ONLINE CERTIFICATE COURSE
MLOps
Machine Learning Operations
Online Intensive Learning with Hands-On Exercises for better understanding
Intensive Learning
Hands-On Exercise
Interactive Sessions
Individual Practice
Introduction to Course
In today’s data-driven world, businesses are increasingly relying on machine learning to drive innovation and gain a competitive edge. However, effectively managing and deploying machine learning models at scale can be a daunting task. This is where MLOps comes in. MLOps, short for Machine Learning Operations, is a set of practices aimed at streamlining the development, deployment, and maintenance of machine learning models.
At its core, MLOps emphasizes automation and collaboration throughout the machine learning lifecycle. By automating repetitive tasks such as data preprocessing, model training, and deployment, MLOps enables data scientists to focus on more strategic activities, leading to increased productivity and efficiency. Additionally, MLOps facilitates collaboration between data science, IT operations, and business teams, ensuring that ML projects are aligned with organizational goals and requirements.
One of the key benefits of MLOps is its ability to enhance model reliability and scalability. By implementing best practices such as version control, model testing, and monitoring, MLOps helps organizations build robust and scalable ML pipelines that can handle large volumes of data and adapt to changing business needs. This not only improves the performance of ML models but also reduces the risk of errors and downtime.
Applications of Machine Learning Operations
In this section we are discussing some of the potential areas of application of Machine Learning Operations & how it can be helpful in these areas to carryout analysis.
Model Deployment and Monitoring
MLOps ensures smooth deployment of machine learning models into production environments and facilitates continuous monitoring to track model performance and detect anomalies.
Automated Testing and Validation
MLOps enables automated testing of machine learning models to ensure they meet performance standards and comply with business requirements, helping to maintain model accuracy and reliability.
Version Control and Model Governance
MLOps provides version control mechanisms for tracking changes to models, datasets, and code, ensuring transparency, reproducibility, and compliance with regulatory standards.
Scalability and Efficiency
MLOps helps organizations scale their machine learning workflows efficiently by automating repetitive tasks, optimizing resource allocation, and leveraging cloud-based infrastructure for increased flexibility and scalability.
CI/CD
MLOps facilitates CI/CD practices for machine learning projects, allowing organizations to automate the process of building, testing, and deploying ML models, leading to faster iteration cycles and accelerated time to market.
Different Types of Analysis
Majorly, there are 3 different types of operations using MLOps so below we have tried to list them down to help you take better decision if this course will be relevant for your learning and be helpful in your intended area of research.
Model Development Operations
This involves practices and tools for managing the development lifecycle of machine learning models, including version control, collaboration, and automation of tasks such as data preprocessing, feature engineering, model training, and evaluation.
Model Monitoring and Management
This operation focuses on monitoring ML models in production to ensure they perform as expected and managing their lifecycle.Continuously tracking key performance metrics such as accuracy, latency to detect any degradation in model performance.
Model Governance & Compliance
This includes governance frameworks for model development and deployment, compliance with regulatory requirements such as GDPR or HIPAA, and mechanisms for ensuring transparency, fairness, and accountability in model decision-making.
Topics Covered
MLOps is essential for maximizing the efficiency and performance of machine learning projects. By adopting MLOps practices, organizations can unlock the full potential of their data and drive innovation at scale.
Train at Scale
- Packaging & Virtual Env 101
- Installing & Using a Package
- Testing your Package
- Data Engineering Tipst
Cloud Training
- Reminders
- Objective
- Cloud platform
- Application parameters
- Model in the cloud
- Data in the cloud
- Training in the cloud
Automate Model Lifecycle
- Objective
- Experiment tracking with MLflow
- Automating the Model Lifecycle with Prefect
Predict in Production
- Root Entry Point
- Running API using the web server
- Prediction API Use Case
- Create a Docker Image
- Storing our Docker images
- Updating the Dockerfile for Google Cloud Run
- Build Image for Artifact Registry
- Push Image to Artifact Registry
- Deploy Image to Cloud Run
Deploy
- User Interface
- Connecting User Interface with API.
- Storing the UI in cloud.
CI - Continuous Integration
- GitHub Actions
CD - Continuous Deployment
- Continuous Deployment with Streamlit/GCP + GitHub Actions
- Google Cloud Platform
- Testing it.
TakeAway From The Course
MLOps enables organizations to accelerate time to market for ML applications through continuous integration and continuous deployment (CI/CD) practices. By automating the deployment process and establishing feedback loops, MLOps allows organizations to iterate rapidly and deliver value to customers faster.
Apart from the topics mentioned above there are a few extra things which you can take away from this bootcamp, which will be adding more value to your work
An introductory theory document to help you better understand the subject will also be provided.
After the completion of the session complete access to the trainers slide deck will also be provided
We will also be providing a complete access to trainers repository so that you can use it as reference later
During the course of 3 days we will be have live8+ hourse of training sessions with the participants.
Hands-On exercises are a must to better learn any technology and be able to reproduce it later.
A Participations certificate is a must after successfully completing the training as a sign of accomplishment.
Expected Outcomes of the Course
After completing all the tasks of this masterclass all of our participants will be able to:
MLOps helps ensure that machine learning models deployed in production environments are reliable and performant.
MLOps enables organizations to scale their machine learning operations efficiently to handle growing volumes of data.
MLOps streamlines the machine learning lifecycle, automating repetitive tasks, standardizing processes.
By automating the deployment process and implementing continuous integration and continuous deployment practices.
Terms & Conditions
- All fee paid is not refundable so please read all the terms & conditions before making any payments. If you still have any doubts please contact us and confirm and then only make the payment.
- Participants need to bring their registration tickets along with a valid Institutional ID, then only they will be allowed to attend the session. Please reach out to our team in case of any exceptions.
- Please fill all your details in the form correctly as those details will be used in your certificate as well.
- Participants need to bring their own computer (laptop) system for the program.
- The software tools and other required software tools will be provided from our side for the purpose of this program.
- Participants need to reach the venue and report 30 minutes prior to the start of the sessions.
- Participants need to wear masks all the time inside the premises and abide by the other rules at the premises.
- Particpants need to attend all the sessions in order to be eligible for getting the certificate.
- Welcome email will be sent to all the participants with all the details related to the program. Please check your Inbox/Spam folder for the email.
- All the details of the software installations and how to prepare your system for the Program will be shared with all the participants in the Welcome Email itself.
Contact Us
We understand that you may have some questions before you make the payment for the course. Feel free to get in contact with us through the below given options.
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