands
- Continuous Support till you get job
MOLPS Training in Hyderabad
Curriculum
- What is MLOPS
- Different Stages in MLOPS
- ML Project Life Cycle
- Job Roles in MLOPS
- What is Development Stage of an ML Workflow
- Pipelines and Steps
- Artifacts
- Materializers
- Parameters and Settings
- Stacks & Components
- Orchestrators
- Artifact Stores
- Flavors
- ML Server Infrastructure
- Server deployment
- Mata Data Tracking Collaborations
- Dashboards
MLOPS Tools
Get Hands-on experience on tools that are used in industry
MLOPS Course In Hyderabad
Training Highlights
Industry-Ready Curriculum
Experienced Trainers
Foundation Level Training
Adaptive Study Methods
Interactive Classes
Lifetime Access
Practical Implementation
Doubt Clarification Sessions
Personalized Mentorship
Advanced Topics
Continuous Support
Interview Preparation
What is Machine Learning Operations (MLOPS)
- Machine Learning Operations (MLOPS) is a set of practices that combines Machine Learning (ML) and DevOps to automate and streamline the Application , management, and monitoring of ML models in production environments.
- The primary objectives of MLOPS are to enhance collaboration between data scientists and operations teams and to automate the ML model lifecycle, ensuring reliable and scalable deployment.
- MLOPS encompasses several core components: data engineering, which involves the collection, cleaning, and preprocessing of data; MLOPS Training in Hyderabad Includes model training, which includes the development, training, and hyperparameter tuning of ML models; model deployment.
- MLOPS focuses on transitioning models to production and integrating them with existing systems; and monitoring and maintenance, which involves the continuous monitoring of model performance and retraining as necessary.
- MLOPS improves collaboration between data scientists, developers, and operations teams and helps maintain compliance with regulatory requirements through versioning and documentation. MLOps also supports the efficient utilization of computational resources, optimizing costs and performance.
- MLOPS also presents challenges such as integrating diverse tools and technologies, efficiently managing computational resources, ensuring data and model security, and continuously monitoring models for drift and performance degradation.
- MLOPS is essential for ensuring that ML models are robust, scalable, and maintainable, enabling organizations to derive value from their ML investments effectively. The future of MLOPS looks promising, with trends such as the increased adoption of cloud-based MLOPS platforms, greater emphasis on model interpretability and fairness, and advances in automated machine learning (AutoML) and orchestration.
- MLOPS ensures that ML models are not only robust and reliable but also adaptable to changing business needs and technological advancements. MLOPS also presents challenges such as integrating diverse tools and technologies, efficiently managing computational resources, ensuring data and model security, and continuously monitoring models for drift and performance degradation.
MLOPS Training in Hyderabad
Objectives
MLOps Training in Hyderabad Trains professionals with the skills to streamline machine learning workflows and enhance collaboration between data science and operations teams. Through hands-on experience with industry-standard tools, participants will master automation, Application , and management of machine learning models.
Understand the Basics of MLOPs: Learn what Machine Learning Operations (MLOPS) is, why it’s important, and how it fits into the machine learning process.
Learn Automation Techniques: Discover how to automate steps in machine learning, like data preparation, training models, and deploying them.
Explore Ways to Execute Models: Understand different methods to Implement machine learning models so they work well and are easy to maintain.
Use CI/CD in Machine Learning: Learn how to use continuous integration and Implementation to make the model deployment process smoother and faster.
Improve Team Collaboration: Help data scientists, ML engineers, and operations teams work together better for more efficient workflows.
Monitor and Manage Models: Learn how to keep an eye on models after deployment to ensure they continue to perform well.
Know Security and Compliance: Understand how to keep data secure and follow privacy rules when deploying machine learning models.
Use Popular MLOPS Tools: Get practical experience with tools like Docker, Kubernetes, MLflow, and TensorFlow Extended (TFX).
Develop Problem-Solving Skills: Improve your ability to solve real-world MLOps problems through hands-on projects.
MLOPS Training in Hyderabad
Pre-Requisites
- Strong analytical skills and the ability to solve complex problems related to machine learning and operations, algorithms, and workflows.
- Proficiency in programming languages such as Python or R, commonly used in machine learning and data science.
- Ability to work with data, including data preprocessing, cleaning, and transformation.
- Basic knowledge of cloud computing platforms like AWS, Azure, or Google Cloud, often used for deploying machine learning models.
- Familiarity with popular machine learning frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
Who Should Learn MLOPS Course
Machine Learning Engineers
Professionals focused on developing and deploying scalable machine learning systems selecting the right machine learning model based on data characteristics and evaluating model performance.
Data Scientists
Those looking to streamline their machine learning workflows and improve the deployment and monitoring of their models.
DevOps Engineers
Individuals who want to specialize in the integration of machine learning models into production environments.
Software Developers
Developers interested in expanding their skill set to include machine learning operations.
IT Operations Professionals
Those aiming to understand and support the deployment and management of machine learning models.
Data Engineers
Engineers who want to enhance their capabilities in data preprocessing, pipeline automation, and model deployment.
Outline of MLOPS Course in Hyderabad
01
The course begins with an introduction to MLOps. You’ll learn what MLOps is, why it’s important, and how it’s used in Generative AI and machine learning.
02
You’ll start with the basics of machine learning. This includes understanding different types of machine learning like Key Algorithms.
03
We’ll explore different MLOps pipelines and tools. You’ll learn about CI/CD frameworks and Kubernetes for managing multiple containers.
04
You’ll get practical experience with coding exercises. Using popular frameworks like TensorFlow and PyTorch, you will build and train models.
05
We’ll talk about the ethical aspects of AI and MLOps. It’s important to use these technologies responsibly. We’ll cover topics like data privacy, avoiding bias in AI.
06
We’ll dive into advanced topics like automated model retraining, monitoring, and versioning. You’ll learn how to set up automated pipelines.
07
You’ll work on real-time case studies and projects. This hands-on experience will help you apply what you’ve learned to real-world scenarios.
08
At the end of the course, we’ll review everything you’ve learned. This will help reinforce the key concepts and skills you’ve gained.
09
After completing the course, you’ll get a certification in MLOps. This certification will show your skills and knowledge, which can help you in job interviews.
Overview Of MLOPS Training in Hyderabad
MLOps Training in Hyderabad is an excellent opportunity to master the integration of machine learning models with operational systems. This course starts with the fundamentals of MLOps, providing a solid foundation in its principles and practical applications.
You’ll delve into key concepts like model automation, deployment strategies, and continuous integration/continuous deployment (CI/CD), gaining hands-on experience with essential tools such as Docker, Kubernetes, and MLflow.
The training is structured to make complex topics accessible, featuring practical exercises and real-world projects that help you apply what you learn effectively.
Additionally, the course covers the important aspects of model monitoring, management, and compliance, ensuring you can deploy and maintain models responsibly and efficiently.
Whether you’re a newcomer or looking to advance your MLOps skills, this course offers a complete overview and practical experience.
Modes Of Training
Classroom Training
- Interactive Classes
- Hands-on Workshops
- Group Discussions
- Instructor-Led Sessions
- On-site Learning
Online Training
- Virtual Classes
- Webinars
- E-Learning Modules
- Remote Labs
- Flexible Schedules
Corporate Training
- Tailored Programs
- Team Building
- Industry-Relevant Content
- On-Site Workshops
- Customized Learning Paths
Difference between Machine Learning and MLOPS
Machine Learning
MLOPS
Focus Area
- Primarily focuses on designing, developing, and training models to make predictions or classifications based on data.
Focus Area
- Concentrates on operationalizing machine learning models, including deployment, monitoring, and maintenance.
Development vs. Operations
- Involves building and fine-tuning algorithms and models, often requiring data scientists and researchers.
Development vs. Operations
- Involves the integration of models into production environments and ensuring their continuous performance and scalability.
Workflow
- Focuses on data collection, preprocessing, model training, and evaluation.
Workflow
- Emphasizes automating and managing the end-to-end machine learning lifecycle.
Tools and Technologies
- Utilizes tools for model development and training such as TensorFlow, PyTorch, and scikit-learn.
Tools and Technologies
- Uses tools for model deployment and management like Docker, Kubernetes, MLflow, and TensorFlow Extended (TFX).
Skills Required
- Requires skills in statistical analysis, data manipulation, and algorithm development.
Skills Required
- Requires skills in software engineering, cloud platforms, containerization, and automation.
MLOPS Placements
- Technology and Software
- Finance and Banking
- Healthcare and Life Sciences
- Retail and E-Commerce
- Telecommunications
- Consulting Firms
- Automotive companies
- Consulting and Professional Services
- Government and Public Sector
MLOPS Training in Hyderabad
Careers
1
MLOps Engineer : Develop and manage machine learning models by MLOPS Engineer in production environments. Implement CI/CD pipelines and ensure models are scalable, reliable, and maintainable.
2
Data Engineer: Develop and maintain data pipelines, ensuring data is clean, reliable, and accessible for machine learning models. Integrate data sources and manage large datasets.
3
Machine Learning Engineer: Design, build, and train machine learning models. Collaborate with data scientists to optimize model performance and prepare models for deployment.
4
DevOps Engineer: DevOps Engineer Implement and manage infrastructure for deploying and scaling machine learning models. Ensure smooth integration between development and operations teams.
5
MLOps Specialist: MLOPS Specialist Focus on the operational aspects of machine learning models, including monitoring, performance tuning, and ensuring compliance with best practices.
6
AI/ML Research Scientist: Conduct research to develop new algorithms and techniques in machine learning. Collaborate with engineering teams to translate research into practical solutions.
7
Model Operations Manager:Oversee the deployment, maintenance, and scaling of machine learning models. Coordinate between teams to ensure model performance and reliability.
8
Data Scientist: Analyze and interpret complex data to inform model development. Work on feature engineering, data preprocessing, and model evaluation.
9
Cloud Solutions Architect:Design and implement cloud-based solutions for deploying and managing machine learning models. Ensure architectures are scalable and cost-effective.
10
AI Operations Analyst:Monitor the performance of AI models, identify issues, and suggest improvements. Work closely with MLOps teams to optimize operational workflows.
Skills Developed post - MLOPS Course
01
Ability to deploy machine learning models into production environments using various tools and platforms, ensuring smooth integration with existing systems.
02
Proficiency in implementing CI/CD pipelines specifically for machine learning models to automate the process of integration, testing, and deployment.
03
Skills in automating tasks such as data preprocessing, model training, and model evaluation to streamline and enhance the efficiency of machine learning operations.
04
Expertise in using containerization technologies like Docker and orchestration tools like Kubernetes to package and manage machine learning applications.
05
Ability to monitor the performance of deployed models in real-time, identify issues, and apply best practices for model maintenance and optimization.
06
Skills in designing and implementing scalable machine learning systems that handle varying loads and ensure high availability and reliability.
MLOPS Training in Hyderabad Certification
MLOps certifications are really important in today’s job market because they show employers that you know how to handle machine learning projects effectively. They prove that you can deploy and manage machine learning models in real-world situations.
When employers see these certifications on your resume, they feel more confident in your ability to apply machine learning techniques in practical ways. It’s like showing them you have the skills needed to make machine learning work smoothly in their business.
Getting certified also helps you keep learning and stay up-to-date with the latest advancements in managing machine learning projects. This continuous learning is crucial in such a fast-changing field.
we Brolly Ai also offers MLOPS Certification at our MLOPS Training in Hyderabad.
MLOPS Course in Hyderabad
Testimonials
Raju
@raju
The MLOps training at Brolly Ai Institute in Hyderabad was a big boost to my career. The course offered hands-on projects that taught me practical skills for deploying and managing machine learning models in real-world environments. The instructors were highly knowledgeable and provided great insights into the latest MLOps tools and practices. Their expertise and practical approach made complex concepts easier to understand. I highly recommend this training for anyone wanting to excel in MLOps and advance their career in machine learning operations.
Harini
@harini
Taking Brolly Ai’s MLOps course was one of the best choices I’ve made for my career development. The trainers were not only knowledgeable but also genuinely passionate about teaching. They made sure we understood the material thoroughly and could apply it effectively in real-world scenarios. The course included practical experience with CI/CD pipelines and model management, which was invaluable. The continuous support and feedback from the instructors helped me refine my skills and gain confidence in handling MLOps tasks. I’m now much better prepared for my role in machine learning operations.
Pawan
@pawan
Brolly Ai Institute’s MLOps course in Hyderabad truly exceeded my expectations. The curriculum was well-structured, covering both foundational concepts and advanced techniques. This made it suitable for learners at different levels. Working on real-world projects provided a unique opportunity to apply what I learned in a practical setting. The access to the latest tools and technologies helped me gain a solid understanding of MLOps. Thanks to the dedicated support from the Brolly Ai team, I’m now well-prepared for a successful career in this growing field.
Kishore
@kishore
After attending Brolly Ai’s MLOps training, I can confidently say it’s essential for anyone working with machine learning models. The course provided a deep dive into practical applications, such as deploying and managing models effectively. The curriculum was designed to be highly relevant to industry needs, which greatly benefited my professional role. The training also included valuable insights into scaling and maintaining machine learning systems. This course has significantly enhanced my skills and helped me meet the demands of my job more efficiently.
Bharath
@bharat
The MLOps course at Brolly Ai Institute was excellent for anyone interested in operationalizing machine learning models. The training covered a wide range of topics, from model deployment to monitoring and scaling. The course was well-organized, and the hands-on approach made it easy to grasp complex concepts. The supportive learning environment and interactions with industry professionals were particularly valuable. The skills I gained from this course have been instrumental in my role, and I feel much more capable in handling MLOps responsibilities.
Akash
@akash
Completing the MLOps course at Brolly Ai Institute was a key factor in my growth as an AI operations specialist. The training provided comprehensive insights into deploying and managing machine learning models, which has been crucial for my work. The practical experience with industry-standard tools and practices has helped me make better decisions in developing AI products. The certification from Brolly Ai has greatly advanced my career, opening up new opportunities and boosting my confidence in MLOps. I’m grateful for the valuable knowledge and skills I gained through this course.
MLOPS Training in Hyderabad
Benefits
Comprehensive Skill Set Development
Our MLOps training program at Brolly Academy equips individuals with a wide range of skills. These include learning how to deploy, monitor, and maintain machine learning models effectively. By mastering these skills, students can enhance their expertise in data science.
Expert Guidance
We offer complete guidance throughout the MLOps training, led by our experienced experts. Our trainers ensure that students gain a deep understanding and practical experience, helping them become proficient in MLOps practices.
Career Advancement
Learning MLOps opens up many job opportunities across various industries. Graduates can pursue roles such as ML engineer, DevOps specialist, and data scientist. These roles offer significant potential for career growth and advancement, making it a valuable skill set to acquire.
Operational Efficiency
The training teaches individuals how to streamline the deployment of machine learning models. This means they can make sure the models work efficiently and reliably, improving the overall performance of systems used in real-world applications.
Real-World Problem Solving
Our MLOps training includes hands-on projects and practical applications. This approach prepares individuals to solve real-world challenges, such as automating the deployment of models and managing large-scale data pipelines.
Industry-Relevant Knowledge
We ensure that our training content is up-to-date with the latest advancements in MLOps. This means our students gain industry-relevant knowledge, making them valuable assets in the fast-changing technology landscape.
MLOPS Market Trend
01
MLOps is becoming more important as companies look to improve how they deploy and manage machine learning models. There’s a growing need for experts who can handle these tasks efficiently.
02
Fields like finance, healthcare, and technology are using MLOps to make their machine learning processes smoother and more efficient. This helps them create better and more reliable solutions.
03
The rise of new tools and platforms is making it easier to manage machine learning models. Technologies like Kubernetes and MLflow are helping businesses deploy and track their models more effectively.
04
MLOps is changing how companies handle data. By using continuous integration and continuous deployment (CI/CD) methods, businesses can manage the entire machine learning process more efficiently.
05
Using cloud services for MLOps is becoming more common. Cloud platforms offer scalable resources that help in deploying and managing machine learning models, and they support real-time data processing.
06
Research in MLOps is focused on improving tools and finding new ways to use them. Innovations like Automated machine learning (AutoML) and better model monitoring are helping the technology grow.
07
There’s an increasing need for skilled MLOps professionals, leading to more training programs and certifications. Companies are looking for experts to help manage and optimize their machine learning workflows.
08
As MLOps becomes more integrated into business operations, there is a stronger focus on security and compliance. Companies are ensuring that their machine learning models meet data privacy and regulatory standards.
09
MLOps training programs provide practical experience to help individuals tackle real-world challenges. With hands-on projects, these programs prepare professionals to effectively manage machine learning models.
Our Accomplishments
FAQ’S
MLOps helps manage and improve how machine learning models are used in real-world applications. Taking this course at Brolly Ai will teach you how to deploy and manage these models effectively, which is important for many tech jobs.
Yes, you can join even if you’re new to coding or machine learning. The course starts with the basics, and while some knowledge of Python is helpful, you’ll learn everything you need from scratch.
The price of the course can vary. For the latest details on cost, please check the Brolly Ai Institute website or contact our team.
The course has a set duration, but you can learn at your own speed. You can choose how quickly you want to go through the materials based on your schedule.
There are no strict requirements, but knowing some Python and having a basic idea of machine learning will help. The course will cover all the basics you need to know.
Yes, you will receive a certificate from Brolly Ai Institute when you finish the course. This can be useful for job applications and career advancement.
If you need help, you can ask questions through our online forums, email, or live chat. We’re here to support you throughout the course.
Yes, you can still access the course materials even after you’ve completed the course. This way, you can review the content whenever you need.
Yes, the course includes practical projects to help you apply what you’ve learned. These projects are designed to give you real-world experience.
Yes, we offer support with job placement and internships. We help with career advice, resume building, and interview preparation to assist you in finding a job in MLOps.
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