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An AI Experience Playbook for Enterprise IT

Applying AI in business has limitless potential. More and more organizations are implementing AI solutions to improve their core business functions. AI is not only revolutionary for end-user experience but also drives productivity and efficiency through automation and cost-savings.

How do you leverage AI to streamline your core Enterprise IT organization?

In this webinar, AI experts Muckai and Sudhakar break down the need for curated historical data, the pros and cons of specific Enterprise IT/Cloud Challenges, and the necessary AI-native solutions. The webinar will provide examples powerful enough to attain the ultimate goal for personalization, automation, prediction, cost savings and improve experience.

Three takeaways planned for the session are:

•Discover how AI will make up 80% of user daily interactions
•Learn how AI delivers automated, human-like service experiences that empower users to self-solve problems
•How AI can be leveraged for your enterprise organization


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Recorded Mar 18 2020 49 mins
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Presented by
MuckAI Girish, CEO, Muck.AI | Muddu Sudhakar Entrepreneur & Executive
Presentation preview: An AI Experience Playbook for Enterprise IT

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  • Winning with Data Science for Executives Jun 11 2020 6:00 pm UTC 45 mins
    David Yakobovitch, Data Science Team Lead, Data Partnerships, Galvanize
    Why is a data-driven strategy essential for the success of your business? Data Science has come so far recently, but why? What industry trends are impacting the Data Science and AI industries? David explores common language to demystify data science, he reveals where companies are today in Data Maturity, and he articulates what data opportunities exist for companies to become data-driven.

    About the speaker:
    David Yakobovitch is a Principal Data Scientist at Galvanize, responsible for delivering Global Instruction, Scaled Training Programs, and Customer Success. He also partners with Engagement Managers and Account Executives with Pre-Sales and Product Marketing. David hosts the HumAIn Podcast, a Top 100 Technology Podcast on Artificial Intelligence, Data Science, Developer Education and Future of Work (www.humainpodcast.com). Prior to Galvanize, David led Enterprise programs at General Assembly (Bloomberg and Booz Allen Hamilton). Prior to GA, David worked in the financial services industry with banks, quantitative firms, and alternative data providers including Citigroup, Deutsche Bank, ADP, Aflac, Intel, and IBM Watson.
  • How to Unlock Logistics & Supply Chains with Artificial Intelligence Jun 11 2020 2:00 pm UTC 45 mins
    About Avkash Chauhan, Head of AI & Digital Intelligence at Roambee
    In an increasingly digitally connected world with abundant data sources, manufacturers and logistics companies need even more efficient ways to make effective supply chain decisions in real-time.

    How do you overcome the delays in analyzing real-time data? How can you translate that to better understand supply chain and distribution risks? How do you reduce the time to decide the best course of action and act on it?

    In order to truly empower your supply chain with data that you can trust, decipher in seconds, use “right then & there,” and act without further need for analysis, you need Artificial Intelligence (AI) to provide a “business-friendly view” of location and condition data that’s collected in the field.

    Join this webinar to learn how AI will provide a business-friendly view of your goods and assets monitoring data, and also play a crucial part in assimilating and instantly validating important data points across multiple data streams for faster and better decision-making.

    About Avkash Chauhan, Head of AI & Digital Intelligence at Roambee:

    Avkash Chauhan is transforming logistics and supply chain by using Artificial Intelligence to help manufacturers and 3PLs make the most of goods and asset visibility. Since joining Roambee, he is leading a team of AI solution developers and solution delivery engineers to assist their customers using AI technology and solutions to transform their business and have an edge over the competition. Avkash's career as a data scientist spans 20 years+ as an engineer, entrepreneur and tech leader working with global enterprises and businesses.
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    We will discuss how we at TBC made the organizational change, the use cases which were made possible through it and what kind of results were achieved. We will talk about lessons learned by the example of two of our successful use cases: digital affluent value proposition, and next best offer.

    Presented by:
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    Mariam Lelashvili, Analytical Transformation Project Leader, TBC Bank
    Levan Borchkhadze, Senior Data Scientist, TBC Bank
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    Richard Maguire, Chief Data Scientist, MDGroup
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    Managing the Big Data Wave

    - Performant Infrastructure not only to manage the data size and types, structured & unstructured, but to manage Compute at Scale
    - A Data Lake allows you to manage the wave of data by capturing all of it
    - Next step is to store data in a proper data store for data type and ensure that the data is clean
    - Then, you need to analyze the data for decision making

    Managing the Big Textual Data that is Usually Forgotten

    - Textual Data is Normally Siloed, not because it is unimportant, but because many do not know how to manage it
    - Natural Language Processing (NLP) uses linguistic and semantic parsing to uncover patterns that can be deployed in predictive analytics

    COVID-19 has Accelerated Move to Virtual Trials & Wearable Sensors: Disruptive Event & Technology

    - Major Benefit is Continuous Patient Sensor Data, so NOT Episodic
    - This means making correlations and predictions more accurate
    - The challenge is the size of this data stream as it can become a tsunami
    - Data Management and Data Cleaning are Paramount
    - Virtual Clinical Trials: The Future is Here Now

    Richard Maguire is presently the Head, Data Science, mdgroup and is tasked with bringing predictive analytics into their patient primary offering via their Primarius mobile app and patient portal. With a background in predictive analytics and Natural Language Processing, Rick has worked to bring wearable sensors into a Healthcare IoT for clinical trials. He has been a Subject Matter Expert at Oracle for Predictive Analytics and Predictive Genomic Medicine as well as Real World Data/Evidence for a global IT provider. Experience as a Director of Clinical Diagnostics in Pathology for a very large comprehensive cancer centre.
  • Practical AI: Predicting Business Outcomes with Analytics Jun 10 2020 3:00 pm UTC 60 mins
    Panel of experts
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    You'll discover:
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    - How predictive modeling can lead to more informed business decisions
    - What steps organizations can take to adopt an AI-enhanced analytics strategy that works for them
    - And more!
  • AI in Grundfos: Production Advances in Business Domains Jun 10 2020 10:00 am UTC 45 mins
    Lishuai Jing, Senior Data Scientist Analytics and AI group, Grundfos, Denmark
    Being historically a manufacturing company, Grundfos aims to bring digital transformation into its core business. This radical move not only bring challenges, but also energizes a wave of exploring/adopting state-of-the-art cloud computing, machine learning and AI technologies that impact different business domains. The new advances in computing platforms and machine learning services brings convenience to productionize industrial intelligent solutions. However, practical obstacles are hindering the scaling capability.

    This talk will take you to peek into some of the challenges that are faced in researching and deploying AI solutions within Grundfos. I will give you a picture on Grundfos’s digital initiatives landscape and our hands-on experience on scaling AI solutions that accelerates enterprise level adoption. Two concrete use cases in sales and marketing and supply chain management can shed some light on how business value, agile development, DevOps, data science, and MLops together enable success in business value creation. In particular, I will address the data challenge and the statistical methods, machine learning/deep learning techniques that are adopted to solve some real life challenges.

    About the speaker:
    Currently, Lishuai Jing is working with advanced statistical inference methods, machine learning and deep learning techniques, and general AI in the largest pump manufacturing company. He has a PhD diploma in statistical signal processing and wireless communication from aalborg university, Denmark. Before joining Grundfos, he was a researcher on 4G/5G and IOT communication technologies. He advocates data and AI-driven approach to improve business intelligence, operating efficiency, productivity and customer satisfaction.
  • Data Governance and The Art of the Fugue Jun 9 2020 9:00 pm UTC 45 mins
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    Without some guidelines, the result is cacophony, when sounds combine with no rhyme or reason, and the ear hears nothing but harshly clashing noise. In the data world, we have the same situation when data lakes become data swamps, filled with in-comprehensive information not fit for any use, because of the lack of data governance.

    Randy will also explore these ideas:
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    - How a similar approach to creating a data governance can fuel innovation

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    About the speaker:
    Randy Gordon is a data governance professional with over 10 years of experience in leadership roles in financial services. Most recently, at Moody’s, Randy established their first formal data governance program, building the data governance framework, principles, standards, and organization.
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    Pragyansmita Nayak, Ph.D., Chief Data Scientist, Hitachi Vantara Federal
    The melee of raw data in the various forms of structured, unstructured and semi-structured data has its own unique challenges. Extracting the hidden nuggets of information to meet the vision and aid the strategic and tactical goals of an organization is a resonating objective of every stakeholder today. This requires a number of systematic, both synchronous and asynchronous processes and techniques to rhythmically accomplish the objectives in a transparent manner. Data driven process enablement and the resulting effective decision making starts from scratch - identification of the most relevant and related data assets, posing the business problem, determining the analytics components.

    The solution should ideally be as reusable as possible in order to aid related problems resolution down the road; aiding the knowledge growth, process automation and the effective business and data interplay. The overarching goal needs to be to complete this unique jigsaw puzzle specific to every individual organization; fitting as comfortably and seamlessly as one wants their hands to fit in a pair of gloves.

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    Pragyansmita Nayak is Chief Data Scientist at Hitachi Vantara Federal (HVF), a wholly owned subsidiary of Hitachi Vantara. She has over 20+ years of experience in software development and data science-related research and development. She holds a Ph.D. in Computational Sciences and Informatics from George Mason University (GMU) and Bachelor's degree in Computer Science from Birla Institute of Technology and Science (BITS), Pilani, India. Her Ph.D. thesis focused on the application of Machine Learning techniques such as Bayesian Networks for redshift estimation.
  • Distributed AI/ML: Architecture for Advanced Analytics Jun 9 2020 8:00 am UTC 60 mins
    Kuldeep Jiwani, Distinguished Architect, Data Science, Guavus, a Thales company
    Distributed ML/AI design strategy serves as a potential solution as it solves two of the most notorious problems, performing near real time analytics on humongous high-speed data. Identify Machine Learning models and find means to integrate them in to existing Distributed computing architecture. First, since the data is large and fast paced it pushes ML closer to the source of data instead of bringing data from different sources at a central location and then applying ML. Second, it focuses on figuring out the right modelling approach combined with the right architectural design for effectively doing distributed ML processing.

    Distributed ML design strategy utilises best of both the worlds, Distributed Computing and Machine Learning algorithms. It brings along the advanced analytical and mathematical reasoning capabilities of Machine Learning and combine it with efficient distributed processing in the form of a group of machines either in a Big Data cluster or over a GPU farm or steaming edge devices or over IoT devices. It is more of a design thought process that enterprises should focus on while planning to leverage Distributed ML architecture for their business problems.

    About the speaker:
    Kuldeep Jiwani: Researcher, Data Scientist, Data Science Architect, Performance specialist, Entrepreneur.
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  • Technology Trends Shaping the Automobile Industry Recorded: Apr 30 2020 64 mins
    MuckAI Girish, CEO, Muck.AI and Srini Bangalore, CEO, Yujala
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    https://www.linkedin.com/in/muckaigirish/

    and

    Srini Bangalore, CEO, Yujala
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
    - http://bit.ly/2PJlPHm
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
    - http://bit.ly/2PJlPHm
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
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    Join BrightTALK's LinkedIn Group for BI & Analytics!
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  • Title: An AI Experience Playbook for Enterprise IT
  • Live at: Mar 18 2020 9:00 am
  • Presented by: MuckAI Girish, CEO, Muck.AI | Muddu Sudhakar Entrepreneur & Executive
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