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!

Despite the increasing number of data scientists who are asked to take on leadership roles as they grow in their careers, there are still few resources on how to lead data science teams successfully.

In this talk, I will argue that an effective data science leader has to wear three hats: Diplomat (understand the organization and their team and liaise between them), Diagnostician (figure out how what organizational needs can be met by their team and how), and Developer (grow their and their team's skills as well as the organization's understanding of data science to maximize the value their team can drive).

Throughout, I draw on my experience as a data science leader both at a political party (the Democratic Party of the United States of America) and at a fintech startup (Even.com).

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.

Topic Modeling the art of extracting latent topics/themes that exist in a set of documents. In this talk we will discuss the use cases of Topic Modeling, particularly pertaining to Latent Dirichlet Allocation (LDA), and the implementation work by the Data Science Applications team at Meredith for the purposes of designing auto-taggers, classifiers for the topics in the custom enterprise taxonomy against hundreds of thousands of documents. We will talk about the best practices of choosing the optimal number of topics for hundreds of thousands of documents, how named entity extraction is employed to derive context in the feature space, alignment of machine learning techniques to support the work of taxonomists, the integration with the enterprise architecture to support expert assessor population for curating training data for Google’s AutoML and other deep learning capabilities.
Latent semantic analysis has been shown to be ideal for quickly clustering the document space. Applied in a hierarchical manner on top-level clusters to derive child clusters and informed with inputs from the subject matter experts and taxonomists, namely taxonomy terms and synonyms, makes it possible to get a sense of the coverage in the content space against the enterprise taxonomy model.
Where there are shortcomings, additional training data needs to be obtained in order to effectively build auto-tagging solutions. One technique for data augmentation is query formulation, again utilizing entity extraction from owned content along with the taxonomy categories and synonyms, to construct social listening streams to surface new off-property content to become part of the training corpus.

{ This is a handson workshop . The use case is Traffic analysis . }

Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

In this digital era when the attention span of customers is reducing drastically, for a marketer it is imperative to understand the following 4 aspects more popularly known as "The 4R's of Marketing" if they want to increase our ROI:

- Right Person

- Right Time

- Right Content

- Right Channel

Only when we design and send our campaigns in such a way, that it reaches the right customers at the right time through the right channel telling them about stuffs they like or are interested in ... can we expect higher conversions with lower investment. This is a problem that most of the organizations need to solve for to stay relevant in this age of high market competition.

Among all these we will put special focus on appropriate content generation based on targeted user base using Markov based models and do a quick hack session.

The time breakup can be:

5 mins : Difference between Martech and traditional marketing. The 4R's of marketing and why solving for them is crucial

5 mins : What is Smart Segments and how to solve for it, with a short demo

5 mins : How marketers use output from Smart Segments to execute targeted campaigns

5 mins: What is STO, how it can be solved and what is the performance uplift seen by clients when they use it

5 mins: What is Channel Optimization, how it can be solved and what is the performance uplift seen by clients when they use it

5 mins: Why sending the right message to customers is crucial, and introduction to appropriate content creation

15 mins: Covering different Text generation nuances, and a live demo with walk through of a toy code implementation

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.

Containers are all the rage in the DevOps arena.

This session is a live demonstration of how the data team at Milliman uses containers at each step in their data science workflow -

1) How do containerized environments speed up data scientists at the data exploration stage

2) How do containers enable rapid prototyping and validation at the modeling stage

3) How do we put containerized models on production

4) How do containers make it easy for data scientists to do DevOps

5) How do containers make it easy for data scientists to host a data science dashboard with continuous integration and continuous delivery

Open Data offers a tremendous opportunity in transformation of today’s manufacturing sector to smarter manufacturing. Smart Manufacturing initiatives include digitalising production processes and integrating IoT technologies for connecting machines to collect data for analysis and visualisation.

In this talk, an understanding of linkage between various industries within manufacturing sector through lens of Open Data Science will be illustrated. The data on manufacturing sector companies, company profiles, officers and financials will be scraped from UK Open Data API’s. The work I plan to showcase in ODSC is part of UK Made Smarter Project, where the work has been useful for major aerospace alliances to find out the champions and strugglers (SMEs) within manufacturing sector based on the open data gathered from multiple sources. The talk includes discussion on data extraction, data cleaning, data transformation - transforming raw financial information about companies to key metrics of interest - and further data analytics to create clusters of manufacturing companies into "Champions" and "Strugglers". The talk showcased examples of powerful R Shiny based dashboards of interest for suppliers, manufacturer and other key stakeholders in supply chain network.

Further analysis includes network analysis for industries, clustering and deploying the model as an API using Google Cloud Platform. The presenter will discuss about the necessity of 'Analytical Thinking' approach as an aid to handle complex big data projects and how to overcome challenges while working with real-life data science projects.

Data Science is all about deriving actionable insights through data analysis.
There is no denying the fact that such insights have a tremendous business value.
But what if -
Some crucial data has been left out of consideration ?
Wrong inferences have been drawn during analysis ?
Results have been graphically misrepresented?
Imagine the adverse impact on your business if you take wrong decisions based on such cases.

In this talk we will discuss the following 5 key pitfalls to lookout for in the data analysis results before you take any decisions based on them
1. Selection Bias
2. Survivor Bias
3. Confounding Effects
4. Spurious Correlations
5. Misleading Visualizations

These are some of the most common points that are overlooked by the beginners in Data Science.

The talk will draw upon many examples from real life situations to illustrate these points.

“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?

In this era of big data , Recommender systems are becoming increasingly important for businesses because they can help companies offer personalized product recommendations to customers. There have been many acknowledged recognized successes of consumer-oriented recommender systems, particularly in e-commerce. However, when it comes to Business to-Business (B2B) market space, there has been less research and real-time application of such systems.

In our case study, we present a hybrid approach of building a context-sensitive recommender system incorporating semantic knowledge in the form of domain ontology and a custom user- user collaborative filtering model in a B2B space. Using Engineering Products transaction data of an Instrumentation company, we demonstrate that this recommendation algorithm offers improved personalization, diversity and cold start performance compared to standard Collaborative Filtering based recommender system.

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.

Abstract:

How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in cellular network management. In this talk I shall introduce CNR(Cellular Network Regression) a unified performance anomaly detection framework for KPI time-series data. CNR realizes simple statistical modelling and machine-learning-based regression for anomaly detection; in particular, it specifically takes into account seasonality and trend components as well as supports automated prediction model retraining based on prior detection results. I demonstrate here how CNR detects two types of anomalies of practical interest, namely sudden drops and correlation changes, based on a large-scale real-world KPI dataset collected from a metropolitan LTE network. I explore various prediction algorithms and feature selection strategies, and provide insights into how regression analysis can make automated and accurate KPI anomaly detection viable.

Index Terms—anomaly detection, NPAR (Network Performance Analysis)

1. INTRODUCTION

The continuing advances of cellular network technologies make high-speed mobile Internet access a norm. However, cellular networks are large and complex by nature, and hence production cellular networks often suffer from performance degradations or failures due to various reasons, such as back- ground interference, power outages, malfunctions of network elements, and cable disconnections. It is thus critical for network administrators to detect and respond to performance anomalies of cellular networks in real time, so as to maintain network dependability and improve subscriber service quality. To pinpoint performance issues in cellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks . ellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks .