AI is moving fast—faster than ever. The real question? Who’s building what, and how is it changing the game?

Welcome to Data Dialogues,the series that goes straight to the source. Whether you're a data scientist, an aspiring ML enthusiast, or a business leader looking to harness AI, Data Dialogues offers unfiltered insights straight from the experts.

From breakthrough ideas to hard-earned lessons, we’re unpacking the moments that matter in AI. Big insights. Bold perspectives. Real stories.

Meet your hosts  

🔹 Poonam Singh heads marketing for Catalyst, translating complex tech into real business innovation. With 18+ years in B2B marketing, she’s all about cutting through the noise to make advanced technology accessible.  

🔹 Ramki R is a machine learning expert at Catalyst with nearly a decade in AI, data science, and automation. From coding to cracking AI challenges, he's played a pivotal role in making ML accessible for businesses. Expect insightful, no-nonsense takes (with a side of humor) on the future of machine learning.    

Episode 1: Mastering end-to-end machine learning pipelines    

Poonam Singh: In a world where AI and machine learning are evolving at lightning speed and projected to reach nearly $251 billion by 2027, why are so many businesses still struggling to implement them? Is it the fear of robots taking over or just the dread of debugging code at 3 a.m.? Welcome to Data Dialogues, where we break down machine learning into bite-sized, actionable insights—no PhD required.

Today, we’re tackling ML implementation and exploring how tools like QuickML are shaking things up faster than my morning coffee. Joining me is Ramki, the machine learning guru at Catalyst. Ramki, welcome!  

Ramki: Thanks for having me, Poonam! Excited to be here—though, I must admit, I was hoping for snacks.  

Poonam: Snacks are for episode three. But for now, let’s dive in. You’ve been in the ML game for nearly nine years. I’d love to hear your story.

Ramki: It’s been quite the journey! I joined Zoho in 2015 as a software developer, but I got hooked on the analytical side of Zoho CRM. Back then, AI wasn’t the buzzword it is today. Now, we’re looking at a projected market value of $251 billion by 2027.  

Poonam: That’s some serious growth! What’s the biggest shift you’ve noticed?

Ramki: Accessibility, hands down. When I started, implementing ML felt like trying to assemble IKEA furniture without the manual. You needed expertise in programming, statistics, and a whole lot of patience. Today, tools like QuickML have turned the process into a plug-and-play experience.  

Poonam: Let’s break it down for our listeners. What does traditional ML implementation look like?  

Ramki: Setting up traditional ML is a long, tedious, multi-step process, kind of like building a house. First, you've got to get things ready, which is data preprocessing. Think of it as cleaning the construction site and preparing the materials. We're talking data cleaning—handling missing values, outliers, the whole nine yards—then feature engineering—creating new, potentially more informative features from the existing data—and, finally, data transformation, like normalization or encoding, to get everything into the right format.

Poonam: Okay, so clean data is the foundation. What's next?

Ramki: Then comes model selection. That's like choosing the blueprint for your house. You pick an algorithm—maybe linear regression, a support vector machine, or even a neural network—depending on what you're trying to predict and the type of data you have.

Poonam: Got it. So, you've got your blueprint. Now you build, right?

Ramki: Exactly! That's the training phase. Here, we use optimization algorithms to adjust the model's parameters, kind of like fine-tuning the construction process. And we often have to tweak hyperparameters too—those are like the architect's design choices that influence how the model learns.

Poonam: So, you build, then you inspect?

Ramki: Precisely! We evaluate the model on a separate test set, like a final inspection of the house. We use metrics like accuracy, precision, or recall to see how well it performs. If it's not up to code, we might have to go back and tweak the preprocessing, choose a different model, or adjust the training process. It's often iterative.

Poonam: And once it passes inspection?

Ramki: Then, and only then, can we deploy it! That's when the model can finally make predictions on new, unseen data. It's a mix of complex coding, data wrangling, and a lot of trial and error, but it's incredibly rewarding when you get it right.

Poonam: That sounds like a lot of work! What are some of the biggest challenges or bottlenecks you typically encounter in this traditional ML pipeline?

Ramki: Data preparation and model selection, no question. It’s like trying to bake a cake without knowing if you have flour or sugar—or if your oven works. Plus, you need to juggle multiple programming languages and frameworks.

Data preparation involves cleaning, organizing, and transforming raw data into a usable format, especially when dealing with large datasets or inconsistent data sources. Data preparation is often the most time-consuming and frustrating part. It's not just about having data; it's about having good data. I often encounter issues like missing values, inconsistent formats, and noisy or biased data. Cleaning and transforming this data, and then engineering relevant features, can be a huge undertaking. It's like trying to build a skyscraper on a shaky foundation. If the data's no good, the model won't be either. Developers often spend 80% of their time on data preparation alone!

Model selection, on the other hand, requires identifying the most suitable algorithm or architecture to solve a specific problem. With so many algorithms available—from linear regression to complex neural networks—choosing the right one for the job can be tricky. It’s like trying to find the right ingredients and recipe all at once, where even a small mistake can lead to poor outcomes. Additionally, you need expertise in multiple programming languages, like Python, R, and Java, as well as familiarity with various frameworks, such as TensorFlow, PyTorch, or scikit-learn, to effectively implement and fine-tune the models.

Poonam: So how does QuickML save the day?  

Ramki: QuickML is like having a smart kitchen where the chef not only cooks your meal but also does your grocery shopping and files your taxes. It takes care of the heavy lifting: importing data from multiple sources without a single line of code, offering an intuitive drag-and-drop interface, and letting you check your data at every step, like taste-testing a dish as you go. What used to take days now takes hours or even minutes, freeing you up for more strategic work (or an extra coffee break). Plus, it seamlessly integrates with Zoho CRM, Zoho Analytics, and external databases. It’s basically a data feast, and you’re the VIP with unlimited access!

Poonam: That sounds amazing! Can you give us a quick example of how QuickML works? Like, does it make coffee, too, or is it just here to crunch numbers?

Ramki: Absolutely! Recently, I worked with a bank marketing dataset to predict if clients would subscribe to a term deposit. With QuickML, I uploaded the dataset, ran some automated exploratory data analysis, and built the model—all in minutes! The entire process, from data upload to model deployment, took less than a day. Traditional methods would have taken at least a week and probably my sanity, too. And the best part? QuickML supports various algorithms: classification, regression, clustering—you name it! It’s like having a Swiss Army Knife, but for data nerds like me.

Poonam: I’m sold. Let’s dive into the key stages of building an ML pipeline.

Ramki: Think of it like making the perfect cup of coffee—because, let’s be honest, bad coffee (or bad ML) ruins everything.

1. Collect and clean data: Start with good beans (data). No rocks, no twigs—just the good stuff.
2. Feature engineering: Grind it to the right consistency—too coarse, and it’s weak; too fine, and it’s a bitter mess.
3. Model training: Brew it. Experiment with water temperature, timing, and ratios to get the best flavor.
4. Fine-tuning: Taste-test and tweak—maybe less sugar, maybe a different milk—until it's just right.
5. Deployment: Serve it up, whether as a quick espresso (API), a full latte (app feature), or a coffee subscription (automation).
6. Monitoring and maintenance: Keep an eye on it; if people start making that face after the first sip, it’s time for adjustments.

Do it right, and you’ve got an ML model that’s smooth, strong, and keeps things running—just like a great cup of coffee!

Poonam: Those stages are the keys to success. What are some of QuickML's cool features that make these steps easier?

Ramki: Oh, there are plenty! First, the no-code interface lets you build models without coding—perfect for those who aren’t programmers. Then there’s automated EDA, which does the heavy lifting for exploratory data analysis, giving you insights without the usual headache. Model deployment helps you deploy models with REST API endpoints for real-time predictions. Scalability is another feature; QuickML handles large datasets like a pro, leveraging cloud resources for speedy processing. It’s all about making machine learning accessible and fun!

Poonam: You make it sound so easy! As we wrap up, what’s your vision for the future of QuickML?

Ramki: QuickML is a game-changer! Traditional machine learning can feel like trying to solve a Rubik’s Cube blindfolded: frustrating, overwhelming, and full of dead ends. Most businesses struggle with complex ML workflows, endless data wrangling, and the need for specialized expertise. QuickML changes that. It simplifies the entire process, making it possible for developers and analysts to build and deploy models without hitting roadblocks. As AI adoption skyrockets, I see a future where using ML is as natural as working with spreadsheets, where teams can leverage its power effortlessly and focus on impact rather than complexity.

Poonam: Thanks so much for chatting today, Ramki! It’s been a blast learning about machine learning and QuickML—who knew algorithms could be this fun? If you’d like to learn more about QuickML, sign up for a free trial.  

Stay tuned for episode two of Data Dialogues, where we’ll dive into the world of contextual AI—because in business, AI that truly understands intent can mean the difference between insights and missed opportunities!