Is it possible for us to image a world where 90% of “Data Engineers” as they exist today are not building pipelines, orchestrations, and configurations?

My theory: AI Governance and Cybersecurity (read: Data Privacy) are the fields that are poised for the biggest growth and will see our current data engineers, scientists, and analysts shift more into these roles.

It seems each day, as more people come out and say they don’t write code anymore, that this type of transition becomes more realistic. The thought being we could potentially be removed from the code writing altogether seems to be a frightening possibility for many.
I venture to say that the code writing was never the important part, once you got past the basics of not writing shit code. Rather, the series of decisions we make before we write a line of code are much more telling than the code itself.

That means, finding different places to plug in our skills and capabilities. Being data aware and “data-driven” are great dispositions, now we need to take a step deeper. If we aren’t writing the code, what are doing? We are spending more time on designing systems (so, Systems Design another great path) and ensuring the resulting code/programs **actually do what they’re supposed to do**, and as always, protecting our companies (or ourselves) from lawsuits.

This is why, a few years ago, I recommended to my wife she pursue Cybersecurity (her LinkedIn). As a lawyer in Mexico for the State of Aguascalientes, she had the training to understand how complex legal systems tie together. Now, she’s been learning the important parts of networks, cryptography, math, programming, and other pieces.

We had been deciding between doing Data Science, Web Dev, or something similar. In hindsight, CySec was the only right path, but we wanted to make sure we considered all the options. That was 2023. Now in 2026, this decision absolutely was the right path.

As AI proves it’s not going anywhere, it’s not in a bubble (hot take?), and we’re seeing adoption across the board, we **have** to realize where we are going to be strategically positioned to utilize these tools.

For myself, this means beginning to dive into IAPP CIPP and stitching AI Agents from different tools like CrewAI, LangGraph, and Python. It’s going to be important to understand where we expose ourselves during the process of utilizing AI in different industries. I’m early on in my process as well. But I am 100% convinced this is where we go. Everyone will need to find their own path during this process and where they fit in to this picture. Even if we aren’t 100% only “AI Governance Engineers”, we are “Data Engineers” who understand the legal implications and governance risks of the systems we design to let AI build.

Peace and love,
Sam

PS: I am here to say that I do not believe anyone worth the air they breathe is at risk of losing their career. Maybe a job, but that company has their priorities misaligned and will see its own downfall soon enough. For those of us that are really in this for the long haul, we will adapt as we always have.

Our first short is out.

We will release a short everyday this week until Saturday (hence Saturdata!) when the full first episode is released.

Stay tuned!

How did we get here? Last night after work, I saw 50% boost for anytime HR bets on FanDuel. I said, I’m sure I could build something to get out some probabilities pretty quick with Claude and Cursor.

FAMOUS LAST WORDS.

So, I begin the project. Mkdir, cargo init, and we are off.

Next up, .cursor/rules/requirements.mdc, and throw some stuff in here about what I’m looking for.

First Mistake

Too much detail, not enough organization, pretty much a rambling-type document. I had a lot of ideas in my mind and thought they were more organized than they actually were.

Details like…

What’s going to lead a player to hit a HR, what backend language am I using, what sites to use, when to use HTTP v headless Chrome, telling it to use Context7 whenever necessary.

That's fine for this newsletter, not fine for an LLM.

How to avoid

I needed sections here. Think TOML.

[scraping]

• containing information about how to make certain decisions,

• in BULLET points

I even said “sign off each message as big daddy" (not original idea, but not sure where or who to attribute it to other than Kim my awesome Snowflake Cursor resource) to make sure I could tell Cursor was still using my rules files, and they hadn’t fallen out of the context window. A few times this happened and I started new chat.

This would help tremendously which sections of the rules files to parse. Or even better yet, creating a different rules files for what would be a new section of the TOML so that way I could reference the specific rules files each time.

Btw see this awesome git repo for rules files examples

Have you tried the big daddy technique?

Leave a comment

Second Mistake

I really thought I knew where I was going. I thought it was common sense and the direction was clear.

After 3 hours of wasting my time, I realized I actually had no idea what the architecture or the ELT was actually supposed to look like. And you know what Cursor and Claude did?

It called me stupid and kept stringing me along making me think IT knew what I was inferring about my whole architecture.

Don't. Do. This.

How to Avoid

Take the time before you start YOLO vibing to research sources (or have Gemini Deep Research do it for you idc), know the resources available to you, and then build a system architecture AND ELT flow (if necessary).

This will take you time. You won’t be able to vibe and finish in an hour, but you also won't finish in an hour otherwise. If you don’t know what you’re doing - the LLM will make you the Mayor of Stupid City.

Third Mistake (a catchall of mistakes)

I was vibing in a language I have knowledge of, but not as good control over like Python. Then, I was also just accepting most of the edits Cursor gave. Whenever it said “oh yeah I messed that up, HERE’S the right answer” - I chose to believe it.

Now, I get stuck in an infinite loop with Cursor and am just wasting credits because I don't actually know. Now we are sitting at >3k lines of Rust code which I mostly know what it’s doing but Rust really demands you know what you're doing especially with types.

Anddddd, the LLMs really don't do a good job of cleaning up after themselves. So, I have a lot of spaghetti code or dead code in the codebase now. Rust picks this up pretty easily, but the onus is on me to clean that up.

Lessons Learned

I got excited about building a tool I can use to make me money betting on sports. Who wouldn’t?!

Albeit, I jumped in too fast without understanding the system I wanted to create. I wanted to vibe to profit.

I’m not going to give up on this. But I am most likely trashing the entire experiment from last night, and going to document what APIs I actually want to use, try to avoid scraping as much as possible, and then design the system architecture instead of just jumping right into hitting some APIs and scraping some sites.

Then, I’ll design the data flow through my system. Most likely fall back to Postgres instead of trying to force DuckDB like I was doing, but also, something I need to research and better understand.

Then, once all of this is narrowed down and better understood, go back and create more structured rules files, vibe less and guide more.

Finally, I'll take this slow. When Cursor tells me to add something, asking it why, trying to remove some of this internal bias or assumptions it might be making without fully understanding my system.

Also, this article may be very out of date by the time you read it! Things are moving quickly so take this with the time it was written.

If you don't know what MCP is - there are plenty of great videos on YouTube and articles around the internet that can explain MCP and give you better depth here.

What I wanted to know, and want you know to - is how do you actually use it?

MCP Server Usage

So, the people that create MCP Servers can make them usable a few different ways.

1. Standalone packages

   1. Published to PyPI or NPX, these are very simple to use

   2. More common with more “professional” products, they're easy to plug into your MCP server definitions

   3. Common access patterns might also include access keys to track usage of the MCP Server
      

2. Git Repos

   1. More common on less-commercial products

   2. Why? Automation hasn't yet been setup, either due to bandwidth / cost / any number of reasons

   3. Tracking who is using is less important for the owner

Which Tools Allow us to use MCP Servers

The leaders to utilizing MCP Servers are going to be AI-first IDEs; Cursor and Windsurf for example. But our other more traditional IDEs are also supporting MCP Servers in some way.

Here, I'll talk about PyCharm, VSCode, and Cursor.

PyCharm:

In Tools → AI Assistant → MCP, here you can add your MCP Server that you'd like to use.

Visual Studio Code

In settings.json we can define the MCP Server we'd like to use. There are also a few other settings, including enabling MCP.

If you just search “MCP” in settings, you'll see these options!

Cursor

The easiest, as you might imagine!

As simple as going to: Settings → Cursor Settings → MCP → add new Global MCP server.

How do I define an MCP Server to use?

By asking Claude. LOL.

No but it's pretty simple, just depends on the way they've set up their server as I mentioned earlier. And in some of my examples here you see I use UV with Polars MCP Server.

Polars-Docs in my example above is just for my sake. That's my own naming. Polars on their GitHub page shows us how to set this up.

We can infer from here that we have a UV Project that runs a Python program, pretty simple. All the details are contained within the .py file that Polars provides, which will give us the different pieces needed to be able to use it.

We `git clone` into the repo, and provide that URL to our MCP Server configuration, so it looks like the images above.

Github MCP Server

Just to provide another example, available on their page here. I drop down to where I see the Claude Desktop, since I'm using Cursor, they're example the same config.

I just added this to my `mcp.json` where my Polars MCP Server is as well - and you'll notice here you need an environment variable. Different MCP servers will have different requirements for how you need to format them, and what you need to include.

But noticing how these MCP Servers are called, it's a lot like CMD in a Docker container. Which makes sense, we're just running some commands to access an endpoint (or program) that will give our LLM instructions on how to use whatever the tool is.

Using the MCP Server

So, how do we actually use the MCP Server?

Here, I see all the different commands that are available using the Polars MCP Server.

What if I don't specify to use the MCP Server?

Looks like it worked out alright, and these are definitely commands that work. I noticed two that LLMs used to get wrong all the time: `with_column, with_columns`, as they would spit out things without the underscore.

What if I try to use it?

It called the MCP tool and correctly formatted everything here.

Great! That was relatively painless. I do hope other MCP Servers have the same or similar experience.

Wrapping Up

So far, so good, for the usage of MCP Servers in Cursor, and even some other tools like VSCode and PyCharm.

I'd love to see these get easier to use, not having to add the JSON or clone the git repo. Just having them be available by default in tools like Cursor will make our LLMs that much more accurate and easier to work with.

Who else is using MCP Servers?

What about building one?

What MCP Servers are you using?

Would love to hear your war stories, or maybe lack thereof and things have been absolutely so easy!

Until next time we need to break something,

Sam

As an industry, we see better where AI fits and where it doesn't. We see the opportunities to leverage it. And not only that, but if you're not leveraging it, you are getting left behind by your coworkers and others at your level that know how to use it.

Learning how to leverage AI is not an overnight task, it's taken me time, and continues to take me time. In this post, my goal is provide you a few avenues for how to learn about AI. I'm not an expert, but I am someone that touches it daily and exposes myself to various ways of learning its capabilities.

I will cover a few paths to enlightenment:

• Reading

• Toying

• Practicing

• Talking

Reading

Remember that Goodreads reading list you have sitting and waiting for you? It might be time to reorder some of those books and adjust your priority.

Reading long-form material (books) is absolutely the best way, in my experience, to expose yourself to new topics. The field is moving fast, but books will introduce you to concepts that are the foundation of AI. They also have the space and breath to be able to explain to you the very concepts they introduce you to, should they chose to. And since books go through rounds of revisions, you're reading something that has been reviewed by people much smarter than either you or I.

Some books I can recommend at the moment:

• AI Engineering, Chip Huyen (somewhat technical)

• Why Machines Will Never Rule the World, Jobst Landgrebe and Barry Smith (more technical at times)

Toying

My dad always told me when I was kid the best way to learn something is to break it and put it back together. That's not necessarily true in this instance but the sentiment matches; don't need to take it so seriously. Play around with it.

Some ideas:

• Ask ChatGPT how it works

• Use Gemini 2.0 Deep Research or Perplexity. Watch how the models reason through what you've asked. This will give you a better idea of what they're doing.

• Replit. It'll build a whole app for you basically in a vibe coding session. Fun to watch.

• Watching YouTube videos of AI-based projects falls into the toying category for me. You can relax and listen without having to be hands on keyboard.

• Using Cursor or VSCode to help you create code while you're working on something

• Use any of the models to help teach you a new concept, have it quiz you after, and help iterate through multiple quizzes proving you know the material (but be careful of hallucinations and the model actually giving you the wrong answer)

  • I do this when I'm trying to learn new data architecture or coding pattern concepts. Don't give me the code. Let me understand what's underneath first. Then start giving me little pieces of code. AI is a great iterative teacher and tutor.

Practicing

This will be a bit more hands-on than toying. This means you're building something that's going to have AI in the DNA.

Experiment with things like Temperature, controlling how random or consistent the model results will be.

Some project ideas:

• Youtube Video Summarization: user passes through a URL, have a service that downloads the video, transcribes, and summarizes the key points. *

• Large text summarization: Same as above, just skipping the Youtube downloading piece. Use a model like gpt4, hit the OpenAI API, and pass the text to the prompt

• Custom Story/Poem Generator, Social Media Content Generator, Financial Analysis, etc, the list goes on

Tools: **

• HuggingFace: Try hosting your own LLM on your machine via HuggingFace vLLM, the inference may take a lot longer, and this will show you why GPUs are being used as opposed to CPU.

• Rent a GPU: When your simple summarization takes 30 mins, try to rent a GPU (cheap ones are available all over the world), and host the LLM on the machine with GPUs available.

• Amazon Bedrock: This could also be in the toying part. But if you have the ability, you can test out Bedrock and see how it enables you to use Foundational Models

• Streamlit: Once you've built out a project and you're hitting the APIs, but you're still running from the terminal, you can try out building something like Streamlit to give you a quick UI. This will give you the ability to see how you can take input from an UI, pass it to the API, and show results back to the user.

Obviously, you can get much more complex than what I've provided here. This is just a starting point for you to kick the tires a bit with GenAI tools and projects. You'll be able to grasp some of these concepts a lot better after doing this.

Note: I am not hitting on anything like propagation, gradients, traditional NLP, or any serious academic concepts that exist underneath the hood of these GenAI tools. I come from Data Science, but they're not concepts I'm qualified to speak to, frankly.

Talking (and listening)

We love talking AI. But sometimes we don't need to do the talking. I like to ask people what they think of AI who aren't even in the Data or SWE spaces. I like to ask people who are deeply familiar with NLP what areas of traditional ML/NLP are relevant to understand in order to better grasp GenAI.

There's a time to talk and a time to listen. We should have discernment enough to know when each time is upon us. I frankly love to listen to really smart people talk about these topics. And yet sometimes, somehow, I'm the most knowledgeable about the situation in the room. I'll still happily admit I'm no expert, but have the context to speak to where it is and where we are going.

Conclusion

There are many ways to feed a cat (thanks Nicholas Pylipiw for the fun adaptation of this phrase). You can learn however you best see fit. These are some ideas depending on your level of commitment. These are also, in no way, exhaustive lists. You are at liberty to use your creative imagination to find the best way that works for you! The possibilities are nearly endless.

GenAI I can say, is not disappearing anytime soon. It will take many forms over the coming years, and it will continue to have impacts on our lives we can't really foresee at the moment. You can prepare yourself to be better informed to what these tools are, and what they aren't, by spending some time with them. Let's get out of our fairy tale domes and learn their capabilities as well as their limitations. We will be a better informed public, less at risk of causing global hysteria, by adequately informing ourselves to these tools.

Sharing the publication or leaving a comment is very valuable for me! What did you think? How are you using AI in your life or at work?

Leave a comment

A share is just as valuable as you subscribing. Help other people find the publication if you think it would be valuable for them to see this!

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Disclaimers

1. You should only download YouTube videos with explicit consent of the creator and only do this for learning purposes. Even better, only do it to your own YouTube videos that you've uploaded. The point is practice hitting the API.

2. Watch your spend. Don't leave services running when you're not using them. Be careful of having too much spend on the OpenAI API.

Then, through the help of some amazing people, we've done it! We put on 4 events in Q1 and 2 events now here in Q2.

TL;DR

Full Stack Workshops will not continue happening in 2024.

Substack and YouTube will be the focus.

Join the Data Nexus Discord community so we can learn together (see below for more info)

Subscribe now

Thanks for the Feedback

I also am not too stubborn to think the thing I did would be the only thing I did. I listen to feedback and watched at how the events turned out.

Here's what I've learned:

• The content works! People are generally enjoying what we're learning // covering in the workshops. It's interesting and provokes conversation.

• Docker was a bit dense to do in a 90-minute in-person workshop. I didn't claim to teach all of Docker, just provide an intro, but it seemed to scare some people away ;D

• In-person + Learning seems to feel a bit too much like work/school to people.

• Add on that in-person requires a time commitment away from family, life, whatever it may be. Add the commute, 90-minute session, and return commute. That's like 2.5 hours people are dedicating to me once a month.

  • It's actually a lot, and even for me it's been a lot.

• The Low Key Meetups that Dan has been hosting are fire! (shoutout to Coalesce.io and all the other orgs that buy us drinks sometimes)

  • Related learnings:

    • people are happy to take the time out to go drink and talk data-related things

    • There's a big community of data-stuff going on in Raleigh
      

I have many more learnings actually but this is just a short list of some of the things I've compiled.

Therefore, we're going to switch some things up in Q3/Q4.

The New Normal

Exactly what the new normal is in terms of the content, I'm still ideating on this.

I could use your help to decide what are some interesting topics you'd want to learn more about that you're not seeing enough on available publicly.

My logistics are changing:

• Focus on Substack and writing (fits in with a larger goal)

  • Some topics in mind:

    • Working in a brownfield project, reverse engineering established systems/pipelines

    • Snowflake/Snowpark and writing Stored Procs in Snowflake

      • With some Easter Eggs you might not see elsewhere

      • Potentially reviewing Snowflake Managed, Snowflake External, and Snowflake External Iceberg tables, discussing each and the pros/cons/tradeoffs

    • Production-level Data Pipeline best practices

      • Small-medium size data, not HUGE data.

      • Best practices that you can relate with and leverage

• Continue on YouTube, but take a course on how to produce engaging content, maybe a bit more time editing, and the videos are a lot shorter

  • Currently, 30-50 min videos

  • Now, 10-15 minute videos

  • Shorten the time, provide the same value.

• Let community feedback drive what events we do, fully online events, and shorter in nature.

• Get back to investing in my own learning

  • I'll be pacing myself on this course starting July 9 if you want to join in:

    • https://github.com/DataTalksClub/llm-zoomcamp

    • Note it's already started if you want to start on it now and join the live cohort!

  • Time to read!

    • Apache Iceberg: The Definitive Guide

    • Bookclub anyone?
      

If this sounds interesting to you!

Subscribe now

And if you think you know people who could benefit, let's get them subscribed so they see what's to come:

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Important Lessons Learned

Balance. Without it there would complete chaos. It's important to invest in many different areas of our life to achieve that stability we're hoping for.

Empathy & Patience. I'm not perfect, and I'm learning. I'm thankful for the opportunity to have an amazing community around me.

Community. We want community, where we can feel connected and contribute.

Thank you

For sticking with me, and be invested in your own learnings as well. We'll continue to grow together, and ideally now in Q3/Q4 we have a larger reach and can help more people grow even further!

Sam

This year-long event, spanning across four quarters of 2024, is designed to foster learning, networking, and hands-on experience in the diverse and dynamic field of data. Each quarter is dedicated to a specific theme, offering depth and insight into different aspects of data science and engineering:

Q1: Data Engineering
Q2: Data Analytics
Q3: Data Science
Q4: MLOps

Why Should You Join?

• Learn from Experts: Dive into each subject with comprehensive workshops led by seasoned professionals.

• Hands-On Experience: Apply what you learn in real-time with practical, guided sessions.

• Networking Opportunities: Connect with like-minded individuals, industry leaders, and potential collaborators.

• Grow Your Skills: Whether you're just starting or looking to deepen your existing knowledge, there's something for everyone.

Sign-up here!

⏱ Workshop Format
5:45-6:30 PM: Food and Networking
6:30-7 PM: Engaging PowerPoint Presentations
7-8 PM: Interactive Hands-On Labs

🙏 How to Participate?
We're offering multiple modalities to cater to everyone's needs and preferences:

• In-Person Attendance: Join us live for the full experience, including networking opportunities and catered dinners.

• Live Online: Tune in from the comfort of your home or office.

• Self-Paced Online: Access workshop content at your convenience.

  
  

We Want to Hear from You

Are you interested in being a part of this exciting series? We're eager to welcome data enthusiasts from all backgrounds and levels of expertise. To help us tailor the experience to your needs, please let us know:

• Your Preferred Mode of Attendance: Would you prefer to join us in-person, live online, or through our self-paced online mode?

• Your Expectations: What are you hoping to gain from the Full Stack Data Workshop Series?
  
  

Get Involved!
And let us know your preferred mode of participation and any specific interests you have for these sessions.

If you’re interested in hosting one of the workshops, especially in the Data Analytics or Data Science quarters, I would especially love to hear from you and what excites you about the chance to share your knowledge with this group.

We're thrilled to offer this platform for learning and growth in the field of data science and engineering. Don't miss out on this opportunity to expand your knowledge, skills, and professional network.

We look forward to having you with us on this journey through data in 2024.

Again, the sign up link is here

Subscribe for free to receive updates on this Full Stack Camp, and other Data insights! Thank you for reading and staying up-to-date with what we’re working on

Money Talks

Introduction

The digital nomad lifestyle, ever enticing with its promise of freedom and endless adventure, isn't devoid of challenges. Chief among them is financial management. With the ascent of remote work, understanding the intricacies of managing finances while traversing the globe is essential. Let's embark on a journey to craft a comprehensive financial blueprint tailored for tech-savvy wanderers.

Crafting a Nomadic Financial Roadmap

Your journey as a digital nomad is as unique as your fingerprint. While each adventure is distinct, there are universal financial pillars that remain consistent. Here's a detailed guide to sculpting a robust financial framework:

1. Ascertain Your Earnings

Your financial odyssey begins with a clear assessment of your earnings. This encompasses everything from salaried remote jobs to sporadic freelancing gigs. An accurate reflection of your income is paramount, acting as the north star guiding your budgetary decisions.

2. Sculpt Your Spending Plan

A meticulous budget is more than just numbers; it's a reflection of your priorities and aspirations. Ensure it captures every facet of your spending, from the mundanities of daily living to those occasional, indulgent splurges that make the journey memorable. Immersing yourself in digital nomad communities can provide insights and hacks to stretch every dollar. It's essential to remember: while some destinations might pamper your wallet, others may demand a bit of splurging. Flexibility is key, and it's prudent to always have a cushion for unforeseen expenses.

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📸 From Bali's beaches to New York's skyscrapers—the world as your office is not a pipe dream, it's a choice!

The Digital Nomad's Dilemma: So Many Places, So Little Time

You've cracked the code on remote work, convinced your manager, and you're raring to hop onto that plane. But wait, where are you actually going? The very freedom that makes digital nomadism so attractive can also make deciding your next destination overwhelming. Worry not, I've got you covered with a comprehensive list of factors to consider. ✈️🌍

Money Matters: Cost of Living

First things first—can you actually afford to live in the place you're fantasizing about? The cost of living is paramount. Your digital nomad dream could quickly sour if you're sipping ramen instead of lattes because Tokyo emptied your bank account. Do your homework: compare the cost of living in your potential destinations against your income. This will help you gauge the lifestyle you can enjoy and for how long. 💵

Visa Viability: What's Allowed?

Visas: the unglamorous yet utterly essential aspect of international travel. You don't want to be deported for overstaying your welcome, trust me. Look for countries that have digital nomad visas or remote work-friendly policies. It can save you a lot of legal headaches and grant you the liberty to explore a place in-depth. 🛂

📸 Why stay cooped up when the world is your office?

The Situation: The Double-Edged Sword of Remote Work

You've nailed it! You're in a remote job that you love, a luxury many aspiring digital nomads are still hustling to attain. So, why does it feel like you're missing a piece of the puzzle? Oh, right. You're still yearning to sip coconut water on a Thai beach while coding, or mull over marketing strategies in a cozy Parisian café. Yet, something (or someone) is holding you back. Let me guess: your manager isn't 100% sold on the digital nomad lifestyle you're fantasizing about. 🤔

Step 1: Empathy is Key - Understand Their Concerns

If you've watched any successful pitch, you'll know it's not just about flaunting the features; it's about solving a problem. The same goes for your pitch to your manager. To be persuasive, you must understand their concerns about the digital nomad lifestyle. Is it about time-zone differences, team communication, or project deadlines? Knowing this will let you tailor your pitch.

With a blend of perseverance and the right strategy, it's a journey that can lead to incredibly fulfilling destinations. Here's a roadmap to guide you through the twists and turns of finding that perfect remote role:

1. Chart Your Course: Define Your Goals and Priorities

First things first, what's your destination? Pinpoint the kind of remote job you're after. What are the non-negotiables? What are the deal-breakers? Clarity in your goals and priorities acts like a compass, steering your search in the right direction.

2. Explore the Terrain: Utilize Job Search Websites

Dive into the digital world of job hunting. Platforms like FlexJobs, Remote.co, and We Work Remotely are like treasure maps, offering a wealth of remote opportunities. Use their filters to hunt down jobs that match your criteria – it's like having a GPS for your job search.

3. Build Bridges: Network

Your network is your alliance. Reach out to peers, past colleagues, and professionals in your field. You never know which conversation might lead to a golden opportunity. Immersing yourself in professional groups and networking events can open doors you didn't even know existed.

4. Craft Your Beacon: Apply Strategically

Each application is your signal flare. Tailor it to shine brightly for each job and company. Do your homework on the company, then reflect that insight in your application. Showcase your relevant skills and experiences, and articulate why you're not just a fit for the job, but a match for the company culture.

5. The Virtue of Patience

The road to finding a remote job can be long and winding. Patience is your trusted companion. If responses don't flood in immediately, or if the perfect role seems elusive, don't lose heart. Persistence is key; keep applying, keep networking, and the right opportunity will appear on the horizon.

Embark on this journey equipped with these strategies, and you're setting sail towards finding a remote job that aligns with your aspirations and lifestyle. Wishing you all the best in your quest!

Stay Connected! Don't miss out on the rest of our series where we unveil more insights and tips on remote work. Subscribe now to journey together towards unlocking your remote work potential! 🌟🚀

I've traveled for >18 months of my life, often times spending roughly 3-4 weeks in new places. Sometimes less, and sometimes more. Effectively living as a Nomad with a full-time job during these periods.

Thus, I decided to create a short series on how I would recommend approaching entering the Digital Nomad lifestyle. I am actually currently paused due to life, but am working to get back to DN'ing.

Therefore, this is also almost a personal guide for myself whenever I come into the various points I will cover here

If you're interested in traveling the world while working remotely, then this Series on Digital Nomading is perfect for you! This collection of articles provides valuable insights into the world of digital nomads, covering everything from finding remote work to managing finances while on the road. So don't miss out on the fun and adventure of becoming a digital nomad - stay tuned for my next few posts on how to make it happen!

Subscribe now!

Where does your organization stand on the MLOps journey? If you're like many, you might believe you're well on your way to achieving it. However, the reality could be quite different.

Many organizations have a long way to go before they can claim full proficiency in MLOps.
But why is this the case?
Why does it take us so long to reach just basic operability with MLOps, bringing automated value via machine learning and data science to our organizations?

Let's dive into some of the key barriers and explore possible solutions.
✏️ Knowledge: Not knowing where or how to start is a common roadblock in the journey towards MLOps. Leaders, the key drivers of this culture shift, often lack the required knowledge base. It's crucial to educate them about MLOps, its benefits, and implementation strategies.

🔑 Coding: MLOps cannot thrive on spaghetti code. We must transition from the mindset of statisticians or 'math geeks' to that of engineers. Our code needs to be modular, testable, and scalable. Adopting software engineering best practices in data science can go a long way in facilitating this transition.

🔋 Effort: Implementing MLOps is not a walk in the park—it requires considerable effort. The data must be well-documented, controlled, and understood. Implementing unit tests, integration tests, and other relevant tests is essential to increase trust in our systems. It may seem like a huge upfront investment, but the long-term returns are worth it.

🔨 Change Management: Implementing MLOps is not just about the technical aspects—it's also about managing change effectively. People need to buy into this process, understand its benefits, and actively drive it forward. Providing clear communication, regular training, and adequate support can help in managing this change.

⏰  "I need this now" Mentality: The pressure of delivering projects with quick deadlines often hampers the adoption of MLOps. Projects attempting to deliver in less than three months may not be conducive to implementing MLOps and automation. It's important to balance short-term delivery goals with long-term operational efficiency and quality.

What are your experiences solutioning and/or implementing MLOps in your role or previous roles? As others are going about this journey, do you have any advice that could help more novice Data Scientists or organizations that are still early in their Data Science journey?

Today, I discovered this amazing resource in Google Chrome to help me with my API Calls. (yes, I use Postman and like it a lot, but this was an unexpected help)

JSON Viewer is a way to prettify your API calls in the browser! So no need to configure everything in Postman to understand it. I can see straight away in the browser if the page I'm looking at is at all interesting to me.

You see in the picture below what the API call looks like in Safari on the left, and what it looks like in Chrome on the right with the JSON Viewer extension.https://github.com/tulios/json-viewer


Thoughts?
Now that I know that this is the page I want, I can move this over to Postman to have in more of a "semi-permanent" state to remind of the structure while I'm coding, but I thought this extension was awesome for being able to quickly look at what the API call returns.

Sam LaFell

As someone who comes from a non-computer science background, I fell into Data Science, and then fell again into Python, which led me to Data Science. Sometimes, I wish I had taken the formal route of computer science, but most of the time, I’m thrilled with the path that has led me to where I am today.

But what does that mean? I lack some formal computer science training that would help me break into the next level, so I pursue this knowledge myself. Today, I want to introduce you all to a topic that I’ve been learning about recently.

Why should we care?
As a data scientist, it’s super important for us to have a good grasp of the Big O time complexity of our algorithms. This can help us optimize our code and make it run faster. By minimizing the time complexity of our algorithms, we can make our code more efficient, reduce the amount of time it takes to process large amounts of data, and ultimately improve the quality of our analysis. Keep up the great work!

When analyzing the efficiency of algorithms, it is important to understand how the amount of input data affects the computation time. A common way to express this is through Big O notation, which characterizes the worst-case time complexity of an algorithm in terms of the size of its input. Here are some common Big O time complexities and their meanings:

O(1)

• The algorithm takes constant time to complete, regardless of the size of the input data. An example of this is accessing an element in an array by its index.

def get_first_element(arr):
   return arr[0]

O(n)

• The algorithm takes time proportional to the size of the input data to complete. Examples of this include iterating through an array or searching for an element in an unsorted list.

def search_array(arr, x):   
	for i in range(len(arr)):      
    	if arr[i] == x:         
        	return i   
        return -1

O(n**2)

• The algorithm takes time proportional to the square of the size of the input data to complete. Examples of this include nested loops or bubble sort.

def bubble_sort(arr):

	n = len(arr)
    for i in range(n):      
    	for j in range(0, n-i-1):         
        	if arr[j] > arr[j+1] :            
            	arr[j], arr[j+1] = arr[j+1], arr[j]   
            	return arr

O(log n)

• The algorithm takes time proportional to the logarithm of the size of the input data to complete. Examples of this include binary search or certain divide-and-conquer algorithms.

def binary_search(arr, x):   
	low = 0   
    high = len(arr) - 1   
    mid = 0   
    while low <= high:      
    	mid = (high + low) // 2      
        if arr[mid] < x:         
        	low = mid + 1      
        elif arr[mid] > x:         
        	high = mid - 1      
        else:         
        	return mid   
        return -1

Understanding the Big O time complexity of your algorithms can help you optimize your code and improve its performance. By minimizing the time complexity of your algorithms, you can make your code more efficient and reduce the amount of time it takes to process large amounts of data.

So, when you are designing or analyzing an algorithm, remember to consider its Big O time complexity and strive to optimize it whenever possible.

Actually, I plan to share with you each of the major DS and Algos topics I learn about over the next few weeks. This is preparing me in line with my career goals, and I hope you all stick around to see some of the things I learn.