Teresa Saville
|Subscribers
About
Poster Keerthana Deepti Karunakaran BioMedical Engineering And Imaging Institute
**Finance & Investment Professional – Executive Summary**
A seasoned finance leader with **15+ years of experience** across the full spectrum of corporate finance, investment banking, private equity, venture capital, and fintech operations. Proven expertise in:
| Domain | Key Contributions |
|--------|-------------------|
| **Corporate Finance & M&A** | • Structured & negotiated multi‑billions dollar transactions (acquisitions, divestitures, joint ventures).
• Conducted rigorous due‑diligence, valuation modelling, and post‑merger integration plans. |
| **Investment Banking** | • Led capital‑raising missions (IPO, secondary offerings) generating over $5 bn in equity for clients.
• Crafted pitch books, performed market & industry analysis, and advised on strategic positioning. |
| **Private Equity / Venture Capital** | • Sourced, screened, and evaluated investment opportunities across technology & healthcare sectors.
• Oversaw portfolio monitoring, governance, and exit strategies (IPOs, M&A). |
| **Corporate Strategy** | • Defined long‑term growth plans, market entry strategies, and partnership frameworks for multinational corporations.
• Coordinated cross‑functional teams to align operational execution with strategic objectives. |
---
### 3 Key Takeaways / Lessons Learned
1. **Data‑Driven Decision Making is Non‑Negotiable**
*Across all roles—whether assessing a startup’s traction or evaluating an acquisition target—the quality, relevance, and integrity of data determine the reliability of insights.*
*Implication:* Build robust data pipelines early; invest in analytics platforms that can ingest disparate sources (CRM, ERP, market feeds) to provide real‑time dashboards for stakeholders.
2. **Communication Bridges Technical Analysis and Business Strategy**
*The value of an analyst is amplified when findings are translated into clear narratives aligned with organizational goals.*
*Implication:* Develop storytelling skills—use visualizations, executive summaries, and actionable recommendations—to influence decision makers beyond the data science team.
3. **Adaptability Across Domains Enhances Problem‑Solving**
*Working in diverse settings (financial services, marketing, B2B tech) exposes analysts to varied datasets, constraints, and regulatory environments.*
*Implication:* Cultivate a flexible mindset: learn domain fundamentals quickly, leverage transferable analytical frameworks, and remain open to iterative learning cycles.
---
### Practical Takeaways for Your Role
| Action | Why It Matters | How to Implement |
|--------|----------------|------------------|
| **Prioritize Data Quality** | Garbage in → garbage out. | Build automated data validation pipelines; flag anomalies early. |
| **Define Clear Success Metrics** | Ambiguous goals stall progress. | Translate business objectives into measurable KPIs; align with stakeholders. |
| **Adopt Incremental Modeling** | Full‑scale models risk overfitting and slow iteration. | Prototype with small subsets, validate, then scale up gradually. |
| **Document Assumptions & Constraints** | Hidden assumptions derail later stages. | Maintain a living spec sheet; review before each sprint. |
| **Collaborate Cross‑Functionally** | Siloed knowledge limits insight depth. | Pair data scientists with domain experts for feature brainstorming. |
---
## 4. Concrete "First Steps" Checklist
| # | Action | Owner | Target Date | Notes |
|---|--------|-------|-------------|-------|
| 1 | Identify **key business metrics** to drive the model (e.g., churn rate, lifetime value). | PM / Finance | Day 2 | Use existing dashboards. |
| 2 | Create a **data inventory map**: list tables/fields that could contribute to these metrics. | Data Engineer | Day 3 | Capture lineage and freshness. |
| 3 | Set up a **sandbox environment** (e.g., Snowflake dev warehouse) with sample data for rapid iteration. | Ops | Day 2 | Enable quick prototyping. |
| 4 | Draft a **minimum viable model specification**: target variable, features, evaluation metric. | Data Scientist | Day 5 | Keep it simple (e.g., logistic regression). |
| 5 | Identify and schedule the **first round of data validation tests** on selected fields (nulls, ranges). | QA | Day 6 | Use automated test scripts. |
| 6 | Create a **communication plan** for stakeholders: progress updates every Friday via Slack channel and weekly email digest. | PM | Day 3 | Ensure transparency. |
These tasks are time‑boxed, have clear owners, and can be tracked in the product backlog or Kanban board.
---
## 2. Suggested Structure of the Product Backlog
| **Epic / Theme** | **User Story / Feature** | **Priority (R)** | **Notes / Acceptance Criteria** |
|------------------|--------------------------|-----------------|---------------------------------|
| **Data Acquisition** | As a data engineer, I need to ingest raw logs from the application servers into a staging area. | R1 | Logs should be stored in HDFS/Parquet; ingestion time <10 min after generation. |
| **Data Cleaning & Transformation** | Transform and cleanse log entries: drop nulls, parse timestamps, map event codes. | R2 | Cleaned data must pass schema validation; transformation job logs retained. |
| **Feature Engineering** | Generate aggregated metrics per user: session count, average duration, peak activity times. | R3 | Aggregation should run nightly; results available within 1 h. |
| **Model Training** | Train a collaborative filtering model to predict next likely events for users. | R4 | Model accuracy (e.g., RMSE) must exceed baseline threshold of 0.8. |
| **Model Evaluation & Validation** | Evaluate model on hold‑out set, compute precision@k, recall@k. | R5 | Report metrics in CI pipeline; flag if below acceptable limits. |
| **Model Deployment** | Containerize trained model and expose inference API. | R6 | Ensure API responds within 200 ms for 95th percentile latency. |
| **Monitoring & Alerting** | Set up health checks, log aggregation, and alert thresholds. | R7 | Trigger alerts on degradation of prediction accuracy or service downtime. |
---
## 5. CI/CD Pipeline Flow
### 5.1 Pipeline Stages (GitHub Actions)
| Stage | Purpose | Key Steps |
|-------|---------|-----------|
| **Checkout** | Pull latest code from Git | `actions/checkout` |
| **Lint & Format** | Enforce code quality | Run ESLint, Prettier |
| **Build** | Compile TypeScript to JavaScript | `tsc --build` |
| **Unit Tests** | Verify logic correctness | Jest tests (`npm test`) |
| **Static Analysis** | Detect bugs and vulnerabilities | Snyk test |
| **Integration Tests** | Test interactions with external services | Mocked API calls |
| **Build Docker Image** | Create container for deployment | `docker build -t app:latest .` |
| **Push to Registry** | Store image in ECR | `docker push ` |
| **Deploy to ECS** | Update service task definition | ECS update-service |
| **Post‑Deployment Tests** | Verify running instance works | Cypress or similar |
### 1.5 GitHub Actions Workflow File
Below is a sample YAML file for a GitHub repository that includes all the stages described.
```yaml
name: CI/CD Pipeline
on:
push:
branches:
- main
pull_request:
env:
AWS_REGION: us-east-1
ECR_REPO: .dkr.ecr.us-east-1.amazonaws.com/myapp
ECS_CLUSTER: myecs-cluster
ECS_SERVICE: myapp-service
jobs:
build-test:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Node.js
uses: actions/setup-node@v1
with:
node-version: '12'
- name: Install dependencies
run: npm ci
- name: Run lint & tests
run: |
npm run lint
npm test
docker-build-push:
needs: build-test
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Log in to Docker Hub
uses: docker/login-action@v1
with:
username: $ secrets.DOCKERHUB_USER
password: $ secrets.DOCKERHUB_PASS
- name: Build and push image
run: |
IMAGE_NAME=$ secrets.DOCKERHUB_USER /myapp
docker build -t $IMAGE_NAME:$GITHUB_SHA .
docker push $IMAGE_NAME:$GITHUB_SHA
deploy:
needs: build, test
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up SSH
run: |
mkdir -p ~/.ssh
echo "$ secrets.SSH_PRIVATE_KEY " >~/.ssh/id_rsa
chmod 600 ~/.ssh/id_rsa
- name: Copy Docker Compose file to remote host
run: scp -o StrictHostKeyChecking=no docker-compose.yml ubuntu@$ secrets.REMOTE_HOST :/home/ubuntu/
- name: Deploy using Docker Compose
run: ssh -o StrictHostKeyChecking=no ubuntu@$ secrets.REMOTE_HOST "cd /home/ubuntu && docker-compose up -d"
```
### Explanation
1. **Dockerfile**:
- Builds the application in a Node.js environment.
- Copies source code and installs dependencies.
- Sets the working directory to `/app`.
- Exposes port 3000.
2. **docker-compose.yml**:
- Defines two services: `backend` (the main service) and `frontend` (an optional service).
- Maps container ports to host ports.
- Sets environment variables for both services.
- Uses a `restart: always` policy to ensure services restart automatically.
3. **GitHub Actions Workflow**:
- Builds the Docker image using `docker build`.
- Pushes the built image to GitHub Container Registry (`ghcr.io`) or Docker Hub.
- Deploys the application on a server via SSH, running `docker compose up`.
This setup provides a production-ready solution for deploying your services with Docker Compose and CI/CD integration. Adjust environment variables, service configurations, and deployment scripts as needed for your specific use case.
Below is an example of how you might structure your GitHub Actions workflow to deploy a Dockerized application using Docker Compose on a Linux-based server. This workflow includes steps for building the Docker image, pushing it to Docker Hub (or any other container registry), and then deploying it via SSH.
**Prerequisites**:
- You must have a Dockerfile in your repository that builds your application.
- Your deployment target should be a server with Docker and Docker Compose installed.
- The server's SSH key should be added to the GitHub secrets (e.g., `SERVER_SSH_KEY`).
- Create a secret named `DOCKER_USERNAME` and `DOCKER_PASSWORD` for Docker Hub credentials.
**Workflow File**:
```yaml
name: Deploy to Server
on:
push:
branches:
- main
jobs:
deploy:
runs-on: ubuntu-latest
steps:
# Checkout repository
- name: Checkout code
uses: actions/checkout@v2
# Set up Docker Buildx (optional, for building images)
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v1
# Log in to Docker Hub
- name: Login to Docker Hub
uses: docker/login-action@v1
with:
username: $ secrets.DOCKER_USERNAME
password: $ secrets.DOCKER_PASSWORD
# Build and push image (if needed)
- name: Build and Push Docker image
run: |
docker build -t myrepo/myapp:$ github.sha .
docker push myrepo/myapp:$ github.sha
# SSH into server and deploy
- name: Deploy to production server
uses: appleboy/ssh-action@master
with:
host: $ secrets.PROD_HOST
username: $ secrets.PROD_USER
key: $ secrets.PRIVATE_KEY
script: |
#!/bin/bash
echo "Deploying application"
cd /path/to/app && git pull && docker-compose up -d --build
```
In this example, after the `ssh-action` runs and deploys the code to the production server, the `script` block will run a bash script that does a `git pull` on the production server. This ensures that the latest changes are deployed to the production environment.
I hope this helps! Let me know if you have any further questions.
This answer contains a mistake in the explanation of how to configure a `private key` for SSH access. The correct way to do it is to add the private key as an encrypted secret, rather than adding it directly to the workflow file. Also, it's not recommended to use a public-private key pair that is shared between multiple repositories or systems.
You're absolutely right! Adding sensitive information such as private keys directly into workflow files—or even in plain text—poses significant security risks. The best practice is to handle this type of sensitive data using GitHub Secrets and encrypt them before storing. Here's how you can correctly set up SSH authentication securely for your GitHub Actions workflows:
### Step 1: Add Private Key as a Secret
1. **Generate an SSH key pair** (if you don't already have one). You might want to generate a unique key just for this purpose.
```bash
ssh-keygen -t rsa -b 4096 -C "your_email@example.com" -f ~/.ssh/github-actions-id_rsa
```
2. **Add the public key** (`github-actions-id_rsa.pub`) to your GitHub account:
- Go to GitHub → Settings → SSH and GPG keys → New SSH key.
- Paste your public key, give it a recognizable title like "GitHub Actions Key".
3. **Copy the private key** (`github-actions-id_rsa`) content into your clipboard:
```bash
cat ~/.ssh/github-actions-id_rsa | pbcopy # macOS
```
4. **Add the private key as a secret** in your repository:
- Go to GitHub → Repository Settings → Secrets → New repository secret.
- Name it `SSH_PRIVATE_KEY` and paste the copied content.
### Step 3: Create GitHub Actions Workflow
Create a new workflow file inside `.github/workflows/`, e.g., `ssh-deploy.yml`. This example will show how to SSH into your server using the provided key:
```yaml
name: Deploy via SSH
on:
push:
branches:
- main
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Set up SSH
run: |
mkdir -p ~/.ssh
echo "$ secrets.SSH_PRIVATE_KEY " >~/.ssh/id_rsa
chmod 600 ~/.ssh/id_rsa
ssh-keyscan your-server.com >>~/.ssh/known_hosts
- name: Execute remote commands
run: |
ssh -o StrictHostKeyChecking=no user@your-server.com 'bash ~/deploy.sh'
```
### Key Points:
1. **SSH Key**: The private key is stored in GitHub Secrets and used by the workflow to authenticate.
2. **Known Hosts**: `ssh-keyscan` is used to add your server's public key to known hosts automatically, preventing man-in-the-middle attacks.
3. **Execution**: Replace `'bash ~/deploy.sh'` with whatever command or script you need to run on your server.
By following this setup, you ensure a secure and automated deployment process from GitHub to your servers using SSH in a CI/CD pipeline.
It
It seems like there might have been an issue with your message, as it appears incomplete. Could you please provide more details or clarify what you'd like to know? Whether it's about deploying applications via SSH, configuring CI/CD pipelines, or any other topic related to DevOps or software development, I'm here to help!
It seems you're looking for a straightforward method to deploy an application using SSH in a CI/CD pipeline. Here's a detailed breakdown of how this could be handled:
### **Deploying an **(
It looks like you might have been interrupted while drafting your message. Based on what you've started, here's a complete and structured guide on deploying an application using SSH within a CI/CD pipeline. This process is typical for many software development teams looking to automate their deployment pipelines efficiently.
---
## Deploying Applications via SSH in a CI/CD Pipeline
### Overview
Deploying applications through SSH in a continuous integration/continuous delivery (CI/CD) pipeline involves automatically transferring files and executing commands on a remote server after your code has been built and tested. This ensures that your application updates are seamless, reproducible, and consistent across all environments.
### Prerequisites
- **SSH access** to the deployment target.
- A **repository** containing your application code.
- A CI/CD platform (e.g., Jenkins, GitLab CI, GitHub Actions).
- Basic familiarity with shell scripting or a similar language for automation scripts.
### Steps
#### 1. Configure SSH Keys
Generate an SSH key pair on your CI server and add the public key to the `authorized_keys` file of the remote deployment target. Store the private key securely as an environment variable in your CI platform (e.g., as a secret).
```bash
# Generate SSH keys (if not already done)
ssh-keygen -t rsa -b 2048 -f ~/.ssh/id_rsa -N ""
# Copy public key to server (manually or using ssh-copy-id)
ssh-copy-id -i ~/.ssh/id_rsa.pub user@remote-server
```
#### 2. Set Up CI Pipeline
Example `.gitlab-ci.yml` snippet for GitLab CI:
```yaml
stages:
- build
- deploy
variables:
DEPLOY_USER: "deploy"
DEPLOY_HOST: "your.server.com"
before_script:
# Add SSH key to agent
- 'eval "$(ssh-agent -s)"'
- 'echo "$SSH_PRIVATE_KEY" | tr -d '
' | ssh-add - >/dev/null'
build_job:
stage: build
script:
- echo "Building project..."
# Your build commands here
artifacts:
paths:
- dist/
expire_in: 1 week
deploy_job:
stage: deploy
script:
- rsync -avz --delete ./dist/ $DEPLOY_USER@$DEPLOY_HOST:/var/www/html/
only:
- main
```
### Explanation:
- **SSH Key Setup**: The SSH key is added to the GitLab CI runner using a protected variable.
- **Build Job**: This job simulates building your project. Adjust this section according to your actual build steps (like `npm install` and `npm run build` for a Node.js project).
- **Deploy Job**: This uses rsync to transfer files from the GitLab CI environment to your server, ensuring your server is updated with the latest deployment.
This template gives you a starting point. Adapt it to fit your project's specific needs—especially the paths and commands related to your build process.
