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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

Gaurav Agarwal’s Blueprint for Success with RagaAI

Nitika Sharma
Nitika Sharma 18 Aug, 2024
4 min read

In this episode of Leading with Data, we chat with Gaurav Agarwal, the founder and CEO of RagaAI, about the exciting world of generative AI. As this technology continues to reshape industries, RagaAI is focused on making sure it does so reliably. Gaurav shares his journey, the challenges he’s faced, and how RagaAI is helping companies build AI systems that are not only innovative but also dependable. Dive into his insights on the future of AI, the importance of early-stage testing, and what it takes to stay ahead in this fast-moving field.

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!

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

Key Insights from Our Conversation with Gaurav Agarwal

  • Building a motivated team that aligns with the company’s vision is essential for success.
  • Ensuring AI systems are reliable is crucial, and RagaAI plays a pivotal role in achieving this.
  • Engaging in reliability testing from the concept phase is vital for effective AI application development.
  • The landscape of generative AI agents is rapidly evolving, bringing with it the challenge of testing these complex systems.
  • There’s a growing trend towards developing smaller, more efficient AI models designed for specific applications.
  • As technology advances and reliability concerns are addressed, the adoption of generative AI is expected to become widespread.
  • RagaAI aspires to set the industry standard for reliable generative AI applications in the future.

Let’s look into the details of our conversation with Gaurav Agarwal!

How did your journey into AI and generative AI begin?

Thank you for having me on the show. My journey into AI began over 15 years ago when I pursued my master’s in computer vision. At that time, AI wasn’t as widely recognized as it is today. My undergraduate project also involved computer vision, so my interest in the field has deep roots. The opportunity to work on cutting-edge technologies during my academic years was the first exposure to AI for me, and since then, the field has grown exponentially.

Can you share some key moments or learnings from your experiences with leading tech companies?

Certainly. Each experience has been unique and offered exciting learnings. For instance, at Ola Electric, the pace at which we built AI products was eye-opening. I learned the importance of having a motivated team that’s bought into your vision. At Nvidia, I witnessed the birth of the autonomous driving era and the significant growth in technology from 2015 to 2020. These experiences shaped my understanding of the importance of team dynamics and the power of thinking big.

What inspired you to start RagaAI?

A pivotal moment for me was a near-death experience due to an AI failure while test-driving a semi-autonomous vehicle. It was a rainy night, and the car failed to detect debris on the road. I had to intervene manually to avoid an accident. That incident underscored the critical need for AI systems to be reliable. At RagaAI, we’re dedicated to understanding why AI fails and how to prevent it, ensuring AI systems fulfill their intended roles safely.

How did you approach the early days of building RagaAI?

In the beginning, I focused on understanding customer requirements and building the right team. I reached out to potential customers to grasp their pain points and started assembling a team with complementary skills. These two aspects were crucial in setting the foundation for RagaAI.

What are some common AI errors that organizations encounter, and how does RagaAI address them?

RagaAI is the leading product for ensuring the reliability of generative AI. We’ve identified over 100 dimensions of potential errors, such as bias, inappropriate tone, and information leakage. Our system not only detects these errors but also diagnoses and prescribes solutions to fix them, which is essential for building and scaling reliable AI applications.

At what stage should companies engage with RagaAI for their generative AI applications?

We recommend that companies engage with us from the concept phase. Building a reliable generative AI application requires every step to be properly tested and evaluated, and we’ve successfully partnered with organizations to ensure this from the early stages.

How do you balance the trade-off between error reduction and cost in generative AI applications?

The trade-off involves considering technology costs, compute costs, and latency. The decision on how much overhead is acceptable varies by industry and application. For mission-critical applications, like in healthcare or finance, reliability is paramount. For less sensitive applications, some level of inaccuracy may be tolerable. We help customers benchmark and understand these trade-offs to make informed decisions.

How do you see the role of generative AI agents evolving, and what are the implications for testing?

Generative AI agents are complex and hold immense potential to change how we live. Testing these agents is exponentially more complex as each part and the system as a whole must be tested. We’re working on sophisticated methods to ensure the reliability of these agents and will be announcing something significant in this area soon.

How do you stay up-to-date with the rapidly evolving field of AI?

Staying up-to-date is a full-time job in itself. I spend a significant amount of time reading newsletters, listening to podcasts, and engaging with the community on platforms like LinkedIn and Twitter. Our team also shares knowledge actively, ensuring we’re all informed about the latest developments.

What advice would you give to those starting their careers in AI?

We’re living in a golden age of information. My advice is to learn as much as you can and build something tangible. Join open-source communities, contribute, and implement what you learn. That’s the best way to grasp new technologies.

What are your predictions for the generative AI industry in the next few years?

We’ll see widespread adoption of generative AI as reliability issues are addressed. There will be significant improvements in technology, and we’ll see a trend towards building smaller, more efficient models tailored to specific applications. This will lead to more personalized and distributed intelligence.

What does the future hold for RagaAI?

Our goal is to become the default standard for reliable generative AI. We’re building foundational technologies to ensure this, focusing on the rapid evolution of the field. We aim to be at the forefront, providing the tools necessary for companies to deploy generative AI confidently.

Summing-up

As generative AI continues to redefine possibilities, RagaAI remains committed to pioneering reliability standards. From early-stage engagement to predicting industry trends, Gaurav Agarwal’s journey exemplifies the dedication required to shape AI’s future. With a vision to foster innovation while ensuring safety and precision, RagaAI sets the stage for widespread adoption of AI technologies. The path forward promises smaller, more efficient models tailored to specific needs, underpinning a future where AI integrates seamlessly into everyday life, driven by reliability and transformative potential.

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

Check our upcoming sessions here.

Gaurav AgarwalGenerative AILeading With DataRagaAIResponsible AI
Nitika Sharma
Nitika Sharma 18 Aug, 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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