Since we originally proposed the need for a first-class language, compiler and ecosystem for machine learning (ML) - a view that is increasingly shared by many, there have been plenty of interesting developments in the field. Not only have the tradeoffs in existing systems, such as TensorFlow and PyTorch, not been resolved, but they are clearer than ever now that both frameworks contain distinct "static graph" and "eager execution" interfaces. Meanwhile, the idea of ML models fundamentally being differentiable algorithms – often called differentiable programming – has caught on.

Where current frameworks fall short, several exciting new projects have sprung up that dispense with graphs entirely, to bring differentiable programming to the mainstream. Myia, by the Theano team, differentiates and compiles a subset of Python to high-performance GPU code. Swift for TensorFlow extends Swift so that compatible functions can be compiled to TensorFlow graphs. And finally, the Flux ecosystem is extending Julia’s compiler with a number of ML-focused tools, including first-class gradients, just-in-time CUDA kernel compilation, automatic batching and support for new hardware such as TPUs.

This talk will demonstrate how Julia is increasingly becoming a natural language for machine learning, the kind of libraries and applications the Julia community is building, the contributions from India (there are many!), and our plans going forward.

It is too tedious to keep on asking questions, seek explanations or set thresholds for trends or anomalies. Why not find problems before they happen, find explanations for the glitches and suggest shortest paths to fixing them? Businesses are always changing along with their competitive environment and processes. No static model can handle that. Using dynamic models that find time-delayed interactions between multiple time series, we need to make proactive forecasts of anomalous trends of risks and opportunities in operations, sales, revenue and personnel, based on multiple factors influencing each other over time. We need to know how to set what is “normal” and determine when the business processes from six months ago do not apply any more, or only applies to 35% of the cases today, while explaining the causes of risk and sources of opportunity, their relative directions and magnitude, in the context of the decision-making and transactional applications, using state-of-the-art techniques.

Real world processes and businesses keeps changing, with one moving part changing another over time. Can we capture these changing relationships? Can we use multiple variables to find risks on key interesting ones? We will take a fun journey culminating in the most recent developments in the field. What methods work well and which break? What can we use in practice?

For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

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.

With the big boom in Data Science and Analytics Industry in India, a lot of data scientists are keen on learning a variety of learning algorithms and data manipulation techniques. At the same time, there is this growing interest among data scientists to give back to the society, harness their acquired skills and help fix some of the major burning problems in the nation. But how does one go about finding meaningful datasets connecting to societal problems and plan data-for-good projects? This session will summarize our experience of working in Data-for-Good sector in last 5 years, sharing few interesting datasets and associated use-cases of employing machine learning and artificial intelligence in social sector. Indian social sector is replete with good volume of open data on attributes like annotated images, geospatial information, time-series, Indic languages, Satellite Imagery, etc. We will dive into understanding journey of a Data-for-Good project, getting essential open datasets and understand insights from certain data projects in development sector. Lastly, we will explore how we can work with various communities and scale our algorithmic experiments in meaningful contributions.

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.

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.

This will be a hands-on workshop how to build a custom interactive dashboard application on your local machine or on any cloud service provider. You will also learn how to deploy this application with both security and scalability in mind.

Powerful Data visualization software solutions are extremely useful when building interactive data visualization dashboards. However, these types of solutions might not provide sufficient customization options. For those scenarios, you can use open source libraries like D3.js, Chart.js, or Bokeh to create custom dashboards. While these libraries offer a lot of flexibility for building dashboards with tailored features and visualizations.

Introduction:

Synthesizing audio for specific domains has many practical applications in creative sound design for music and film. But the application is not restricted to entertainment industry. We propose an architecture that will convert audio (human voice) to the voice owner’s preferred image – for the time being we restrict the intended images to two domains – Object Design and Human body. Many times, human beings are unable to describe a design (may be power-point presentation or interior decoration of a house) or a known person by verbally described attributes as they are able to visualise the same design or the person. But the other person, the listener may be unable to interpret the object or human descriptions from the speaker’s verbal descriptions as he/she is not visualising the same. Complete communication thus needs much of a trial and error and overall hazardous and time consuming. Examples of such situations are 1) While making presentation, an executive or manager can visualise something and an express to his/her employee to make the same. But, making the best slides from manger’s description may not be proper. Another relevant example is that a house owner or office owner wants his/her premises to have certain design which he/she can visualise and express to the concerned vendor. But the vendor may not be able to produce the same. Also, trial and error in this case is highly expensive. Having an automated Image, recommended to him/her can address this problem. 2) Verbal description of a terrorist or criminal suspect (facial description and/or attribute) may not be always available to all the security people every time, in Airports or Railway Stations or sensitive areas. Presence of a software system having Machine Generated Image with Ranked Recommendation for such suspect can immediately point to one or very few people in a crowded Airport or even Railway Station or any such sensitive place. Security agencies can then frisk only those people or match their attributes with existing database. This can avoid hazardous manual checking of every people in the same process and can help the security agencies to do adequate checking for those recommended individuals.

We can use a Sequential Architecture consisting of simple NLP and more complex Deep Learning algorithms primarily based on Generative Adversarial Network (GAN) and Neural Personalised Ranking (NPR) to help the object designers and security personnel for serving their specific purposes.

The idea to combat the problem:

I propose a combination of Deep Learning and Recommender System approach to tackle this problem. Architecture of the Solution model consists of 4 major Components – 1) Speech to Text

2) Text Classification into Person or Design; 3) Text to Image Formation; 4) Recommender System

We are trying to address these four steps in consecutive applications of effective Machine Learning and Deep Learning Algorithms. Deep Learning community has already been able to make significant progress in terms of Text to Image generation and also in Ranking based Recommender System

Brief Details about the four major pillars of this problem:

Deep Learning based Speech Recognition – Primary technique for Speech to text could be Baidu’s DeepSpeech for which a Tensorflow implementation is readily available. Also, Google Cloud Speech-to-Text enables the develop to convert Voice to text. Voice of the user needs to be converted in .wav file. Our steps for Deep-Speech-2 are like this – Fixing GPU memory, Adding Batch normalization to RNN, implement row Convolution layer and generate text.

Nowadays, we have quite a few free Speech to Text software, e.g. Google Docs Voice typing, windows Speech Recognition, Speech-notes etc.

Text Classification of Content – This is needed to classify the converted text into two classes – a) Design Description or b) Human Attribute Description because these two applications and therefore image types are different. This may be Statistically easier part, but its importance is immense. A Dictionary of words related to Designs and Personal Attributes can be built using online available resources. Then, a supervised algorithm using tf-idf and Latent Semantic Analysis (LSA) should be able to classify the text into two classes – Object and Person. These are very much traditional and proven techniques in many NLP research

Text to Image Formation – This is our main component for this proposal. Today, one of the most challenging problems in the world of Computer Vision is synthesizing high-quality images from text descriptions. In recent years, GANs have been found to generate good results. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. There have been a few approaches to address this problem, all using GAN. One of those is given as Stacked Generative Adversarial Networks (StackGAN). Heart of such approaches is Conditional GAN which is an extension of GAN where both generator and discriminator receive additional conditioning variables c, yielding G(z, c) and D(x, c). This formulation allows G to generate images conditioned on variables c.

In our case, we train deep convolutional generative adversarial network (DC-GAN) conditioned on text features. These text features are encoded by a hybrid character-level convolutional-recurrent neural network. Overall, DC-GAN uses text embeddings where the context of a word is of prime importance. Class label determined in the earlier step will be of help in this case. This will simply help DC-GAN to generate more relevant images than irrelevant ones. Details will be discussed during the talk

The most straightforward way to train a conditional GAN is to view (text, image) pairs as joint observations and train the discriminator to judge pairs as real or fake. The discriminator has no explicit notion of whether real training images match the text embedding context. To account for this, in GAN-CLS, in addition to the real/fake inputs to the discriminator during training, a third type of input consisting of real images with mismatched text is added, which the discriminator must learn to score as fake. By learning to optimize image/text matching in addition to the image realism, the discriminator can provide an additional signal to the generator. (details are in talk)

Image Recommender System – In the last step, we propose personalised image recommendation for the user from the set of images generated by GAN-CLS architecture. Image Recommendation brings down the number of choice of images to a top N (N=3, 5, 10 ideally) with a rank given to each of those and therefore user finds it easier to choose. In this case, we propose Neural Personalized Ranking (NPR) – a personalized pairwise ranking model over implicit feedback datasets – that is inspired by Bayesian Personalized Ranking (BPR) and recent advances in neural networks. We like to mention that, now NPR is improved to contextual enhanced NPR. This enhanced Model depends on implicit feedbacks from the users, its contexts and incorporates the idea of generalized matrix factorization. Contextual NPR significantly outperforms its competitors

In the presentation, we shall describe the complete sequence in detail

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.

Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

In this talk, I will introduce exponential family embeddings. Developed by Rudolph and Blei, these methods extend the idea of word embeddings to other types of high-dimensional data. I will demonstrate how they can be used to conduct advanced topic modeling on datasets that are medium-sized, which are specialized enough to require significant modifications of a word2vec model and contain more general data types (including categorical, count, continuous). I will discuss how my team implemented a dynamic embedding model using Tensor Flow and our proprietary corpus of job descriptions. Using both categorical and natural language data associated with jobs, we charted the development of different skill sets over the last 3 years. I will specifically focus the description of results on how tech and data science skill sets have developed, grown and pollinated other types of jobs over time.

This case study, followed by a demo, will parade on how handwritten date and amount fields were extracted and validated. By adopting this automated way of recognising handwritten digits, banks can cut down the manual time and increase speed in their process. Although this is still in the proof of concept phase, this feat was achieved using computer vision and image processing techniques.

This case study will briefly cover:

• Detection of bounding and taking the region of interest
• Fragment and Identify technique
• Checking the accuracy of bounding box using Intersection over Union technique

This case study/approach can be extended to other operative environments, where handwritten digits recognition is needed.

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.