The last mile problem in ML

“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?
 
 

Outline/Structure of the Talk

  • Discussion on some of the issues with deploying ML models to production.
  • Discussion about mlflow including a quick demo.
  • Discussion about sagemaker BYO algorithms training.
  • Discussion about packagining ML code in a robust manner.

Learning Outcome

  • Understand some of the pitfalls in ML deployment.
  • Get familiarized with mlflow, sagemaker etc

Target Audience

ML enthusiasts

Prerequisites for Attendees

  • Highlevel understanding of Machine Learning
  • Interest to know about some ways to build robust ML applications.
schedule Submitted 7 months ago

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    Case Study
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    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:

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    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.

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    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

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    Akash Tandon
    Data Engineer
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    schedule 7 months ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

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    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.

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    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.

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    Maryam Jahanshahi
    Maryam Jahanshahi
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    schedule 10 months ago
    Sold Out!
    45 Mins
    Case Study
    Intermediate

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    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.

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    Sunil Jacob - Automated Recognition of Handwritten Digits in Indian Bank Cheques

    Sunil Jacob
    Sunil Jacob
    Sr. Architect
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    schedule 8 months ago
    Sold Out!
    45 Mins
    Case Study
    Beginner

    Handwritten digit recognition and pattern analysis are one of the active research topics in digital image processing. Moreover, automatic handwritten digit recognition is of great technical interest and academic interest.

    In today’s digital realm, banks cheques are widely used around the world for various financial transactions. A rough estimate says that almost 120+ billion cheques move around the world. In the Indian banking scenario, CTS cheque clearance system has come. Even though the check is cleared quickly, there is still manual intervention needed to validate the date and amount fields. There is a lot of manual effort in this area.

    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:

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    Case Study
    Beginner

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    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

  • Liked Dr. Neha Sehgal
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    Dr. Neha Sehgal - Open Data Science for Smart Manufacturing

    45 Mins
    Talk
    Intermediate

    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.

  • Liked Pallavi Mudumby
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    Pallavi Mudumby - B2B Recommender System using Semantic knowledge - Ontology

    45 Mins
    Case Study
    Intermediate

    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.