Implementing MLOps in Enterprise Environments

Sarah Johnson

Lead ML Engineer

MLOps Machine Learning CI/CD Model Governance Model Monitoring Enterprise AI

In this comprehensive webinar, Sarah Johnson shares practical insights on implementing robust MLOps practices within enterprise organizations. Drawing from her experience leading ML engineering teams at Fortune 500 companies, Sarah covers the essential components of MLOps, common implementation challenges, and proven strategies for moving from experimental to production-grade machine learning workflows.

Originally presented on March 28, 2025 • 1,800 views

Key Points

MLOps Foundations

Defining MLOps in the enterprise context
The intersection of DevOps and data science
MLOps maturity model for enterprises
Building a business case for MLOps investment

Implementation Strategy

Starting with pilot projects and MVPs
Incremental improvements vs. complete overhauls
Team structures and skill requirements
Change management and organizational alignment

Technical Infrastructure

Model training orchestration
Feature store implementation
Deployment platforms and strategies
Model registry and versioning

Governance & Monitoring

Data and model quality monitoring
Model explainability requirements
Compliance and regulatory considerations
Automation of model retraining cycles

Enterprise MLOps Workflow

Data Engineering

ETL, validation, feature engineering

Experimentation

Research, model development

CI/CD Pipeline

Testing, validation, packaging

Deployment

Serving models in production

Monitoring

Data drift, performance

Technical Demo Highlights

During the webinar, Sarah demonstrates several key MLOps implementation patterns, including this sample CI/CD pipeline configuration for ML models:

# Sample GitLab CI/CD configuration for ML pipeline
stages:
  - data-validation
  - train
  - evaluate
  - package
  - deploy

data-validation:
  stage: data-validation
  script:
    - python validate_data.py --dataset ${DATASET_PATH}
  artifacts:
    paths:
      - data/validated/

model-training:
  stage: train
  script:
    - python train.py --config configs/production.yaml
  artifacts:
    paths:
      - models/trained/model-${CI_PIPELINE_ID}.pkl
      - metrics/training-${CI_PIPELINE_ID}.json

model-evaluation:
  stage: evaluate
  script:
    - python evaluate.py --model models/trained/model-${CI_PIPELINE_ID}.pkl
  artifacts:
    paths:
      - metrics/evaluation-${CI_PIPELINE_ID}.json
    reports:
      metrics: metrics/evaluation-${CI_PIPELINE_ID}.json

The webinar includes demonstrations of several enterprise MLOps tools including Kubeflow, MLflow, and custom CI/CD pipelines that integrate with existing enterprise DevOps workflows.

Additional Resources

MLOps Implementation Playbook

Detailed guide for planning and executing an enterprise MLOps strategy

Sample MLOps Project Repository

GitHub repo with reference implementation and documentation

MLOps Tools Comparison Video

Deep dive into the leading MLOps platform options for enterprises

ROI Calculator Spreadsheet

Excel template for calculating the business value of MLOps investments

0:00 - Introduction

Hello everyone, and welcome to today's webinar on implementing MLOps in enterprise environments. I'm Sarah Johnson, and I've spent the last eight years helping organizations build and scale their machine learning capabilities.

Today we're going to explore how to transition from ad-hoc data science projects to robust, production-grade machine learning systems that deliver consistent business value.

3:25 - Why MLOps Matters for Enterprises

Let's start by discussing why MLOps has become so critical for enterprise organizations. According to Gartner, nearly 85% of AI projects fail to deliver their intended results...

The primary reasons for these failures aren't usually related to model quality or data science expertise, but rather to operational challenges in deploying and maintaining models in production...

MLOps addresses these challenges by providing a systematic approach to the end-to-end machine learning lifecycle...

12:18 - MLOps Maturity Model

Now let's examine the MLOps maturity model that I've developed based on my work with enterprise clients...

Level 0 represents the starting point for many organizations: manual processes, limited collaboration between data scientists and engineers, and disconnected workflows...

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