The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

Causal questions are ubiquitous in data science. For e.g. questions such as, did changing a feature in a website lead to more traffic or if digital ad exposure led to incremental purchase are deeply rooted in causality.

Randomized tests are considered to be the gold standard when it comes to getting to causal effects. However, experiments in many cases are unfeasible or unethical. In such cases one has to rely on observational (non-experimental) data to derive causal insights. The crucial difference between randomized experiments and observational data is that in the former, test subjects (e.g. customers) are randomly assigned a treatment (e.g. digital advertisement exposure). This helps curb the possibility that user response (e.g. clicking on a link in the ad and purchasing the product) across the two groups of treated and non-treated subjects is different owing to pre-existing differences in user characteristic (e.g. demographics, geo-location etc.). In essence, we can then attribute divergences observed post-treatment in key outcomes (e.g. purchase rate), as the causal impact of the treatment.

This treatment assignment mechanism that makes causal attribution possible via randomization is absent though when using observational data. Thankfully, there are scientific (statistical and beyond) techniques available to ensure that we are able to circumvent this shortcoming and get to causal reads.

The aim of this talk, will be to offer a practical overview of the above aspects of causal inference -which in turn as a discipline lies at the fascinating confluence of statistics, philosophy, computer science, psychology, economics, and medicine, among others. Topics include:

• The fundamental tenets of causality and measuring causal effects.
• Challenges involved in measuring causal effects in real world situations.
• Distinguishing between randomized and observational approaches to measuring the same.
• Provide an introduction to measuring causal effects using observational data using matching and its extension of propensity score based matching with a focus on the a) the intuition and statistics behind it b) Tips from the trenches, basis the speakers experience in these techniques and c) Practical limitations of such approaches
• Walk through an example of how matching was applied to get to causal insights regarding effectiveness of a digital product for a major retailer.
• Finally conclude with why understanding having a nuanced understanding of causality is all the more important in the big data era we are into.

In machine learning, hyperparameters are parameters that governs the training process itself. For example, learning rate, number of hidden layers, number of nodes per layer are typical hyperparameters for neural networks. Hyperparameter Tuning is the process of searching the best hyper parameters to initialize the learning algorithm, thus improving training performance.

We present Katib, a scalable and general hyper parameter tuning framework based on Kubernetes which is ML framework agnostic (Tensorflow, Pytorch, MXNet, XGboost etc). You will learn about Katib in Kubeflow, an open source ML toolkit for Kubernetes, as we demonstrate the advantages of hyperparameter optimization by running a sample classification problem. In addition, as we dive into the implementation details, you will learn how to contribute as we expand this platform to include autoML tools.

Context: The Search problem

GOJEK is a SuperApp: 19+ apps within an umbrella app. One of these is GO-FOOD, the first food delivery service in Indonesia and the largest food delivery service in Southeast Asia. There are over 300 thousand restaurants on the platform with a total of over 16 million dishes between them.

Over two-thirds of those who order food online using GO-FOOD do so by utilising text search. Search engines are so essential to our everyday digital experience that we don’t think twice when using them anymore. Search engines involve two primary tasks: retrieval of documents and ranking them in order of relevance. While improving that ranking is an extremely important part of improving the search experience, actually understanding that query helps give the searcher exactly what they’re looking for. This talk will show you what we are doing to make it easy for users to find what they want.

GO-FOOD uses the ElasticSearch stack with restaurant and dish indexes to search for what the user types. However, this results in only exact text matches and at most, fuzzy matches. We wanted to create a holistic search experience that not only personalised search results, but also retrieved restaurants and dishes that were more relevant to what the user was looking for. This is being done by not only taking advantage of ElasticSearch features, but also developing a Query semantics engine.

Query Understanding: What & Why

This is where Query Understanding comes into the picture: it’s about using NLP to correctly identify the search intent behind the query and return more relevant search results, it’s about the interpretation process even before the results are even retrieved and ranked. The semantic neighbours of the query itself become the focus of the search process: after all, if I don’t understand what you’re trying to ask for, how will I give you what you want?

In the duration of this talk, you will learn about how we are taking advantage of word embeddings to build a Query Understanding Engine that is holistically designed to make the customer’s experience as smooth as possible. I will go over the techniques we used to build each component of the engine, the data and algorithmic challenges we faced and how we solved each problem we came across.

Recommendation systems aid in consumer decision making processes
like what to buy, which books to read or movies to watch.
Recommendation systems are specially useful in e-commerce websites
where a user has to navigate through several hundred items
in order to get to what they’re looking for . The data on how users
interact with the systems can be used to analyze user behaviour and
make recommendations that are in line with users’ preferences of
certain item attributes over others. Collaborative filtering has, until
recently, been able to achieve personalization through user based
and item based collaborative filtering techniques. Recent advances
in the application of Deep Learning in research as well as industry
has led people to apply these techniques in recommendation systems.
Many recommendation systems use product features for recommendations.
However textual features available on products are
almost invariably incomplete in real-world datasets due to various
process related issues. Additionally, product features even when
available cannot describe completely a certain feature. These limit
the success of such recommendation techniques. Deep learning
systems can process multi-modal data like text, images, audio and
thus is our choice in implementing multi-modal recommendation
system.
In this talk we show a real-world application of a fashion recommendation
system. This is based on a multi-modal deep learning system which is able to address the problem of poor annotation in the product data. We evaluate different deep learning architectures
to process multi-modal data and compare their effectiveness. We
highlight the trade-offs seen in a real-world implementation and
how these trade-offs affect the actual choice of the architecture.

Anecdotally only 2% of the models developed are productionized, i.e., used day to day to improve business outcomes. Part of the reason is the high cost and complexity of productionization of models. It is estimated to be anywhere from 40 to 80% of the overall work.

In this talk, we will share Scribble Data’s insights into productionization of ML, and how to reduce the cost and complexity in organizations. It is based on the last two years of work at Scribble developing and deploying production ML Feature Engineering Platform, and study of platforms from major organizations such as Uber. This talk expands on a previous talk given in January.

First, we discuss the complexity of production ML systems, and where time and effort goes. Second, we give an overview of feature engineering, which is an expensive ML task, and the associated challenges Third, we suggest an architecture for Production Feature Engineering platform. Last, we discuss how one could go about building one for your organization

Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.

Knowledge graph is another representation technique deeply researched in academia as well as used by businesses in production to augment search relevancy in information retrieval(Google knowledgegraph), improve recommender systems, semantic search applications and also Question answering problems.In this talk i will illustrate the benefits of semantic knowledge graph, how it differs from Semantic ontologies, different technologies involved in building knowledge graph, how i built one to analyse unstructured (twitter data) to discover hidden relationships from the twitter corpus. I will also show how Knowledge graph is data scientist's tool kit to discover hidden relationships and insights from unstructured data quickly.

In this talk i will show the technology and architecture used to determine entity reputation and entity co-occurence using Knowledge graph.Scoring an entity for reputation is useful in many Natural language processing tasks and applications such as Recommender systems.

Graphs have long held a special place in computer science’s history (and codebases). We're seeing the advent of a new wave of the information age; an age that is characterized by great emphasis on linked data. Hence, graph computing and databases have risen to prominence rapidly over the last few years. Be it enterprise knowledge graphs, fraud detection or graph-based social media analytics, there are a great number of potential applications.

To reap the benefits of graph databases and computing, one needs to understand the basics as well as current technical landscape and offerings. Equally important is to understand if a graph-based approach suits your problem.
These realizations are a result of my involvement in an effort to build an enterprise knowledge graph platform. I also believe that graph computing is more than a niche technology and has potential for organizations of varying scale.
Now, I want to share my learning with you.

This talk will touch upon the above points with the general premise being that data structured as graph(s) can lead to improved data workflows.
During our journey, you will learn fundamentals of graph technology and witness a live demo using Neo4j, a popular property graph database. We will walk through a day in the life of data workers (engineers, scientists, analysts), the challenges that they face and how graph-based approaches result in elegant solutions.
We'll end our journey with a peek into the current graph ecosystem and high-level concepts that need to be kept in mind while adopting an offering.

Language Wars have always been there for ages and it's got a new candidate with Data science booming - R vs Python. While the fans are fighting R vs Python, the creators (Hadley Wickham (Chief DS @ RStudio) and Wes McKinney (Creator of Pandas Project)) are working together as Ursa Labs team to create open source data science tools. A similar effort by RStudio has given birth to Reticulate (R Interface to Python) that helps programmers combine R and Python in the same code, session and project and create a new kind of super hero.

“We have built a machine learning model, What next?”

There is quite a bit of journey that one needs to cover from building a model in Jupyter notebook to taking it to production.
I would like to call it as the “last mile problem in ML” , this last mile could be a simple tread if we embrace some good ideas.

This talk covers some of these opinionated ideas on how we can get around some of the pitfalls in deployment of ML models in production.

We would go over the below questions in detail think about solutions for them.

• How to fix the zombie models apocalypse, a state when nobody knows how the model was trained ?
• In Science, experiments are found to be valid only if they are reproducible. Should this be the case in Datascience as well ?
• Training the model in your local machine and waiting for an eternity to complete is no fun. What are some better ways of doing this ?
• How do you package your machine learning code in a robust manner?
• Does an ML project have the luxury of not following good Software Engineering principles?

How many times did you feel that you were not able to understand someone else’s code or sometimes not even your own? It’s mostly because of bad/no documentation and not following the best practices. Here I will be demonstrating some of the best practices in Data Science, for R and Python, the two most important programming languages in the world for Data Science, which would help in building sustainable data products.

- Integrated Development Environment (RStudio, PyCharm)

- Coding best practices (Google’s R Style Guide and Hadley’s Style Guide, PEP 8)

- Linter (lintR, Pylint)

- Documentation – Code (Roxygen2, reStructuredText), README/Instruction Manual (RMarkdown, Jupyter Notebook)

- Unit testing (testthat, unittest)

- Packaging

- Version control (Git)

These best practices reduce technical debt in long term significantly, foster more collaboration and promote building of more sustainable data products in any organization.

GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. This talk summarizes the approaches considered and lessons learnt during the design and successful experimentation of a search system that uses ML to personalize the restaurant results based on the user’s food and taste preferences .

We formulated the estimation of the relevance as a Learning To Rank ML problem which makes the task of performing the ML inference for a very large number of customer-merchant pairs the next hurdle.
The talk will cover our learnings and findings for the following:
a. Creating a Learning Model for Food Search
b. Targetting experiments to a certain percentage of users
c. Training the model from real time data
d. Enriching Restaurant data with custom tags

Our story should help the audience in making design decisions on the data pipelines and software architecture needed when using ML for relevance ranking in high throughput search systems.

Natural Language Processing is a messy and complicated affair but modern advanced techniques are offering increasingly impressive results. Embeddings are a modern machine learning technique that has taken the natural language processing world by storm.

This hands-on tutorial will showcase the advantage of learning custom Word and Character Embeddings for natural language problems over pre-trained vectors like ELMo and BERT using a Named Entity Recognition case study over e-commerce data.