While architecting a data-based solution, one needs to approach the problem differently depending on the specific strategy being adopted. In traditional machine learning, the focus is mostly on feature engineering. In DL, the emphasis is shifting to tagging larger volumes of data with less focus on feature development. Similarly, synthetic data is a lot more useful in DL than ML. So, the data strategies can be significantly different. Both approaches require very similar approaches to the analysis of errors. But, in most development processes, those approaches are not followed leading to substantial delay in production times. Hyper parameter tuning for performance improvement requires different strategies between ML and DL solutions due to the longer training times of DL systems. Transfer learning is a very important aspect to evaluate in building any state of the art system whether ML or DL. The last but not the least is understanding the biases that the system is learning. Deeply non-linear models require special attention in this aspect as they can learn highly undesirable features.

In our presentation, we will focus on all the above aspects with suitable examples and provide a framework for practitioners for building ML/DL applications.

Conversational Agents (Chatbots) are machine learning programs that are designed to have conversation with a human to help them fulfill a particular task. In recent years people have been using chatbots to communicate with business, help get daily tasks done and many more.

With the emergence of open source softwares and online platforms building a basic conversational agent has become easier but making them work across multiple domains and handle millions of requests is still a challenge.

In this talk I am going to talk about the different algorithms used to build good chatbots and the challenges faced to run them at scale in production.

Cleaning, Preparing , Transforming and Exploring Data is the most time-consuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The chal- lenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative fac- tors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year’s worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.

Recently, we have heard success stories on how deep learning technologies are revolutionizing many industries. Deep Learning has proven huge success in some of the problems in unstructured data domains like image recognition; speech recognitions and natural language processing. However, there are limited gain has been shown in traditional structured data domains like BFSI. This talk would cover American Express’ exciting journey to explore deep learning technique to generate next set of data innovations by deriving intelligence from the data within its global, integrated network. Learn how using credit card spend data has helped improve credit and fraud decisions elevate the payment experience of millions of Card Members across the globe.

Cognitive computing makes a new class of problems computable. To respond to the fluid nature of users understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. These systems differ from current computing applications in that they move beyond tabulating and calculating based on pre-configured rules and programs. They can infer and even reason based on broad objectives. In this sense, cognitive computing is a new type of computing with the goal of more accurate models of how the human brain or mind senses, reasons, and responds to stimulus. It is a field of study which studies how to create computers and computer software that are capable of intelligent behavior. This field is interdisciplinary, in which a number of sciences and professions converge, including computer science, electronics, mathematics, statistics, psychology, linguistics, philosophy, neuroscience and biology. Project Features are Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time; Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people; Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time; Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). {A set of cognitive systems is implemented and demonstrated as the project J+O=Y}

Data science projects, unlike their software counterparts tend to be uncertain and rarely fit into standardized approach. Each organization has it’s unique processes, tools, culture, data and in-efficiencies and a templatized approach, more common for software implementation projects rarely fits.

In a typical data science project, a data science team is attempting to build a decision support system that will either automate human decision making or assist a human in decision making. The dramatic rise in interest in data sciences means the typical data science project has a large proportion of relatively inexperienced members whose learnings draw heavily from academics, data science competitions and general IT/software projects.

These data scientists learn over time that the real world however is very different from the world of data science competitions. In the real-word problems are ill-defined, data may not exist to start with and it’s not just model accuracy, complexity and performance that matters but also the ease of infusing domain knowledge, interpretability/ability to provide explanations, the level of skill needed to build and maintain it, the stability and robustness of the learning, ease of integration with enterprise systems and ROI.

Human factors play a key role in the success of such projects. Managers making the transition from IT/software delivery to data science frequently do not allow for sufficient uncertainty in outcomes when planning projects. Senior leaders and sponsors, are under pressure to deliver outcomes but are unable to make a realistic assessment of payoffs and risks and set investment and expectations accordingly. This makes the journey and outcome sensitive to various behavioural biases of project stakeholders. Knowing what the typical behavioural biases and pitfalls makes it easier to identify those upfront and take corrective actions.

The speaker brings his nearly two decades of experience working at startups, in R&D and in consulting to lay forth these recurring behavioural biases and pitfalls.

Many of the biases covered are grounded in the speakers first-hand experience. The talk will provide examples of these biases and suggestions on how to identify and overcome or correct for them.

This demo would be regarding some of the work that I have already done since starting my journey in Machine Learning. So, there are a lot of MOOCs out there for ML and data science but the most important thing is to apply the concepts learned during the course to solve simple real-world use cases.

• One of the projects that I did included building state of the art Facial recognition system [VIDEO]. So for that, I referred to several research papers and the foundation was given to me in one of the courses itself, however, it took a lot of effort to connect the dots and that's the fun part.
• In another project, I made an Emoji Classifier for humans [VIDEO] based on your hand gestures. For that, I used deep learning CNN model to achieve great accuracy. I took reference from several online resources that made me realize that the data science community is very helpful and we must make efforts to contribute back.
• The other projects that I have done using machine learning:
  1. Handwritten digit recognition [VIDEO],
  2. Alphabet recognition [VIDEO],
  3. Apparel classification [VIDEO],
  4. Devnagiri recognition [VIDEO].

With each project, I have tried to apply one new feature or the other to make my model a bit more efficient. Hyperparameter tuning or just cleaning the data.

In this demonstration, I would just like to point out that knowledge never goes to waste. The small computer vision applications that I built in my college has helped me to gain deep learning computer vision task. It's always enlightening and empowering to learn new technologies.

I recently was part of a session on ‘Solving real world applications from Machine learning’ to Microsoft Advanced Analytics User Group of Belgium as well as broadcasted across the globe (Meetup Link) [Session Recording]

In these two training sessions ( 4 hours each, 8 hours total), you will learn to use data visualization and analytics software Tableau Public (free to use) and turn your data into interactive dashboards. You will get hands on training on how to create stories with dashboards and share these dashboards with your audience. However, the first session will begin with a quick refresher of basics about design and information literacy and discussions about best practices for creating charts as well as decision making framework. Whether your goal is to explain an insight or let your audience explore data insights, Tableau's simple drag-and-drop user interface makes the task easy and enjoyable. You will learn what's new in Tableau and the session will cover the latest and most advanced features of data preparation.

In the follow up second session, you will learn to create Table Calculations, Level of Detail Calculations, Animations and understanding Clustering. You will learn to integrate R and Tableau and how to use R within Tableau. You will also learn mapping, using filters / parameters and other visual functionalities.

When most known classification models fail to predict month on month telecom churn for a leading telecom operator, what can we do? Could there be an alternative?

Sentiment analysis has been for long poster boy problem of NLP and has attracted a lot of research. However, despite so much work in this sub area, most sentiment analysis models fail miserably in handling sarcasm. Rise in usage of sentiment models for analysis social data has only exposed this gap further. Owing to the subtilty of language involved, sarcasm detection is not easy and has facinated NLP community.

Most attempts at sarcasm detection still depend on hand crafted features which are dataset specific. In this talk we see some of the very recent attempts to leverage recent advances in NLP for building generic models for sarcasm detection.

Key take aways:
+ Challenges in sarcasm detection
+ Deep dive into a end to end solution using DL to build generic models for sarcasm detection
+ Short comings and road forward

The greatest challenge when building high performance model isn't about choosing the right algorithm or doing hyperparameter tuning: it is about getting high quality labeled data. Without good data, no algorithm, even the most sophisticated one, will deliver the results needed for real-life applications. And with most modern algorithms (such as Deep Learning models) requiring huge amounts of data to train, things aren't going to get better any time soon.

Active Learning is one of the possible solutions to this dilemma, but is, quite surprisingly, left out of most Data Science conferences and Computer Science curricula. This workshop is hoping to address the lack of awareness of the Machine Learning community for the important topic of Active Learning.

Link to data used in this course: https://s3-us-west-1.amazonaws.com/figure-eight-dataset/active_learning_odsc_india/Active_Learning_Workshop_data.zip

Apparently, Entertainment is a pure art form, but there's a huge bit that science can back the art. AI can drive multiple human intensive works in the Media Industry, driving the gut based decision to data-driven-decisions. Can we create a promo of a movie through AI? How about knowing which part of the video causing disengagement among our audiences? Could AI help content editors? How about assisting script writers through AI?

i will talk about few specific experiments done specially on Voot Original contents- on binging, hooking, content editing, audience disengagement etc.

In this session you will learn how to adopt DevOps values, principles and practices in AI world. DevOps culture increases the collaboration among Data engineering, Data science/AI engineering, & Operations team. DevOps enables faster delivery of high quality product through process improvement & technology adoptions like Cloud, Automation, feedback loop, Self-service, and shift left security.

Democratising Analytics Driven Decision Making at Enterprise Scale: This talk is about the journey Aditya Birla Group has embarked upon to embed Analytics Driven Decision making across the enterprise and will delve into a few use cases across the variety of businesses like Cement, Metals, Carbon Black along with Fashion & Retail where the analytical solutions have taken the existing decision making to more data (science) driven. The speaker will also cover the various challenges faced in the journey.

It took 4 days to run FFT (Fast Fourier transform) on a scanned grey scale image using 486 processor system way back in 1994, my engineering project , Digital Image Processing. Now, it takes few hours to train a deep learning model on a 32 core CPU system, say Music information retrieval (MIR), which has around 100k audio tracks of size 1000GB.

The evolution in the neural network has brought in the capability of rich feature extraction from thousands of images to identify a pattern in the data for further classification. This needs a high compute system. An image of say , 320 x 280 resolution needs 268,800 flops of computation (320x280x3 (RGB) )

We have the GPU enabled system now which is capable of supporting parallel processing with its huge numbers of processing threads.So why to wait for days/hours if it can be done in hours or minutes . CUDA (Computer Unified Device Architecture) is the framework which supports the device level data movement and memory management.

As a Deep Learning developer how we can leverage the GPU processor to accelerate the DL training process ? I am sharing my experience of training the DL models using the GPU processor based environment and with Keras & TF framework.

Do you have issues running your Machine Learning models in to production or want to learn what are some of the best industry practices in regards to deployment and serving predictions for Machine Learning models.
In a local environment the task is simple but things become complex when a model is run in production. Scaling, Robustness and reliability are few metrics that one has to take care of while doing so.

This talk will look into answering that question by taking the audience through an example using best made industry practices.

Tensorflow serving "is a flexible, high-performance serving system for machine learning models, designed for production environments" . It allows one to deploy newer versions of a model to production without changing either the architecture or the API.
Configure automatic deployment of a newer model as soon as an update model is available and run it alongside the older one for a no frills transition or scrap the older model completely and run the improved one in the production. With Tensorflow Serving one can easily deploy, manage their models in production to scale and even automate their deployment pipeline.

image credits: Tensorflow Serving Architecture

Kubernetes is used to deploy the created Serving, which will enable better scaling and robustness. How properties of a Kubernetes cluster like elasticity and resource managment can be used to serve models to a large number of users and run computationally and data intensive models in production.

Audience will be taken through each step of the process with in depth insights on the tools used while answering the questions of What, Why and How along the process.