{"id":4737,"date":"2026-03-17T21:42:47","date_gmt":"2026-03-17T16:12:47","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/"},"modified":"2026-03-17T21:42:47","modified_gmt":"2026-03-17T16:12:47","slug":"machine-learning-best-practices-7-tips-for-success","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/","title":{"rendered":"Machine Learning Best Practices: 7 Tips for Success"},"content":{"rendered":"<p><strong>INTERNAL POST-MORTEM: PROJECT &#8220;ICARUS&#8221; \/ INCIDENT REPORT #8842-B<\/strong><br \/>\n<strong>TO:<\/strong> Engineering Leadership, DevOps, and anyone else who thinks they can &#8220;just run a script&#8221;<br \/>\n<strong>FROM:<\/strong> Silas Thorne, Principal Systems Architect (Infrastructure &amp; Recovery)<br \/>\n<strong>SUBJECT:<\/strong> The Smoldering Remains of our &#8220;Machine Learning&#8221; Pipeline<\/p>\n<p>It is 4:42 AM. I have been awake for thirty-eight hours. The air in the server room is thick with the smell of overstressed silicon and the bitter ozone of a failing UPS. I am currently staring at a terminal window that represents the professional epitaph of our former &#8220;Rockstar&#8221; Data Scientist, Chad. Chad has moved on to a &#8220;stealth-mode AI startup&#8221; in Palo Alto, leaving us with a repository that is less of a software project and more of a crime scene.<\/p>\n<p>If you are reading this, it means I have successfully stabilized the production environment, or at least I\u2019ve managed to stop the $450-per-hour bleeding from our AWS account. This document is not a suggestion. It is a mandatory autopsy of why our <strong>machine learning<\/strong> efforts failed and a manifesto for how we will operate moving forward. If you disagree with anything here, my office door is locked, and I am ignoring all Slack notifications until next Tuesday.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69d8540a89872\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69d8540a89872\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#THE_INCIDENT_03_14_AM\" >THE INCIDENT: 03:14 AM<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#1_The_Fallacy_of_the_Infinite_Cloud_Budget\" >1. The Fallacy of the Infinite Cloud Budget<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#2_Dependency_Hell_is_a_Choice_and_You_Chose_Poorly\" >2. Dependency Hell is a Choice (and You Chose Poorly)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#3_Why_Your_Notebook_is_a_Liability_Not_an_Asset\" >3. Why Your Notebook is a Liability, Not an Asset<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#4_Data_Versioning_DVC_or_Death\" >4. Data Versioning (DVC) or Death<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#5_Feature_Drift_and_the_PrometheusGrafana_Altar\" >5. Feature Drift and the Prometheus\/Grafana Altar<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#6_The_Physical_Reality_of_the_Rack\" >6. The Physical Reality of the Rack<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#7_%E2%80%9CIt_Worked_on_My_Laptop%E2%80%9D_is_a_Fireable_Offense\" >7. &#8220;It Worked on My Laptop&#8221; is a Fireable Offense<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#Checklist_for_the_Next_Person_Who_Tries_to_Break_My_Production_Environment\" >Checklist for the Next Person Who Tries to Break My Production Environment<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/#Related_Articles\" >Related Articles<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"THE_INCIDENT_03_14_AM\"><\/span>THE INCIDENT: 03:14 AM<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The following log is the last thing our primary node saw before it decided to commit digital seppuku.<\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\">[2024-05-20 03:14:18] INFO: Starting epoch 42...\n[2024-05-20 03:14:19] DEBUG: Loading batch 14400\/50000\n[2024-05-20 03:14:21] WARNING: Memory pressure detected. System RAM at 98.4%.\n[2024-05-20 03:14:22] ERROR: torch.cuda.OutOfMemoryError: CUDA out of memory. \nTried to allocate 12.50 GiB (GPU 0; 40.00 GiB total capacity; 38.22 GiB already allocated; \n1.12 GiB free; 38.50 GiB reserved in total by PyTorch 2.2.0)\n[2024-05-20 03:14:22] CRITICAL: Kernel Panic - not syncing: Fatal exception in interrupt\n[2024-05-20 03:14:23] CONNECTION_LOST: Worker node ip-10-0-42-11.ec2.internal unreachable.\n<\/code><\/pre>\n<p>The &#8220;Rockstar&#8221; forgot that tensors don&#8217;t magically vanish when you\u2019re done with them if you keep them in a global list for &#8220;later visualization.&#8221; He was caching every single intermediate activation from a transformer model during a production inference run. On a 40GB A100. At 3:00 AM, the garbage collector finally gave up, and the OOM killer took the entire API gateway down with it.<\/p>\n<hr \/>\n<h2><span class=\"ez-toc-section\" id=\"1_The_Fallacy_of_the_Infinite_Cloud_Budget\"><\/span>1. The Fallacy of the Infinite Cloud Budget<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Chad\u2019s philosophy was simple: if the code is slow, throw more compute at it. I found a Terraform script that was spinning up <code>p4d.24xlarge<\/code> instances for &#8220;exploratory data analysis.&#8221; We were spending $32 an hour so a guy could run a regex on a 2GB CSV file.<\/p>\n<p>In this <strong>machine learning<\/strong> pipeline, the data loading was so inefficient that the GPUs were idling 85% of the time, waiting for the CPU to unpickle Python objects. He was using <code>pickle<\/code> for data serialization. In 2024. Not only is that a security nightmare, but it\u2019s also incredibly slow. I\u2019ve replaced this with Apache Arrow and Parquet, but the damage to our quarterly budget is already done.<\/p>\n<p>We are not a charity for NVIDIA. From this point forward, every model training run must have a projected cost-benefit analysis. If you cannot explain why you need 96GB of VRAM to classify customer support tickets, you don&#8217;t get the keys to the cluster. We are moving back to spot instances with aggressive checkpointing. If your code can&#8217;t handle a <code>SIGTERM<\/code> and resume from a saved state, your code isn&#8217;t production-ready.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"2_Dependency_Hell_is_a_Choice_and_You_Chose_Poorly\"><\/span>2. Dependency Hell is a Choice (and You Chose Poorly)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I spent six hours yesterday trying to replicate the environment. Chad\u2019s <code>requirements.txt<\/code> was a work of fiction. It contained <code>torch==2.2.0<\/code>, but the code relied on a specific bug in <code>torchvision 0.17.0<\/code> that was patched three weeks ago. Even worse, he had manually compiled a custom C++ extension against a version of the LLVM compiler that only exists on his specific MacBook Pro.<\/p>\n<p>Here is a snippet of the <code>pip freeze<\/code> I managed to scrape from the dying container:<\/p>\n<pre class=\"codehilite\"><code class=\"language-text\">numpy==1.26.4\npandas==2.2.2\nscikit-learn==1.4.2\nscipy==1.13.0\ntorch==2.2.0\ntorchvision==0.17.0\n# The following were installed via direct git links with no commit hashes:\ngit+https:\/\/github.com\/some-random-repo\/experimental-layers.git\n<\/code><\/pre>\n<p>Do you see that last line? That is a ticking time bomb. That repository was updated two days ago, breaking the API. Because there was no commit hash, the CI\/CD pipeline pulled the &#8220;latest&#8221; version, which was incompatible with our inference logic. <\/p>\n<p><strong>The New Rule:<\/strong> Every dependency must be pinned. Not just the version, but the hash. We are moving to <code>Poetry<\/code> or <code>uv<\/code> for dependency management. If I see a <code>pip install<\/code> without a version number in a Dockerfile, I will personally revoke your sudo access. We are also standardizing on Python 3.11.8. No more &#8220;I&#8217;m using 3.12 for the speed&#8221; while the rest of the stack is on 3.9.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"3_Why_Your_Notebook_is_a_Liability_Not_an_Asset\"><\/span>3. Why Your Notebook is a Liability, Not an Asset<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I found a folder named <code>final_models\/<\/code> containing 42 Jupyter notebooks. They were named <code>train_v1.ipynb<\/code>, <code>train_v2_FIXED.ipynb<\/code>, <code>train_v2_FIXED_FINAL.ipynb<\/code>, and my personal favorite, <code>train_v2_FIXED_FINAL_USE_THIS_ONE_FOR_REAL.ipynb<\/code>.<\/p>\n<p>Notebooks are for sketching. They are not for production. The &#8220;Rockstar&#8221; was running cells out of order, creating a hidden state that made it impossible to reproduce his results. He would run cell 1, then cell 5, then cell 2, and then wonder why the model accuracy was 99% (spoiler: he was leaking the label into the feature set).<\/p>\n<p>When we tried to export this to a <code>.py<\/code> script, the model performance dropped to 60%. Why? Because the notebook had a global variable <code>X_train<\/code> that had been modified in a cell he deleted, but the kernel hadn&#8217;t been restarted in three weeks. <\/p>\n<p><strong>The New Rule:<\/strong> No code goes to production unless it is a modular, testable Python package. If it\u2019s in a <code>.ipynb<\/code> file, it doesn&#8217;t exist. I want to see <code>pytest<\/code> suites for your data transformations. I want to see type hints. If you can&#8217;t run <code>mypy<\/code> on your <strong>machine learning<\/strong> code without it lighting up like a Christmas tree, you aren&#8217;t done.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"4_Data_Versioning_DVC_or_Death\"><\/span>4. Data Versioning (DVC) or Death<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The most terrifying part of this &#8220;machine learning&#8221; pipeline was the data. Or rather, the lack of it. Chad had a script that pulled data from a production SQL database with a <code>WHERE created_at &gt; '2023-01-01'<\/code> clause. <\/p>\n<p>Every time that script ran, the dataset changed. There was no snapshot. No versioning. No way to go back and see what data produced the &#8220;Model_v4&#8221; that is currently hallucinating prices for our European customers. We have no reproducibility. We are essentially practicing alchemy, not engineering.<\/p>\n<p>I have spent the last 12 hours setting up DVC (Data Version Control) version 3.50.1. <\/p>\n<pre class=\"codehilite\"><code class=\"language-yaml\"># dvc.yaml\nstages:\n  process_data:\n    cmd: python src\/process.py data\/raw data\/processed\n    deps:\n      - data\/raw\n      - src\/process.py\n    outs:\n      - data\/processed\n  train_model:\n    cmd: python src\/train.py data\/processed models\/model.pkl\n    deps:\n      - data\/processed\n      - src\/train.py\n    outs:\n      - models\/model.pkl\n<\/code><\/pre>\n<p>If you change a single row in the training set, I want a new hash. I want to be able to <code>git checkout<\/code> a specific commit and have the exact dataset, the exact code, and the exact model weights appear. If we can&#8217;t reproduce a failure, we can&#8217;t fix it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"5_Feature_Drift_and_the_PrometheusGrafana_Altar\"><\/span>5. Feature Drift and the Prometheus\/Grafana Altar<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The &#8220;Rockstar&#8221; told management the model was &#8220;self-correcting.&#8221; That is a lie. Models don&#8217;t self-correct; they degrade. <\/p>\n<p>I checked the logs. Our input data distribution shifted three months ago when the marketing team changed the lead-gen form. The model, built on <code>scikit-learn 1.4.2<\/code>, was expecting a normalized range between 0 and 1. Marketing started sending integers between 1 and 100. The model didn&#8217;t crash; it just started giving garbage outputs. And because we had no monitoring, we\u2019ve been serving garbage to our users for 90 days.<\/p>\n<p>We are now implementing a full observability stack. I don&#8217;t care about your &#8220;accuracy&#8221; on a static test set from last year. I care about the Kolmogorov-Smirnov test results on our live features.<\/p>\n<pre class=\"codehilite\"><code class=\"language-yaml\"># prometheus_exporter_config.yaml\nmetrics:\n  - name: feature_drift_score\n    type: gauge\n    help: &quot;Distance between training and serving data distributions&quot;\n    labels: [feature_name, model_version]\n  - name: prediction_latency_ms\n    type: histogram\n    help: &quot;Time taken for model inference&quot;\n    buckets: [10, 50, 100, 500, 1000]\n<\/code><\/pre>\n<p>Every model endpoint will now export metrics to Prometheus. We will have Grafana dashboards that scream at us when the input distribution changes. If the model starts predicting &#8220;0&#8221; for 95% of requests, I want a PagerDuty alert to wake you up, not me.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"6_The_Physical_Reality_of_the_Rack\"><\/span>6. The Physical Reality of the Rack<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You people think the &#8220;cloud&#8221; is some ethereal dimension where logic lives. It\u2019s not. It\u2019s a series of Dell PowerEdge R750s in a data center in Northern Virginia that are currently running so hot they could bake bread. <\/p>\n<p>When you write an inefficient loop in Python that iterates over a <code>pandas 2.2.2<\/code> DataFrame instead of using vectorized operations, you are physically heating up a room. You are consuming real-world electricity. You are contributing to the heat death of the universe because you were too lazy to learn how <code>numpy<\/code> broadcasting works.<\/p>\n<p>The &#8220;Rockstar&#8221; had a nested loop that was $O(n^2)$ for a join operation that could have been a single hash map lookup. This isn&#8217;t just &#8220;bad code.&#8221; It\u2019s an insult to the engineers who built the hardware you\u2019re abusing. We are going to start auditing the computational complexity of our training scripts. If your &#8220;machine learning&#8221; training job takes 48 hours, I will be looking at your code to see if it <em>should<\/em> take 48 hours, or if you\u2019re just a bad programmer.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"7_%E2%80%9CIt_Worked_on_My_Laptop%E2%80%9D_is_a_Fireable_Offense\"><\/span>7. &#8220;It Worked on My Laptop&#8221; is a Fireable Offense<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The final straw was when I told Chad the production build was failing. His response? &#8220;That&#8217;s weird, it worked on my laptop.&#8221;<\/p>\n<p>Your laptop has 64GB of RAM, a different version of GLIBC, and you\u2019re running macOS. The production environment is a stripped-down Alpine Linux container running on an EPYC processor. Your laptop is irrelevant. If it doesn&#8217;t work in the container, it doesn&#8217;t work.<\/p>\n<p>We are moving to a &#8220;Container-First&#8221; development workflow. You will use DevContainers. You will test your code in an environment that mirrors production. If I hear the words &#8220;on my machine&#8221; one more time, I will personally delete your <code>~\/.ssh<\/code> folder.<\/p>\n<hr \/>\n<h3><span class=\"ez-toc-section\" id=\"Checklist_for_the_Next_Person_Who_Tries_to_Break_My_Production_Environment\"><\/span>Checklist for the Next Person Who Tries to Break My Production Environment<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Before you even think about pushing a &#8220;machine learning&#8221; model to the staging branch, you will verify the following:<\/p>\n<ol>\n<li><strong>Deterministic Seeding:<\/strong> Have you set <code>random.seed()<\/code>, <code>np.random.seed()<\/code>, and <code>torch.manual_seed()<\/code>? If I run your script twice, do I get the exact same weights? If not, go back to the drawing board.<\/li>\n<li><strong>Dependency Lockdown:<\/strong> Is your <code>pyproject.toml<\/code> or <code>requirements.txt<\/code> fully pinned? Does it include the specific version of <code>CUDA<\/code> and <code>cuDNN<\/code> required?<\/li>\n<li><strong>Memory Profiling:<\/strong> Have you run <code>mprof<\/code> or a similar tool to check for memory leaks? Does your memory usage scale linearly with batch size, or is there a hidden leak in your evaluation loop?<\/li>\n<li><strong>Data Integrity:<\/strong> Is the data pulled via a DVC-tracked hash? Is there a schema validation step (using <code>Pydantic<\/code> or <code>Pandera<\/code>) to ensure the input features haven&#8217;t changed?<\/li>\n<li><strong>Logging vs. Printing:<\/strong> Did you remove all <code>print(\"here\")<\/code> statements and replace them with structured logging? Do the logs include the model version and the request ID?<\/li>\n<li><strong>Unit Tests for Logic:<\/strong> Do you have tests for your custom loss functions? Do you have tests for your data augmentation pipeline? (Hint: If your augmentation flips an image but doesn&#8217;t flip the bounding box, your model is learning nonsense).<\/li>\n<li><strong>Resource Limits:<\/strong> Have you defined <code>resources: limits:<\/code> and <code>requests:<\/code> in your Kubernetes manifest? If your pod gets killed for exceeding its limit, do you have a plan for how the system recovers?<\/li>\n<li><strong>The &#8220;Chad&#8221; Test:<\/strong> If I delete your entire home directory right now, can I still rebuild and deploy the model using only what is in the Git repository?<\/li>\n<\/ol>\n<p>I am going home now. I am going to sleep for fourteen hours. When I come back, I expect to see this repository cleaned up. I have left a script in the root directory called <code>cleanup_mess.sh<\/code>. It will delete every <code>.ipynb<\/code> file it finds. You have until I wake up to save your work.<\/p>\n<p>This is not a &#8220;vibrant&#8221; community of &#8220;rockstars.&#8221; This is an engineering department. Start acting like it.<\/p>\n<p><strong>Silas Thorne<\/strong><br \/>\n<em>Principal Systems Architect<\/em><br \/>\n<em>Department of Fixing Other People&#8217;s Mistakes<\/em><\/p>\n<hr \/>\n<p><strong>Technical Specs of the Recovered System (for the record):<\/strong><br \/>\n&#8211; <strong>OS:<\/strong> Ubuntu 22.04.4 LTS (Jammy Jellyfish)<br \/>\n&#8211; <strong>Kernel:<\/strong> 5.15.0-101-generic<br \/>\n&#8211; <strong>NVIDIA Driver:<\/strong> 550.54.14<br \/>\n&#8211; <strong>Python:<\/strong> 3.11.8<br \/>\n&#8211; <strong>PyTorch:<\/strong> 2.2.0+cu121<br \/>\n&#8211; <strong>NumPy:<\/strong> 1.26.4<br \/>\n&#8211; <strong>Pandas:<\/strong> 2.2.2<br \/>\n&#8211; <strong>Scikit-Learn:<\/strong> 1.4.2<br \/>\n&#8211; <strong>DVC:<\/strong> 3.50.1<br \/>\n&#8211; <strong>Prometheus Client:<\/strong> 0.20.0<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Related_Articles\"><\/span>Related Articles<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Explore more insights and best practices:<\/p>\n<ul>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/aws-best-practices-the-ultimate-guide-to-cloud-success\/\">Aws Best Practices The Ultimate Guide To Cloud Success<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/ubuntu-remote-desktop-builtin-screen-sharing\/\">Ubuntu Remote Desktop Builtin Screen Sharing<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/install-wordpress-5-2-on-ubuntu-18-04-with-nginx-mysql-8-and-php-7-3\/\">Install WordPress 5 2 On Ubuntu 18 04 With Nginx Mysql 8 And Php 7 3<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>INTERNAL POST-MORTEM: PROJECT &#8220;ICARUS&#8221; \/ INCIDENT REPORT #8842-B TO: Engineering Leadership, DevOps, and anyone else who thinks they can &#8220;just run a script&#8221; FROM: Silas Thorne, Principal Systems Architect (Infrastructure &amp; Recovery) SUBJECT: The Smoldering Remains of our &#8220;Machine Learning&#8221; Pipeline It is 4:42 AM. I have been awake for thirty-eight hours. The air in &#8230; <a title=\"Machine Learning Best Practices: 7 Tips for Success\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/\" aria-label=\"Read more  on Machine Learning Best Practices: 7 Tips for Success\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4737","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning Best Practices: 7 Tips for Success - ITSupportWale<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-7-tips-for-success\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Best Practices: 7 Tips for Success - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"INTERNAL POST-MORTEM: PROJECT &#8220;ICARUS&#8221; \/ INCIDENT REPORT #8842-B TO: Engineering Leadership, DevOps, and anyone else who thinks they can &#8220;just run a script&#8221; FROM: Silas Thorne, Principal Systems Architect (Infrastructure &amp; Recovery) SUBJECT: The Smoldering Remains of our &#8220;Machine Learning&#8221; Pipeline It is 4:42 AM. 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