MLOPS Training in Hyderabad

What is MLOps?

• MLOps stands for Machine Learning Operations.
• It combines Machine Learning, DevOps, and Data Engineering.
• Helps manage, deploy, and monitor ML models effectively in production.
• Ensures automation, collaboration, and faster delivery of ML solutions.

Different Stages in MLOps

• Data Collection – Gathering and preparing data for training.
• Model Training – Creating and testing ML models.
• Model Versioning – Tracking different versions of models and data.
• Model Deployment – Making models available to users.
• Monitoring – Checking performance and identifying issues in real-time.
• Model Retraining – Updating models based on new data or performance drops.

ML Project Lifecycle

• Define the Problem – Understand the business goal.
• Collect & Prepare Data – Clean and process data for training.
• Build the Model – Train and tune machine learning models.
• Evaluate the Model – Test accuracy and reliability.
• Deploy the Model – Launch the model in a real-world environment.
• Monitor & Improve – Track performance and make updates as needed.

 Job Roles in MLOps

• MLOps Engineer – Builds and manages ML pipelines and infrastructure.
• ML Engineer – Focuses on model development and optimization.
• Data Engineer – Handles data pipelines and storage systems.
• DevOps Engineer (ML) – Maintains deployment and automation systems.
• AI/ML Architect – Designs the end-to-end machine learning infrastructure.

What is the Development Stage of an ML Workflow?

• This is where data scientists build, train, and test models.
• Focuses on exploring data, creating features, and evaluating models.
• Typically involves experimentation and iteration to find the best model.

Pipelines and Steps

• A pipeline is a sequence of steps that process data and train models.
• Common steps include
  • Data loading
  • Preprocessing
  • Training
  • Evaluation
  • Deployment prep
• Pipelines make the workflow repeatable and scalable.

 Artifacts

• Artifacts are the outputs of each pipeline step.
• Examples include:
  • Processed datasets
  • Trained models
  • Evaluation metrics
• They help track progress and reuse results.

 Materializers

• Materializers handle how artifacts are saved and retrieved.
• They help in storing outputs in a way that can be reused later.
• Example: Saving a trained model as a .pkl file.

Parameters & Settings

• Parameters are variables used in training (e.g., learning rate, batch size).
• Settings configure how pipeline steps are run (e.g., number of retries, caching).

Changing parameters allows for easy experimentation without editing code.

Stacks & Components

• A stack is the complete setup or environment used to run an ML workflow.
• It includes different components like:
  • Orchestrator (controls workflow steps)
  • Artifact store (saves data and models)
  • Container or runtime (where the code runs)

Orchestrators

• Orchestrators manage and schedule pipeline runs.
• Ensure tasks run in the right order with dependencies handled.
• Popular tools: Airflow, Kubeflow, Prefect, ZenML Orchestrator.

Artifact Stores

• These are storage locations where artifacts (datasets, models, logs) are saved.
• Can use cloud (e.g., S3, GCS) or local disk.
• Helps in reproducibility and version tracking.

Flavors

• Flavors define the specific tool or technology used for each component.
• Example:
  • Orchestrator flavor: Apache Airflow
  • Artifact store flavor: AWS S3
  • Model deployer flavor: MLflow

This makes stacks modular and customizable.

ML Server Infrastructure

• Refers to the hardware and cloud setup used to run ML models.
• Can be on-premise servers or cloud platforms like AWS, GCP, or Azure.
• Needs to support scalability, high availability, and performance.
  

• Server Deployment

• Deploying ML models on servers so they can serve predictions.
• Common methods: REST APIs, batch jobs, streaming.
• Tools: Docker, Kubernetes, Flask, FastAPI, TensorFlow Serving.

Metadata Tracking

• Automatically stores information about:
  • Model versions
  • Dataset versions
  • Training parameters
  • Results and logs
• Helps in debugging, auditing, and reproducibility.

Collaborations

• Enables teamwork across data scientists, ML engineers, and DevOps.
• Involves shared pipelines, access control, version control (Git), and project tracking.
• Promotes faster and more organized development.

Dashboards

• Visual tools to monitor:
  • Model performance
  • Data drift
  • Training metrics
  • System health
• Tools: Grafana, Prometheus, MLflow UI, Streamlit, ZenML Dashboards.