BIO HELP LEARNING COURSE

Cloud and DevOps for Computational Biologists

Build, test, deploy and scale bioinformatics software like an engineering team: Git workflows, CI/CD, Docker, Terraform, AWS, Kubernetes and MLOps for omics data.

Programming

◉ Advanced◷ 30 Hours▣ Certificate Included

Course Information

Everything you need to know before enrolling.

MODE OF LEARNINGLive Classes

LANGUAGEEnglish

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Live online classes
Group & college batches
Certificate on completion

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▤ Overview ☷ Curriculum ♙ Instructor ☆ Reviews ▣ FAQs

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About This Course

Most bioinformatics code runs on one laptop or one cluster and breaks when someone else tries to use it. Biotech companies, sequencing providers and large research groups now expect bioinformaticians to ship software the way engineering teams do: tested, containerised, deployed to the cloud and able to scale from 10 samples to 10,000.

This course teaches that skill set using biological data throughout. You turn analysis scripts into a tested Python package, automate testing and image builds with GitHub Actions, write production-quality Docker images for tools like samtools and STAR, and provision AWS infrastructure with Terraform. You then run Nextflow pipelines on AWS Batch and Kubernetes, store results as Parquet on S3 and query them with Athena and DuckDB, serve results through an API and dashboard, track ML models with MLflow, and add monitoring, cost controls and data-protection safeguards for genomic and patient data.

Who this course is for

Bioinformaticians moving into industry roles such as Bioinformatics Engineer, Research Software Engineer or Cloud Bioinformatician
PhD students, postdocs and core-facility staff who need to scale analyses beyond a single server or HPC cluster
Computational biologists who build tools or pipelines that other people use
Software and DevOps engineers moving into biotech who want to learn the domain’s data and tools

Prerequisites

Reproducible Pipelines with Nextflow, Snakemake, Docker and HPC (or equivalent experience)
Comfortable with the Linux command line, basic Git, and Python
Has run at least one complete NGS workflow (for example RNA-seq or WGS)
A laptop with 16 GB RAM recommended; Docker Desktop or Linux with Docker
An AWS account (free tier). Budget alerts are set up on Day 6 so cloud costs stay small, and every lab ends with a clean-up step.

Final project

Build and deploy a small genomics platform end to end. A raw-FASTQ QC and alignment pipeline is packaged in containers and tested in CI. The infrastructure is defined in Terraform, the pipeline runs on AWS Batch or Kubernetes, results land on S3 as Parquet, and an API and dashboard serve them, with monitoring and a cost report. You present the architecture and a live demo.

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What You’ll Learn

✓Use professional Git workflows: branching, pull requests, code review and pre-commit hooks

✓Package bioinformatics code as an installable, tested Python command-line tool

✓Build CI/CD pipelines with GitHub Actions that test code and publish container images

✓Write small, secure, reproducible Docker images for bioinformatics tools

✓Run multi-service applications with Docker Compose (API, database, dashboard)

✓Work confidently with core AWS services: IAM, EC2, S3, VPC, Batch and CloudWatch

✓Define cloud infrastructure as code with Terraform

✓Scale Nextflow pipelines on AWS Batch with spot instances

✓Deploy and run workloads on Kubernetes, locally and on Amazon EKS

✓Store and query large omics results with Parquet, DuckDB and Athena

✓Track, version and serve ML models with MLflow, DVC and FastAPI

✓Monitor pipelines, control cloud costs and protect genomic and patient data

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Tools & Technologies

GitGitHub ActionsPythonpytestDockerDocker ComposeTrivyAWS EC2 & S3AWS IAMAWS BatchAmazon EKSAmazon AthenaTerraformKubernetesHelmArgo WorkflowsKarpenterNextflowSeqera PlatformParquetDuckDBPrefectFastAPIPostgreSQLStreamlitMLflowDVCPrometheus & Grafana

Course Curriculum

What you will learn, day by day

15 days 30 hours of training 63 topics Certificate on completion

- Git beyond the basics: branching strategies, rebase vs merge, resolving conflicts
- Pull requests and code review on GitHub
- Pre-commit hooks for formatting and linting
- Remote development on a server with VS Code
- Turning analysis scripts into an installable Python package with pyproject.toml
- Building command-line tools with Typer
- Logging, configuration files and error handling
- Testing with pytest using small FASTQ and VCF test files
- GitHub Actions workflows: run tests on every pull request
- Linting and type checking with Ruff and mypy
- Building and publishing container images to GitHub Container Registry
- Releasing a tool: versioning, changelogs and a Bioconda recipe
- Multi-stage builds for compiled tools (samtools, STAR)
- Smaller images, layer caching and pinned versions
- Using and extending BioContainers
- Image security scanning with Trivy
- pinning images by digest
- Docker Compose: an API, a database and a dashboard together
- A FastAPI service that serves variant annotations from PostgreSQL
- A Streamlit dashboard for QC results
- Environment variables, secrets and volumes done safely
- IAM users, roles and least-privilege policies
- EC2 instance types for memory-heavy tools
- S3 storage classes and lifecycle rules for raw FASTQ
- VPC basics, the AWS CLI and boto3
- Budgets, cost alerts and tagging, set up before anything else
- Why click-ops fails for reproducible science
- Terraform providers, resources, variables and state
- Creating S3 buckets, IAM roles and a compute instance from code
- Reusable modules and safe plan/apply/destroy habits
- Running Nextflow on AWS Batch: compute environments and job queues
- Spot instances, retries and cost per sample
- Storing work directories and results on S3
- Overview of AWS HealthOmics and Seqera Platform, and when to use them
- Pods, Deployments, Services and Jobs
- ConfigMaps, Secrets and resource requests/limits
- A local cluster with kind and kubectl
- Debugging pods: logs, describe and exec
- Running Nextflow pipelines on Kubernetes
- Persistent volumes for reference genomes and indexes
- Argo Workflows for container-native pipelines
- Packaging apps with Helm charts
- Creating an EKS cluster with Terraform
- Node autoscaling with Karpenter, including spot nodes
- GPU nodes for AlphaFold and deep learning jobs
- Deploying the Day 5 API and dashboard to EKS
- Columnar formats: converting VCF and count tables to Parquet
- Fast local queries with DuckDB
- Querying results on S3 with Athena
- Scheduling recurring data jobs with Prefect
- Experiment tracking and model registry with MLflow
- Versioning datasets with DVC
- Serving a trained classifier as a FastAPI endpoint
- Reproducibility checks for ML on omics data
- Metrics and dashboards with Prometheus and Grafana
- logs in CloudWatch
- Secrets management and encryption at rest and in transit
- Access control and audit logs for genomic data
- Data-protection rules for patient data: India's DPDP Act 2023, GDPR and HIPAA basics
- Putting the platform together: CI-tested pipeline → Terraform infrastructure → AWS Batch/EKS → S3/Parquet → API and dashboard
- Cost report and clean-up of all cloud resources
- Architecture review and feedback
- 10-minute live demo and certificate assessment

♙

Your Instructor

Bio Help Learning Educator

Bioinformatics Educator & Mentor

Learn from experienced educators and practitioners through practical, career-focused bioinformatics training.

Frequently Asked Questions

7 answers about this course

How is this different from the Reproducible Pipelines course?

Reproducible Pipelines teaches you to make an analysis rerunnable: Git, Conda, containers, Snakemake, Nextflow and HPC. This course teaches you to engineer and operate bioinformatics software in production: CI/CD, cloud infrastructure, Kubernetes, APIs, data engineering, MLOps, monitoring and security. Take Reproducible Pipelines first.

Will I have to pay for AWS?

Most labs fit within the AWS free tier or cost very little. On Day 6 you set up budget alerts, and every lab ends with a clean-up step so nothing keeps running. Exact costs depend on your usage.

Do I need to know Kubernetes or AWS already?

No. Both are taught from the basics, but the pace is fast because you are expected to be comfortable with Linux, Git, Python and Docker.

Does it cover Google Cloud or Azure?

The labs use AWS because it is the most common cloud in biotech job listings. Each cloud session also shows the equivalent Google Cloud and Azure services, so the skills carry over.

What jobs does this prepare me for?

Bioinformatics Engineer, Research Software Engineer, Cloud Bioinformatician, Data Engineer (life sciences) and MLOps roles at biotech, pharma and genomics companies.

Will I get a certificate?

Yes. You get a verifiable Bio Help Learning certificate after you complete the capstone.

How long will I have access to the course?

You get full access to all course materials for 3 months from the date of enrollment.

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WHAT OUR LEARNERS SAY

Real Stories. Real Growth.

Hear from learners building practical skills with Bio Help Learning.

★★★★★

This training helped me develop practical skills in whole-genome sequencing analysis, from read quality control using FastQC and MultiQC to read trimming, genome alignment, coverage assessment, and variant calling. Working with a real public dataset helped me understand how different bioinformatics tools fit together in a complete analytical workflow. The hands-on activities strengthened my confidence in using command-line tools and interpreting genomic data. I particularly appreciated the practical approach and the opportunity to apply the concepts through a real project.

Yao Apélété Roland TOGLOUniversity of Lomé TogoWhole Genome Sequencing (WGS) Data Analysis Training

★★★★☆

I really enjoyed the course and found it very useful: it gave a clear and practical overview of the whole WGS workflow, from raw reads to a VCF, and the hands-on sessions helped a lot.

Marc Peralta SerranoUniversity of Barcelona SpainWhole Genome Sequencing (WGS) Data Analysis Training

★★★★★

The workshop provided me an understanding of the principles and techniques involved in bioinformatics, molecular docking, protein-ligand interactions, homology modeling, and drug design.

Ridam BhowmickMaulana Abul Kalam Azad University of Technology, West Bengal.Molecular Docking and Drug Discovery

★★★★★

As i am from biotechnology background this workshop, actually very helpfull for me

Anushka ChavanDeogiri college AurangabadBioinformatics Training

★★★★★

Learn about different molecular docking tools.

Shovan NaskarIndian Institute of Technology (Banaras Hindu University), VaranasiBioinformatics Workshop

★★★★★

This workshop helped me to learn working with various softwares and also to develop an interest over drug designing

Subhodeep SahaUniversity of CalcuttaBioinformatics Training

★★★★★

From this workshop I learned many new things such as docking, about new websites,how to use Swiss software,basic bioinformatics etc, it's very helpful for me.

Sayan DuttaUniversity of CalcuttaBioinformatics Training