Cloud-Based Workflow Optimization

I usually spend some of my Christmas break teaching myself something I need to know for the following year , this years topic has been AI Agents written to Microsoft’s Autogen framework , but found there is very little information on running them at scale , found YouTube great resource for content this video on how LLMs work is very helpful https://lnkd.in/g8XaXfeE My use case is an Agent to create LandingZones in Terraform for Cloud platforms, I love that the developer is back in the hot seat Running AutoGen agents at scale requires a robust infrastructure for computation, storage, and networking. Leveraging cloud platforms is typically the most efficient way to achieve this due to their scalability, flexibility, and availability of AI-specific services. Here’s a breakdown of best practices for running AutoGen agents at scale on the cloud: 1. Choose a Cloud Platform • Top Options: • AWS (Amazon SageMaker, EC2, Lambda) • Google Cloud (Vertex AI, Compute Engine, Kubernetes Engine) • Azure (Azure ML, Azure Functions, AKS) 2. Orchestrate with Containerization • Why? Containers ensure consistency, portability, and efficient resource utilization. • Use Docker to package your AutoGen agents and their dependencies. • Deploy with Kubernetes (K8s) for dynamic scaling and orchestration. • For example, Kubernetes can scale AutoGen agents up/down based on workload 3. Utilize Serverless Architectures • When to use serverless? • For agents with short-lived tasks and intermittent workloads. • Benefits: You pay only for compute time, and the cloud handles scaling. • Examples: • AWS Lambda • Google Cloud Functions • Azure Functions 4. Use Managed Machine Learning Services • Platforms like AWS SageMaker, Google Vertex AI, or Azure ML simplify model training, deployment, and inference. These services often integrate with containerization and orchestration tools. 5. Build an Event-Driven Workflow • Use tools like Apache Kafka, AWS SQS, or Google Pub/Sub for asynchronous communication between agents. • Benefits: Decouples agent interactions and scales independently. 6. Optimize Cost and Resources • Spot Instances/Preemptible VMs: For non-time-critical workloads, leverage low-cost compute options. 7. Employ Distributed Computing • Use frameworks like Ray or Dask to parallelize and scale distributed tasks efficiently. 8. Monitor and Manage Agents • Use monitoring tools like Prometheus, Grafana, or cloud-native tools (e.g., AWS CloudWatch, Azure Monitor). • Employ logging and tracing (e.g., ELK Stack, Jaeger) to debug and improve agent performance. 9. Consider AI-Specific Infrastructure • Use cloud GPUs/TPUs for high-performance AI workloads (e.g., AWS EC2 G4, Google TPU Pods, Azure NC series). 10. Use CI/CD for Fast Iteration • Integrate Continuous Integration and Deployment pipelines (e.g., GitHub Actions, GitLab CI/CD, AWS CodePipeline). • Automate updates and scaling for AutoGen agents.

#Workflow Managers! Workflow managers like #Nextflow, #Snakemake, #CWL, #WDL (#cromwell), #ensembl‑hive, and others act as orchestrators/conductors. They: 🔹 Define dependencies between tasks (e.g. FASTQ → alignment → variant calling) 🔹 Use executors to send jobs to HPC, cloud, Kubernetes, etc. (e.g. Slurm, AWS Batch, LSF, SGE) 🔹 Track status, retries, logging, error handling, and provenance 🔹 Allow workflows to be reproduced and resumed, even mid‑execution with caching 🔹 They support containers, resource specs, and automatic parallelisation through portable DSLs or config ➿ Workflow Patterns Workflow managing tools essentially build and run Directed Acyclic Graphs (DAGs). Common execution patterns use asynchronous type communication and include: 🪭 Fan – one task splits into multiple parallel jobs (e.g. process 100 samples). 🍸 Funnel – results gathered and merged back into one downstream task. ⛔ Semaphore or Barrier – wait until all tasks in a stage finish before continuing. ❓ Conditional execution – run tasks only if e.g. QC fails. These patterns enable flexible, parallel, and reproducible pipelines across all major systems. ℹ️ Scaling, Performance & IO Tips 🔸 Batch and Chunk High-Memory or Heavy-IO Jobs/ Divide-and-Conquer Strategy For memory-intensive tools, partition/split data (e.g. chromosomes, bam file regions) and run parallel subprocesses before merging (funnelling) - this is beneficial to reduce RAM requirements and helps to mitigate exit 137 OOM issues. 🔸 Beware Heavy I/O Steps Tasks like indexing or sorting in many tools can saturate disk space. Use local scratch space (e.g. `$TMPDIR`) or use RAM-disks/IO optimised compute instances, and delete intermediate files as soon as they’re no longer needed. 🔸 Specify Resources Explicitly Always define accurate CPU, memory, and time requirements with slight contingency. Overcommitting kills performance; under-allocating introduces job failures. 🔸 Leverage Caching & Resume Features Nextflow, Snakemake, CWL, WDL and ensembl-hive all support resuming where things did not complete or something changed - ideal for long-running or costly tasks. It saves costs and time (and the environment). Watch out for unintended non-deterministic patterns that may break serialisation in Nextflow! (I've been bitten by this!). 🔸 Authorise Executors Thoughtfully Aim for executors that work with containerisation (Docker, Singularity/apptainer etc), but tune your cluster/batch submission parameters (e.g. job arrays vs scatter, progressive best fit, spot allocation etc). 🔸 Avoid Workflow Overhead Thousands of small jobs can slow down the scheduler. Group trivial tasks where possible. Hope this acts as a good reminder/quick guide, let me know in the comments if you have any other workflow-manager-agnostic, or workflow-manager-specific tips and tricks - which workflow manager do you most predominantly use?

From 6 hours → 33 minutes. No new nodes. No extra cost. Just patience, tuning… and a few late nights. A few weeks ago, I was staring at a Spark job in Azure Databricks that took over 6 hours to finish. Every run felt like waiting for a flight that kept getting delayed. Fast forward a week — the same job now finishes in 33 minutes. Here’s how it actually happened 👇 1️⃣ Data skew — the silent killer A few partitions were overloaded while others chilled. Repartitioned on the right key + compacted tiny Parquet files = instant balance. 2️⃣ Delta done right ZORDER on frequently filtered columns + batched merges = faster reads, fewer headaches. 3️⃣ Cluster tuning over cluster growing Instead of adding nodes, we resized workers and enabled autoscaling with spot instances. More efficiency, less cost. 4️⃣ Selective caching Only cached reused DataFrames. The rest? Straight through — saved tons of memory. 5️⃣ Photon magic Turned it on for SQL workloads. Zero code change. Immediate boost. 6️⃣ Monitored like crazy Shuffle reads, skew, executor utilization — Spark UI told us everything we needed to fix. It wasn’t one big change. It was a series of small, consistent improvements that compounded into a 10x performance gain. Now this workflow saves hours of runtime and thousands in compute — every single day. Sometimes, optimization isn’t about doing more. It’s about doing smarter. 💭 What’s the biggest performance win you’ve pulled off in Databricks? Drop it below — I’ll feature the most interesting one in my next post! COE – https://lnkd.in/gbSWWxKa COE LinkedIn - https://lnkd.in/gCfiMH_K COE Fabric Learning Group- https://lnkd.in/g7mQm_Cg #AzureDatabricks #ApacheSpark #DataEngineering #BigData #PerformanceTuning #Optimization

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

Post 16: Real-Time Cloud & DevOps Scenario Scenario: Your organization manages a critical API on Google Cloud Platform (GCP) that experiences traffic spikes during peak hours. Users report slow response times and timeouts, highlighting the need for a scalable and resilient solution to handle the load effectively. Step-by-Step Solution: Use Google Cloud Load Balancing: Deploy Google Cloud HTTP(S) Load Balancer to distribute incoming traffic across backend instances evenly. Enable global routing for optimal latency by routing users to the nearest backend. Enable Autoscaling for Compute Instances: Configure Managed Instance Groups (MIGs) with autoscaling based on CPU usage, memory utilization, or custom metrics. Example: Scale out instances when CPU utilization exceeds 70%. yaml Copy code minNumReplicas: 2 maxNumReplicas: 10 targetCPUUtilization: 0.7 Cache Responses with Cloud CDN: Integrate Cloud CDN with the load balancer to cache frequently accessed API responses. This reduces backend load and improves response times for repetitive requests. Implement Rate Limiting: Use API Gateway or Cloud Endpoints to enforce rate limiting on API calls. This prevents abusive traffic and ensures fair usage among users. Leverage GCP Pub/Sub for Asynchronous Processing: For high-throughput tasks, offload heavy computations to a message queue using Google Pub/Sub. Use workers to process messages asynchronously, reducing load on the API service. Monitor Performance with Stackdriver: Set up Google Cloud Monitoring (formerly Stackdriver) to track key metrics like latency, request count, and error rates. Create alerts for threshold breaches to proactively address performance issues. Optimize Database Performance: Use Cloud Spanner or Cloud Firestore for scalable and distributed database solutions. Implement connection pooling and query optimizations to handle high-concurrency workloads. Adopt Canary Releases for API Updates: Roll out updates to a small percentage of users first using Cloud Run or Traffic Splitting. Monitor performance and rollback if issues arise before full deployment. Implement Resiliency Patterns: Use circuit breakers and retry mechanisms in your application to handle transient failures gracefully. Ensure timeouts are appropriately configured to avoid hanging requests. Conduct Load Testing: Use tools like k6 or Apache JMeter to simulate traffic spikes and validate the scalability of your solution. Identify bottlenecks and fine-tune the architecture. Outcome: The API service scales dynamically during peak traffic, maintaining consistent response times and reliability.Enhanced user experience and improved resource efficiency. 💬 How do you handle traffic spikes for your applications? Let’s share strategies and insights in the comments! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Let’s learn and grow together! #DevOps #CloudComputing #GoogleCloud #careerbytecode #thirucloud #linkedin #USA CareerByteCode

If you’re in cloud and not looking at optimization end-to-end, you’re missing out — here are the key strategies you should know.. → Compute ↳ Right-size instances, use auto-scaling/serverless, and leverage spot/preemptible VMs ↳ Consolidate workloads with Kubernetes/Fargate/Cloud Run → Storage ↳ Use lifecycle policies to move infrequently used data to cheaper tiers ↳ Deduplication, compression, and smart replication strategies reduce costs → Networking ↳ CDN for static content, private networking to cut egress, and traffic shaping with load balancers ↳ Always optimize data transfer (avoid unnecessary cross-region costs) → Databases ↳ Use managed services, read replicas, and caching ↳ Shard/partition for scale, and pick the right DB for the workload → Big Data ↳ Spot clusters for jobs, serverless analytics, and data partitioning ↳ Stream only what’s critical, batch the rest → Security ↳ Enforce least privilege IAM, encrypt in transit/at rest ↳ Automate threat detection and centralize secrets with KMS/Vault → AI/ML ↳ Track experiments, use AutoML/pre-trained APIs ↳ Share GPUs, and clean/optimize data before training Essential Note: Cloud optimization isn’t a one-time exercise. You have to keep at it — especially now, with AI workloads driving cloud costs to new highs. Start with one area → measure impact → repeat. What other strategies would you add? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

Most companies think they’re doing data science. The truth? Without AI-first workflows, they’re just doing data cleanup. If you use data at work, this new AI-first workflow will transform how you lead, decide, and deliver insights. I’ve spent the last several years building world-class conversational analytics platforms for some of the world’s top businesses, platforms that support billions of dollars in decisions every year. And I can tell you this: The Google Cloud Data Science Guide is groundbreaking. Why? Because it finally shows how to run data science in the age of AI, not in theory, but in practice. Here are 5 game-changing insights from the guide: ⨠ AI removes the grunt work. Agents now automate cleaning, feature engineering, and pipeline building so you stay in flow. ⨠ Notebooks go AI-first. Colab Enterprise and Vertex AI Workbench let you jump from SQL to Python to Spark seamlessly, with AI drafting code and fixing errors. ⨠ Unstructured data is unlocked. Images, audio, contracts, all queryable in BigQuery like regular tables. Multimodal analysis is now standard. ⨠ Modeling lives in the warehouse. BigQuery ML lets you train, evaluate, and deploy ML models with SQL, no messy data movement. ⨠ MLOps is finally integrated. From feature store to model registry to deployment, Vertex AI and BigQuery are stitched into a single workflow. This isn’t the future. This is available today. The future of analytics belongs to those who can blend business vision with cloud-native, AI-powered workflows. 👉 Download the guide. Study it. Build with it. It will redefine how you think about data science. ___________________________________________ 👋 I’m Amit Rawal, Director of AI-led Business Transformation at Google Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. 🧠 Join my free masterclass: Design Your Life with AI Learn how to work smarter, live longer, and grow richer, with AI as your co-pilot. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better.