How can managers save time on Datascience Projects?

Demand Forecast made easy

Forcasting has huge number of use cases across industries. Be it Inventory forcasting, product sales or man power, forcasting helps us in eliminating unwanted expenses. Todays episode we talk about forcasting on aws. How to upload the timeseries data and forcast for each product also about additional benefits like what if analysis

How can managers save time on Datascience Projects?

Managing your datascience projects is different from managing your typical IT projects. Here we provide tips on how managers can use AWS Sagemaker Feature store to save time and streamline the entire process of feature engineering across their datascience projects.

Talking to CEO of an IIOT based AI Startup

Todays episode, I will be talking to the founder and CEO of an AI Startup called MeghaAI (www.meghaai.com). Meghani has build a product where he claims to have automated the entire datascience pipeline for collecting industrial IOT data to building anomaly detection on it. This he claims helps many industries perform automated machine learning without the need of hiring datascientists. Lets listen to him talk about how he started his journey and how AWS has helped him build the product.

Understanding Bias and Variance

Todays episode we introduce you to machine learning models that have prediction errors, and these prediction errors are usually known as Bias and Variance. In machine learning, there will always be a deviation between the model predictions and actual predictions. The main aim of ML/data scientists is to reduce these errors in order to get more accurate results. In this episode we are going to discuss bias and variance, Bias-variance trade-off, Underfitting and Overfitting. Also, we would take a quick look on how AWS Sagemaker clarify helps us to understand data and model bias

Monitor ML models in Production

Machine learning models are very different from code. When you deploy code you don't really need to monitor it on how it is delivering the results. However, ML models are different, we need to monitor their input data and measure them to a baseline. This is what we talk about in todays episode and talk on Services like AWS Sagemaker, Model Monitor, Model Drift and data collection. The process of Model Monitor is part of the MLOps lifecycle