Notre soif de puissance de calcul et notre appétit pour les données génèrent une hausse significative des coûts informatiques. Ils contribuent également largement à la hausse des émissions de carbone, qui épuisent nos réserves en électricité et en eau potable. Alors que l’usage du cloud et de l’IA ne cesse de croître, bon nombre d’entre nous mettent en place des solutions pour que cet engouement s’opère tout en préservant notre planète. Rejoignez-nous et vos pairs de la communauté Sustainable Cloud lors de notre prochain Meetup à Paris, organisé par IBM. Un programme chargé qui comprend des intervenants experts et des sessions interactives en petits groupes. Examinons ensemble de près comment nous pouvons utiliser les stratégies FinOps et GreenOps pour rendre l’IT rentable ET durable.

16:30 Accueil 17:00 Bienvenue & Introduction (Mike Bradbury, Antoine Lagier) 17:10 Keynote - Arkema; notre parcours FinOps (Philippe le Blevec) 17:20 Débats éclairs - Samuel Rince\, Algune - Francois Moriamez\, Thales - Carole Crumbach\, Orwell Consulting - James Hall\, GreenPixie - Alex Long\, Apptio 18:00 Tables rondes 18:15 Cocktails & Networking 19:00 Fin

Avis de confidentialité: En vous inscrivant à ce meetup vous consentez au traitement de vos informations personnelles à des fins de gestion et de communication de l'événement. Vos données pourront être partagées avec l'organisateur et les sponsors, uniquement si nécessaire pour le bon déroulement de l'événement. Vous disposez des droits de retrait, d'accès, de correction et de suppression de vos données personnelles. Si vous souhaitez vous désinscrire ou si vous avez des questions concernant l'utilisation de vos données, veuillez nous contacter à [email protected]

When Feb 15, 2024 – 10:00 AM Pacific

Where Virtual / Zoom - https://voxel51.com/computer-vision-events/feb-2024-ai-machine-learning-data-science-meetup/

Agenda

Lightning Talk: The Next Generation of Video Understanding with Twelve Labs

The evolution of video understanding has followed a similar trajectory to language and image understanding - with the rise of large pre-trained foundation models trained on a huge amount of data. Given the surge of multimodal research lately, video foundation models are becoming even more powerful to decipher the rich visual information embedded in videos. This talk will explore diverse use cases of video understanding and provide a glimpse of Twelve Labs offerings.

Speaker: James Le is the Head of Developer Experience at Twelve Labs, a startup building multimodal foundation models for video understanding.

Towards Fair Computer Vision: Discover the Hidden Biases of an Image Classifier

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, previous bias identification methods overly rely on human experts to conjecture potential biases, which may neglect other underlying biases not realized by humans. Is there an automatic way to assist human experts in finding biases in a broad domain of image classifiers? In this talk, I will introduce solutions.

Speaker: Chenliang Xu is an Associate Professor in the Department of Computer Science at the University of Rochester. His research originates in computer vision and tackles interdisciplinary topics, including video understanding, audio-visual learning, vision and language, and methods for trustworthy AI. He has authored over 90 peer-reviewed papers in computer vision, machine learning, multimedia, and AI venues.

Food Waste Classification with AI

1/3 of all food gets wasted, with millions of tons of food being thrown away each day. Food does not mean the same thing everywhere in the world, there are thousands of different meals across the world, therefore a lot of different classes to distinguish between. In this talk we’ll see through challenges of food-waste classification and see how foundation models can be useful to this task. We will also explore how we use FiftyOne to test models during development.

Speaker: Luka Posilović is a computer scientist with a PhD from FER, Zagreb, Croatia, working as a Head of machine learning in Kitro. Him and the team are trying to reduce the global food waste problem by using AI.

Objects and Image Geo-localization from Visual Data

Localizing images and objects from visual information stands out as one of the most challenging and dynamic topics in computer vision, owing to its broad applications across different domains. In this talk, we will introduce and delve into several research directions aimed at advancing solutions to these complex problems.

Speaker: Safwan Wshah is an Associate Professor in the Department of Computer Science at the University of Vermont. His research interests encompass the intersection of machine learning theory and application, with a particular emphasis on geo-localization from visual information. Additionally, he maintains broader interests in deep learning, computer vision, data analytics, and image processing.

Don’t Forget

• Voxel51 will make a donation on behalf of the Meetup members to the charity that gets the most votes this month.
• Can’t make the date and time? No problem! Just make sure to register here so we can send you links to the playbacks.

When Feb 15, 2024 – 10:00 AM Pacific

Where Virtual / Zoom - https://voxel51.com/computer-vision-events/feb-2024-ai-machine-learning-data-science-meetup/

Agenda

Lightning Talk: The Next Generation of Video Understanding with Twelve Labs

The evolution of video understanding has followed a similar trajectory to language and image understanding - with the rise of large pre-trained foundation models trained on a huge amount of data. Given the surge of multimodal research lately, video foundation models are becoming even more powerful to decipher the rich visual information embedded in videos. This talk will explore diverse use cases of video understanding and provide a glimpse of Twelve Labs offerings.

Speaker: James Le is the Head of Developer Experience at Twelve Labs, a startup building multimodal foundation models for video understanding.

Towards Fair Computer Vision: Discover the Hidden Biases of an Image Classifier

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, previous bias identification methods overly rely on human experts to conjecture potential biases, which may neglect other underlying biases not realized by humans. Is there an automatic way to assist human experts in finding biases in a broad domain of image classifiers? In this talk, I will introduce solutions.

Speaker: Chenliang Xu is an Associate Professor in the Department of Computer Science at the University of Rochester. His research originates in computer vision and tackles interdisciplinary topics, including video understanding, audio-visual learning, vision and language, and methods for trustworthy AI. He has authored over 90 peer-reviewed papers in computer vision, machine learning, multimedia, and AI venues.

Food Waste Classification with AI

1/3 of all food gets wasted, with millions of tons of food being thrown away each day. Food does not mean the same thing everywhere in the world, there are thousands of different meals across the world, therefore a lot of different classes to distinguish between. In this talk we’ll see through challenges of food-waste classification and see how foundation models can be useful to this task. We will also explore how we use FiftyOne to test models during development.

Speaker: Luka Posilović is a computer scientist with a PhD from FER, Zagreb, Croatia, working as a Head of machine learning in Kitro. Him and the team are trying to reduce the global food waste problem by using AI.

Objects and Image Geo-localization from Visual Data

Localizing images and objects from visual information stands out as one of the most challenging and dynamic topics in computer vision, owing to its broad applications across different domains. In this talk, we will introduce and delve into several research directions aimed at advancing solutions to these complex problems.

Speaker: Safwan Wshah is an Associate Professor in the Department of Computer Science at the University of Vermont. His research interests encompass the intersection of machine learning theory and application, with a particular emphasis on geo-localization from visual information. Additionally, he maintains broader interests in deep learning, computer vision, data analytics, and image processing.

Don’t Forget

• Voxel51 will make a donation on behalf of the Meetup members to the charity that gets the most votes this month.
• Can’t make the date and time? No problem! Just make sure to register here so we can send you links to the playbacks.

The evolution of video understanding has followed a similar trajectory to language and image understanding - with the rise of large pre-trained foundation models trained on a huge amount of data. Given the surge of multimodal research lately, video foundation models are becoming even more powerful to decipher the rich visual information embedded in videos. This talk will explore diverse use cases of video understanding and provide a glimpse of Twelve Labs offerings.

Join us once again for the next PyData Bristol Meetup! The PyData community in Bristol continues to thrive with engaging talks, insightful lightning sessions, and lively networking. A massive thank you to our generous hosts, Cookpad, for supplying the venue, Adlib for the scrumptious pizza, and thirst-quenching refreshments, along with additional sponsorship.

Agenda for the evening: 🚪 6:00 pm - Doors open 🕡 6:30 pm - Talks commence (sharp!)

📚 Two 25-minute talks:

• Talk 1: Hiring for diversity - Dr Elena Hensinger-Schien
• Talk 2: DBT - Data Build Tools - James Yarrow

📚 One 5-minute lightning talks:

• Lightning Talk 1: Signal Processing with CNN - Michael Roberts
• Managing a million tiles to (sort of) cure cancer - James Leech

📢 Community announcements 🤝 Relaxed networking over beers and soft drinks

Interested in sharing your knowledge or experience at this or a future event? Fill out this form to submit your talk proposal: https://goo.gl/forms/8lsz1WA1986Ahbbs1 We look forward to seeing you there for another fantastic evening of Python, Data Science, and camaraderie!

Talks

1. Introduction to DBT - James Yarrow

DBT or Data Build Tools is a python library with a huge open source community and so much more. DBT focuses on the Transformation in ELT and is used by analytics engineers, data engineers and data scientist. It can be used to managed models in SQL server with great python extensibility and governance features.

2. Inviting diversity when hiring for data jobs - Dr Elena Hensinger-Schien

When looking to build a diverse team, the job description, the interview task and the overall communication experience can be either inviting or off-putting for candidates. This talk will share some best practice, such as inclusive language and a critical assessment of our interview goal and approach, that will be beneficial not only to candidates from underrepresented groups within data, but all candidates.

Lightning Talks

1. Signal Processing with CNN - Michael Roberts

This talk will give a brief overview of a method to apply Convolutional Neural Networks to analysis of audio data through the use of the Discrete Fourier Transform. This approach is used in the current state-of-the-art algorithm for musical segment boundary detection, the subject of my MSc thesis.

2. Managing a million tiles to (sort of) cure cancer - James Leech

Medical computer vision is complicated because biopsy scans are huge. Think of it as a satellite map analysis tool, where you are trying to read the road marking to determine what country you’re in. It’s a subclass of image analysis called multiple instance learning. In this lightning talk I want to introduce the specific challenges associated with medical computer vision, some solutions I’ve learned thus far, and some gaps in current technology you could fill to make yourself rich.

🕖 LOGISTICS Talks kick off at 18:30 sharp; then networking in The Knights Templar from 20:40. If you realise you can't make it, please un-RSVP in good time to free up your place for your fellow community members. Follow @pydatabristol (https://twitter.com/pydatabristol) for updates on this and future events, as well as news from the global PyData community.

📜 CODE OF CONDUCT The PyData Code of Conduct governs this meetup (https://pydata.org/code-of-conduct/). To discuss any issues or concerns relating to the code of conduct or behaviour of anyone at the PyData meetup, please contact the PyData Bristol organisers, or you can submit a report of any potential Code of Conduct violation directly to NumFOCUS (https://numfocus.typeform.com/to/ynjGdT).

Dive deep into how machine learning is transforming the financial industry with 'Machine Learning for Finance'. This comprehensive guide explores cutting-edge concepts in machine learning while providing practical insights and Python code examples to help readers apply these techniques to real-world financial scenarios. Whether tackling fraud detection, financial forecasting, or sentiment analysis, this book equips you with the understanding and tools needed to excel. What this Book will help me do Understand and implement machine learning techniques for structured data, natural language, images, and text. Learn Python-based tools and libraries such as scikit-learn, Keras, and TensorFlow for financial data analysis. Apply machine learning for tasks like predicting financial trends, detecting fraud, and customer sentiment analysis. Explore advanced topics such as neural networks, generative adversarial networks (GANs), and reinforcement learning. Gain hands-on experience with machine learning debugging, products launch preparation, and addressing bias in data. Author(s) James Le None and Jannes Klaas are experts in machine learning applications in financial technology. Jannes has extensive experience training financial professionals on implementing machine learning strategies in their work and pairs this with a deep academic understanding of the topic. Their dedication to empowering readers to confidently integrate AI and machine learning into financial applications shines through in this user-focused, richly detailed book. Who is it for? This book is tailored for financial professionals, data scientists, and enthusiasts aiming to harness machine learning's potential in finance. Readers should have a foundational understanding of mathematics, statistics, and Python programming. If you work in financial services and are curious about applications ranging from fraud detection to trend forecasting, this resource is for you. It's designed for those looking to advance their skills and make impactful contributions in financial technology.