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Big Data Analytics

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  • Tensorflow: Architecture and use case
    Tensorflow: Architecture and use case Gema Parreño Piqueras. AI product developer Recorded: Apr 21 2017 49 mins
    The webinar drives into the introduction of the architecture of Tensorflow and the designing of use case.

    You will learn:
    -What is an artificial neuron?
    -What is Tensorflow? What are its advantages? What's it used for?
    -Designing graphs in Tensorflow
    -Tips & tricks for designing neural nets
    -Use case
  • The Secrets to WINNING with Machine Learning
    The Secrets to WINNING with Machine Learning Patrick Rice, CEO, Lumidatum Recorded: Apr 13 2017 39 mins
    It’s easy to get caught in the excitement of machine learning and start optimizing RMSE, AUC or recall, but machine learning success starts with aligning to the business.

    Join Patrick Rice, CEO of Lumidatum, as he leverages his decade of experience in big data analytics including implementing machine learning solutions at Amazon to outline the secrets to winning at machine learning and delivering real ROI to the business.
  • Machine Learning To Increase Human Understanding
    Machine Learning To Increase Human Understanding Assaf Baciu, Co-founder, SVP of Product & Engineering at Persado Recorded: Apr 13 2017 46 mins
    There is a misconception that infusing automation into the customer experience will make it less human, and thus less relatable, to consumers. On the contrary, companies are pioneering AI technologies to enable them to gain a deeper understanding of the people they serve, resulting in experiences that are more relevant, personal, emotional, and ultimately rewarding--for customer and company alike.

    Join Assaf Baciu, head of product and co-founder of Persado, who will demonstrate how Fortune 1000 brands are leveraging machine learning to create emotional relationships with their audiences.
  • The Ways Machine Learning and AI Can Fail
    The Ways Machine Learning and AI Can Fail Brian Lange, Partner and Data Scientist, Datascope Recorded: Apr 13 2017 48 mins
    Good applications of machine learning and AI can be difficult to pull off. Join Brian Lange, Partner and Data Scientist at data science firm Datascope, as he discusses a variety of ways machine learning and AI can fail (from technical to human factors) so that you can avoid repeating them yourself.
  • Different Strategies of Scaling H2O Machine Learning on Apache Spark
    Different Strategies of Scaling H2O Machine Learning on Apache Spark Jakub Hava, Software Engineer at H2O.ai Recorded: Apr 13 2017 46 mins
    Sparkling Water integrates H2O, open source distributed machine learning platform, with the capabilities of Apache Spark. It allows users to leverage H2O’s machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O’s Flow GUI which makes Sparkling Water a great enterprise solution.

    Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. One of the latest and largest features is the ability to configure Sparkling Water for different workloads, scale and optimize the platform according to your data and needs.

    In this talk we will introduce the basic architecture of Sparkling Water, go over different scaling strategies and explain the pros and cons of each solution. We will also compare the approaches with regards to the specific use cases and provide the rationale why or why not each solution may be a good fit for the desired use case.

    This talk will finish with a live demo demonstrating the mentioned approaches and should give you a real time experience of configuring and running Sparkling Water for your use case(s)!
  • Data Science in Modern Banking
    Data Science in Modern Banking Charlie Leahy, Head of Software Architecture and Data Science (Hufsy) Recorded: Apr 13 2017 45 mins
    Banks have a vast wealth of mineable data available to them, but traditionally have provided their customers with little feedback beyond a balance and list of transactions.

    In this talk Charles Leahy, Tech Lead at Hufsy, looks at ways in which tools such as visualisation and machine learning can be employed to give users meaningful insights, helping them make the most of their money.
  • Applied Data Science
    Applied Data Science Giovanni Lanzani, Chief Science Officer at GoDataDriven Recorded: Apr 13 2017 49 mins
    Now that the Data Science hype is levelling out, many companies are wondering what went wrong as they could not extract values from their data science efforts.

    In this webinar we will explore what does it take to apply data science and machine learning in the real world.

    Key takeaways include:
    - How can you go beyond the traditional data warehouse when doing machine learning
    - How should you adapt your processes to keep monetizing on your data
    - How to close the feedback loop between your customers and your machine learning models
    - What kind of profiles are essential to successfully become a data driven organization
  • How Machine Learning Helps Predict Equipment Failure
    How Machine Learning Helps Predict Equipment Failure Yaroslav Nedashkovskyi, System Architect at SoftElegance Recorded: Apr 13 2017 23 mins
    We are going to discuss a case study on a unified data lake for the oil industry -- it is a software architecture and a set of microservices that are used to get business values from the data that are generated during the oil production. Math models were developed to make failure prediction of rod pumps during the oil artificial lifting.

    We used modern capabilities of Big Data Architecture, based on Apache Spark set of technologies, machine learning, archived data, and streaming data from wells to build a unified math model to predict failure of that kind of industrial equipment.

    Join this webinar to learn:
    -- How machine learning can help to predict failure of industrial equipment

    -- Architecture to handle near real-time data-flow from oil wells
  • How to Build Chat-Bots Using Machine Learning and NLP
    How to Build Chat-Bots Using Machine Learning and NLP Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs Recorded: Apr 12 2017 64 mins
    Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. How Machine Learning frameworks have been applied in the real world continues to evolve and affect our daily lives, especially with chat bots.

    In this session Dr. Hodeghatta Rao will explain the fundamental concepts of Natural Language Processing (NLP), what are the practical applications of NLP, how machine learning is adopted to process the natural language and finally end with an overview of QnA (chat) system.
  • Semantic Natural Language Understanding w/ Spark, ML Annotators & DL Ontologies
    Semantic Natural Language Understanding w/ Spark, ML Annotators & DL Ontologies David Talby, CTO, Atigeo Recorded: Apr 12 2017 62 mins
    A text-mining system must go way beyond indexing and search to appear truly intelligent. First, it should understand language beyond keyword matching. (e.g. distinguishing between “Jane has the flu,” “Jane may have the flu,” “Jane is concerned about the flu," “Jane’s sister has the flu, but she doesn’t,” or “Jane had the flu when she was 9” is of critical importance.) This is a natural language processing problem. Second, it should “read between the lines” and make likely inferences even if they’re not explicitly written. (e.g. if Jane has had a fever, a headache, fatigue, and a runny nose for three days, not as part of an ongoing condition, then she likely has the flu.) This is a semisupervised ML problem. Third, it should automatically learn the right contextual inferences to make. (e.g. learning on its own that fatigue is sometimes a flu symptom—only because it appears in many diagnosed patients—without a human ever explicitly stating that rule.) This is an association-mining problem, which can be tackled via deep learning or via more guided ML techniques.

    David Talby leads a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records and provides real-time inferencing at scale. The architecture is built out of open source big data components: Kafka and Spark Streaming for real-time data ingestion and processing, Spark for modeling, and Elasticsearch for enabling low-latency access to results. The data science components include spaCy, a pipeline with custom annotators, machine-learning models for implicit inferences, and dynamic ontologies for representing and learning new relationships between concepts.

    David Talby is Atigeo’s CTO, working to evolve its big data analytics platform to solve real-world problems in healthcare, energy, and cybersecurity. David has extensive experience in building & operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams.

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