Tuba Islam, Google Cloud on "ML Journey into production - What are the challenges and how to tackle them?"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Tuba Islam is a machine learning specialist at Google Cloud, based in London, primarily focusing on deep learning, forecasting, natural language processing and the automation of machine learning solutions. 

Before Google, she was a data scientist at SAS, working globally across Europe and the US. She started her career in research and development at the National Research Institute of Electronics and Cryptology in Turkey implementing speech recognition engine for Turkish. She has an engineering background in electronics and holds a master’s degree in digital signal processing.

She delivered successful projects across various industries such as rogue trader fraud detection in capital markets, smart metering analytics in utilities, hazard detection and readmission prediction in healthcare, credit risk in banking, churn prediction in telecom, rate making in insurance, demand forecasting in retail, pharmacovigilance analysis in life sciences.

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ML Journey into production

What are the challenges and how to tackle them?

In this talk, we will share the key challenges that the organizations are likely to face when they move their machine learning models from experimentation stage into production and provide recommendations on how to handle these challenges with the right process and technology in place.

James Hamilton, TUI on "It’s not all about the model – challenges applying artificial intelligence"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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James Hamilton

I have spent my entire career applying data, analytics and modelling to deliver value in different organisation across a range of sectors including retail, public sector and now travel. My greatest satisfaction comes from building and developing analytics teams and in coming up with innovative solutions to new complex problems.

In my current role I head the data science capability and am also lead product owner for data driven solutions in the commercial part of our business, mostly focus on automated pricing. 

Prior to this I worked as head of customer investment strategy at Tesco and as an analytics and modelling consultant at PwC. I have a maths degree from Exeter and a masters in operational research from Lancaster.

It’s not all about the model – challenges applying artificial intelligence

In TUI we have a very successful automated pricing system that has delivered significant benefits over a number of years. We continue to develop it incrementally and our current focus is on better use of our data and applying artificial intelligence.

We have experienced a number of challenges in applying these approaches and I will talk about our experiences, how we are overcoming them and why if we get to training a new model we have already done the hard part.

Samuel Madden, MIT CSAIL on "Outlier and Data Debugging"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Samuel Madden is a Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. His research interests include databases, distributed computing, and networking. Research projects include the C-Store column-oriented database system, the CarTel mobile sensor network system, and the Relational Cloud "database-as-a-service". Madden is a leader in the emerging field of "Big Data", heading the Intel Science and Technology Center (ISTC) for Big Data, a multi-university collaboration on developing new tools for processing massive quantities of data. He also leads BigData@CSAIL, an industry-backed initiative to unite researchers at MIT and leaders from industry to investigate the issues related to systems and algorithms for data that is high rate, massive, or very complex.

Madden received his Ph.D. from the University of California at Berkeley in 2003 where he worked on the TinyDB system for data collection from sensor networks. Madden was named one of Technology Review's Top 35 Under 35 in 2005, and is the recipient of several awards, including an NSF CAREER Award in 2004, a Sloan Foundation Fellowship in 2007, best paper awards in VLDB 2004 and 2007, and a best paper award in MobiCom 2006. He also received a a "test of time" award in SIGMOD 2013 (for his work on Acquisitional Query Processing in SIGMOD 2003), and a ten year best paper award in VLDB 2015 (for his work on the C-S

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Outlier and  Data Debugging

Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of these datasets by introducing outliers -- data points that exhibit surprising behaviour when compared to the rest of the data. In this talk I’ll describe some recent tools we’ve built at MIT CSAIL to identify these outliers in data, including a tool called AutoOD designed to automate many aspects of adapting existing outlier detection methods to complex datasets.

Merve Alanyali, Head of Data Science Academic Partnerships and Research at LVGI, attending the panel on 'Panel on "Data quality and data anomalies'

 Conference TFNetworkAutumn21

Data Science - The beating heart of AI
➣ Conference overview and registration 
➣ YouTube TFNetworkSummer21 Conference Playlist 

Merve Alanyali is Head of Data Science Academic Partnerships and Research at Liverpool Victoria General Insurance with more than five years’ experience in academic research. She draws on an interdisciplinary background in computer science, complex systems and behavioural science. 

Prior to joining LVGI, she worked at The Alan Turing Institute as a research associate leading a group of data scientists on a short-term collaborative project with one of the top electronics companies. She completed her doctoral degree on “Quantifying human behaviour using online images” at the University of Warwick with Chancellor’s International Scholarship and The Alan Turing Institute Enrichment Scheme funding. She was awarded a double degree Master’s degree in Complex Systems Science by the University of Warwick and Chalmers University of Technology, Sweden. 

Her work has been featured by television and press worldwide including coverage in Financial Times and Bloomberg Business.

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Panellist on: Data Quality and Data Anomalies

Alex Healing, Future Cyber Defence Research at BT on "Human-Machine Collaborative Analytics"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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➣ YouTube TFNetworkSummer21 Conference Playlist 

Alex Healing is a senior research manager at BT Labs where he is responsible for the Future Cyber Defence programme. His team design and build AI and visualisation tools that allow security analysts to better understand and respond to evolving cyber threats. He has published work in several conferences and journals, is co-inventor of over twelve inventions, and won the title of Young IT Professional of the Year at the UK IT Industry Awards in 2012. He has a degree in Artificial Intelligence and Computer Science from the University of Edinburgh.

Human-Machine Collaborative Analytics

The use of AI is clearly a critical part of analysing data at the scale that today’s IT systems allow, but sadly the human user in the system is often an afterthought. This talk will touch on the challenge, opportunity and progress made so far to create more human-centric AI systems for data analysis, involving visual interfaces for presenting machine learning results, and to help analysts better explore and generate insight from data.

Faisal Nazir, AWS on "The importance of explainability in AI"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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➣ YouTube TFNetworkSummer21 Conference Playlist 

Faisal Nazir has over 22 year’s experience in engineering, development, integration and strategy consulting. In the past Faisal has worked for Motorola, Cisco, Redhat, Booz Allen and now AWS. Faisal has a Masters in Quantum Physics from Imperial College London.

Faisal is currently working as a Machine Learning Specialist for Amazon also has interests in Blockchain Technologies and Quantum Computing and actively works on projects in all three disciplines.

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The importance of explainability in AI

Data scientist could potentially wield great power over the lives of everyday people. This power comes from how they develop ML models that can be used to make life-changing decisions. Explainability - having knowledge of why an model makes an inference - is the field that tries make sense of a models decision. We will discuss what tooling is available to Data Scientists to help them find out what is going on with the models they train.

Rob Claxton, BT Senior Manager – Big Data, Insight & Analytics on "Are we nearly there yet? Model baselines and Performance"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
➣ Conference overview and registration 
➣ YouTube TFNetworkSummer21 Conference Playlist 

Rob Claxton is a Senior Manager at BT Applied Research. He graduated from the University of York in 1993 with a MEng in Electronic Systems Engineering and has spent his career with BT in a variety of technology roles including work on signalling systems and software development for BT’s Intelligent Network platform. 

In 2005 Rob joined BT Research where he currently leads the Big Data, Insight and Analytics team and played a key role in BT’s adoption of big data technology. 

His team are actively applying machine learning and AI to real-world problems including natural language processing and machine vision. Rob also leads the AI Governance work stream at the TM Forum where he is helping to develop a framework for the safe operation of AI deployed at scale.

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Are we nearly there yet? Model baselines and Performance

The process of building models has a strong emphasis on ‘digital performance testing’. In other words, how accurate is my model? Whilst this makes sense when building prototypes or testing the state of the art, it can lead to problems when developing models for production. In this talk we turn the tables and ask the question, how good does my model need to be?