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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Rohan Rao’s Framework for Selecting the Right LLMs for Business Needs!

Nitika Sharma
Nitika Sharma 20 Sep, 2024
4 min read

In this episode of Leading with Data, we dive into the fascinating world of data science with Rohan Rao, a Quadruple Kaggle Grandmaster and expert in machine learning solutions. Rohan shares insights on strategic partnerships, the evolution of data tools, and the future of large language models, shedding light on the challenges and innovations shaping the industry.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights from our Conversation with Rohan Rao

  • Strategic partnerships in competitions can lead to memorable victories and learning experiences.
  • The evolution of data science tools requires continuous learning and adaptation from practitioners.
  • The future of LLMs may depend on new data sources and synthetic data generation.
  • Businesses are keen on integrating LLMs but face challenges in applying them to unique datasets.
  • A comprehensive framework for selecting LLMs can guide businesses in making informed decisions.
  • Experimentation is key in choosing between traditional algorithms and generative AI for business problems.
  • Proprietary LLMs with APIs often offer a more convenient solution for businesses despite higher costs.
  • Responsible AI involves a multifaceted approach, including technology, policy, and regulation.
  • Specialized AI agents hold promise for targeted, efficient problem-solving within businesses.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Let’s look into the details of our conversation with Rohan Rao!

How Did You Begin Your Journey in Data Science and Which Competition Stands Out for You?

Thank you, Kunal, for having me on Leading With Data. My journey in data science began nearly a decade ago, filled with coding, hackathons, and competitions. It’s challenging to pick a standout competition, but one memorable moment was achieving a hat trick of wins on Analytics Vidhya’s hackathons by cleverly teaming up with a strong competitor. It was a strategic move that paid off and is a fond memory from my competitive days.

The field of data science has seen phases of gradual progress and sudden leaps. Tools like XGBoost revolutionized predictive modeling, while BERT transformed NLP. Recently, the release of ChatGPT marked a significant milestone, showcasing the capabilities of LLMs. These advancements have required data scientists to continuously adapt and upgrade their skills.

What Are Your Predictions for the Future of Generative AI?

The trajectory of LLMs tends to show a steep initial improvement followed by a plateau. Improving performance incrementally becomes more challenging over time. While LLMs have learned from vast amounts of internet data, the future improvements may hinge on new, large datasets or innovations in synthetic data generation. The computational resources available today are unprecedented, making innovation more accessible than ever.

How Are Businesses Adopting Generative AI and LLMs?

Businesses across various industries are eager to integrate LLMs into their operations. The challenge lies in marrying these models to proprietary business data, which is often not as extensive as the data LLMs are trained on. At H2O.ai, we’re seeing a significant portion of our work focused on enabling businesses to leverage the power of LLMs with their unique datasets.

What Are the Most Common Use Cases You’ve Seen in Different Sectors?

The most common question from businesses is how to make an LLM learn from their specific data. The goal is to apply the general capabilities of LLMs to address unique business challenges. This involves understanding the models’ strengths and limitations and integrating them with existing systems and data formats.

Can You Share Your Framework for Selecting the Right LLM for Business Needs?

Certainly. The framework I presented at the Data Hack Summit includes 12 points to consider when selecting an LLM for your business. These range from the model’s capabilities and accuracy to scalability, cost, and legal considerations like compliance and privacy. It’s crucial to evaluate these factors to determine which LLM aligns best with your business objectives and constraints.

How Do You Navigate the Choice Between Traditional Algorithms and Generative AI?

The key is to experiment and iterate. While traditional algorithms like XGBoost have been the go-to for many problems, LLMs offer new possibilities. By comparing their performance on specific tasks, businesses can determine which approach yields better results and is more feasible to deploy and manage.

What Are the Considerations When Building Engineering Solutions Around LLMs?

Choosing between proprietary LLMs with APIs and hosting open-source LLMs on-premises is a significant decision. While open-source models may seem cost-effective, they come with hidden complexities like infrastructure management and scalability. Often, businesses gravitate towards API services for their convenience, despite higher costs.

How Do You Address the Challenges of Responsible AI?

Responsible AI is a complex issue that extends beyond technological solutions. While guardrails and frameworks are in place to prevent misuse, the unpredictable nature of LLMs makes it difficult to fully control. The solution may involve a combination of technological safeguards, government policies, and AI regulations to balance innovation with ethical use.

What’s Your Take on the Use of AI Agents in Business?

I’m extremely bullish on the potential of AI agents. Specialized agents can perform specific tasks with high accuracy, and the challenge lies in integrating these microtasks into broader solutions. While some products may simply wrap existing LLMs with custom prompts, truly specialized agents have the potential to revolutionize how we approach problem-solving in various domains.

End Note

As Rohan emphasizes, navigating the landscape of data science and generative AI requires continuous learning and experimentation. By embracing innovative frameworks and responsible AI practices, businesses can harness the power of data to drive meaningful solutions, ultimately transforming the way they operate and compete in a rapidly evolving market.

For more engaging sessions on AI, data science, and GenAI, stay tuned with us on Leading with Data.

Check our upcoming sessions here.

AIbusinesschallengesdata sciencedatasetsframeworkLeading With DataLLMModelsRohan raotools
Nitika Sharma
Nitika Sharma 20 Sep, 2024

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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