Please Note: In-Person attendees will have access to virtual sessions. If you have a virtual pass, please note that we will not live-stream any in-person sessions. Only virtual sessions will be recorded. The schedule overview is available HERE.
70+ Sessions Added
100+ more coming soon.
In-person | Keynote | Machine Learning | All Levels
In this talk, I’ll discuss some ways that we might cope with and address the unreliability of neural network models. As an initial coping strategy, I will first discuss a technique for detecting whether some content was generated by a machine learning model, leveraging the probability distribution that the model assigns to different content. Next, I will describe an approach for enabling neural network models to better estimate what they don’t know, such that they can refrain from making predictions on such inputs (i.e. selective classification). I will lastly describe methods for adapting models with small amounts of data to improve their accuracy under distribution shift…more details
Virtual | Talk | NLP & LLMs | Intermediate
Large language models (LLMs) have achieved a milestone that undeniably changed many held beliefs in artificial intelligence (AI). However, there re-mains many limitations of these LLMs when it comes to true language un-derstanding, limitations that are a byproduct of the underlying architecture of deep neural networks. Moreover, and due to their subsymbolic nature, whatever knowledge these models acquire about how language works will always be buried in billions of microfeatures (weights), none of which is meaningful on its own, making such models hopelessly unexplainable…more details
Virtual | Talk | Machine Learning | Intermediate
Many retail giants are experiencing huge inventory loss and shrinkage problems because they process a huge number of transaction activities every day across the U.S. and offer a liberal shopping policy to provide a convenient customer shopping and return experience. Due to the facts that 1) they have highly imbalance and complex transaction data set as there are enormous transaction data while different types of anomalies are exceedingly rare; 2) there are seldom predefined labels available as it is not feasible to have human experts manually review every transaction and identify anomalies, it is a challenging task to investigate customers’ return behaviors and prevent fraudulent activities…more details
Virtual | Talk | Machine Learning | Intermediate
In this talk, I will attempt to provide several “bird’s eye” views on GNNs. Following a quick motivation on the utility of graph representation learning, I will derive GNNs from first principles of permutation invariance and equivariance. We will discuss how we can build GNNs that are not strictly reliant on the input graph structure…more details
Virtual | Talk | NLP & LLMs | Beginner
Generative models have demonstrated how helpful they can be on general knowledge, helping students on their writing assignments. But as soon as you want to run it in a professional setting with prompts like “what are the three main feature requests from our largest customers?”, they demonstrate their lack of knowledge. In this session, I will introduce how Large Language Models (LLMs) can be connected to your data via semantic search. As I will present, there are many pitfalls and challenges. Some can be solved, when using the right technologies, others are still open problems…more details
In-person | Case Study
By adopting the proposed framework, organizations can effectively harness the potential of Gen AI, empowering all employees with invaluable insights to make data-driven decisions. This talk aims to inspire organizations to embrace a safe agile approach and cultivate a culture of experimentation and collaboration for the successful deployment of Gen AI solutions…more details
In-person | Track Keynote | Generative AI | All Levels
Generative AI has taken over the world by storm, but building Gen AI applications for production comes with a unique set of challenges. Questions around cost performance, risk, simplifying implementation for production, setting guardrails, adding automation where possible and leveraging CI/CD for ML all become even more important when Gen AI is involved…more details
In-person | Talk | MLOps | Data Engineering & Big Data | Machine Learning | Deep Learning | Intermediate
In this presentation, Amber Roberts, Machine Learning Engineer at Arize AI, will present findings from research on ways to measure vector/embedding drift for image and language models. With lessons learned from testing different approaches (including Euclidean and Cosine distance) across billions of streams and use cases, Roberts will dive into how to detect whether two unstructured language datasets are different — and, if so, how to understand that difference using techniques such as UMAP…more details
In-person | Talk | Machine Learning | All Levels
In this presentation, our speaker will commence by conducting a comprehensive review of a list of common failure factors. Additionally, they will present an AI-driven ecosystem approach for knowledge discovery and discuss a real-world test of this approach involving 14 knowledge discovery projects. Through this, attendees will gain valuable insights into the crucial connection between the success of data-driven knowledge discovery projects and advancements in AI technology…more details
In-person | Talk | Data Visualization & Data Analysis | All Levels
Save time and money by equipping everyone with fundamental data literacy and analytics skills. It sounds great, but most organizations bite off more than they can chew. Instead of trying to turn everyone into a data analyst, teach people the most vital descriptive analytics skills, and eliminate the flow of tedious work requests to your data experts…more details
Virtual |Talk | Machine Learning | Beginner – Intermediate
In this training session, we will delve into the intricacies of building robust and scalable recommendation engines specifically tailored for online food delivery services. We will introduce a newly released dataset called the Delivery Hero Recommendation Dataset (DHRH) and understand how this can be used for training different recommendation models.We will explore the challenges faced in this domain and discuss the techniques and best practices to overcome them, ensuring our recommendation systems can handle large-scale operations and adapt to changing customer preferences…more details
Virtual | Talk | Machine Learning | LLMs | Intermediate
A highlight of this workshop is the introduction to the “causal LLM”, a pioneering concept where LLMs are designed using foundational causal principles. By the end of this session, participants will be equipped with the skills and knowledge to employ LLMs effectively in discerning complex causal models and pioneering advancements in the field of causal AI…more details
Virtual | Talk | Generative AI | All Levels
Human creators have provided the works (whether prose writing, source code, or visual works) that act as the basis for training huge DNNs, arguably create an obligation for the AI outputs. This feels most evident when promps ask AIs to create something “in the style of such-and-such-human.” While such outputs are often flawed in interesting ways, they are also usually recognizable in their connection to the prompted human creator. What rights should those source humans have to control those uses, including the moral right simply to be formally recognized as the source? Few laws exist governing attribution and moral rights in generative AI, but many will come to exist soon. Laws and technical standards may follow good or bad principles, both ethical and technical…more details
Virtual | Talk | MLOps and Data Engineering | Intermediate
With the wide adoption of generative artificial intelligence (AI), more than ever, ensuring the robustness of machine learning models is becoming crucial. One of the most concerning security threats to machine learning (ML) is the potential for adversarial attacks, a technique to exploit vulnerabilities of models to cause incorrect output. They are unapparent to humans but sufficient for machine learning models to misclassify the data, potentially harming the end users. Therefore, including adversarial training in the ML lifecycle is important to consider as you build out your model and prepare it for production usage. Join this talk to learn how the symbiosis between the ML security open source projects like Adversarial Robustness Toolbox and an ML operations (MLOps) project, Kubeflow, can streamline your machine learning workflow and improve your model’s robustness and security. With numerous benefits of the MLOps to assist in generating and defending your model, accelerate your development of secure machine learning models…more details
Virtual | Talk | Machine Learning | Deep Learning | Intermediate-Advanced
In this session we will deep dive into all the new developments and techniques in PyTorch and provide recommendations on how you can accelerate your models using native PyTorch code…more details
In-person | Talk | LLMs | Machine Learning | Intermediate
In the realm of advanced computational linguistics, the efficacy of Large Language Models (LLMs) is intrinsically tied to their contextual precision. In this session presented by Jake from Cloudera (not State Farm), we’ll navigate the complexities of ensuring LLMs yield contextually accurate results, a necessity in today’s intricate data environments. Crucially, attendees will be treated to a live demonstration showcasing the utilization of RAG (Retrieval-Augmented Generation) and PEFT (Parameter Efficient Fine-Tuning) techniques, two of the leading approaches for this task that underpin the success of Cloudera’s Applied ML Prototypes (AMPs)…more details
In-Person | Talk | NLP & LLMs | GenAI | Advanced
In this talk, I will present data2vec, a framework for general self-supervised learning that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches…more details
Virtual | Talk | Responsible AI | Intermediate
Digital experimentation and A/B testing are invaluable methodologies within the AI domain, facilitating the validation and continuous improvement of models, solutions, and systems. This talk will delve into the intricate role of these testing mechanisms in the AI landscape.
We will start by introducing the concept of digital experimentation and A/B testing, elaborating on their integral role in testing hypotheses and making data-driven decisions. The discussion will further touch upon the traditional uses of these methodologies in the digital marketing sphere, enabling businesses to optimize their online content and increase user engagement…more details
In-person | Talk | Machine Learning | Beginner
Statistics do not come intuitively to humans; they always try to find simple ways to describe complex things. Given a complex dataset, they may feel tempted to use simple summary statistics like the mean, median, or standard deviation to describe it. However, these numbers are not a replacement for visualizing the distribution…more details
Virtual | Talk | Machine Learning Safety and Security | Intermediate
This session will focus on security threats to machine learning models, including a demonstration of the similarities and differences between adversarial attacks in the domains of computer vision and natural language processing (NLP) with examples from open source projects like Adversarial Robustness Toolbox and TextAttack. Join the session to discuss applying adversarial research to real-world systems and learn how to be proactive with machine learning security…more details
Virtual | Talk | NLP | Beginner
In this talk, we will delve into the intricate relationship between NLP task definition and the outcomes of NLP system development, exploring how the precise formulation of tasks can either propel or hinder progress. Through case studies and examples, attendees will understand how ambiguous or biased task definitions can lead to misguided model objectives, high data acquisition costs, and misleading system evaluations, and learn how to strike the right balance between specificity and adaptability in NLP task design…more details
In-person | Talk | All Levels
Open source technology has historically driven technological progress, from operating systems, to web development, and now, AI and machine learning. We’re at a point in history where the canonical “open source ML stack” has yet to be discovered. In this talk, you’ll hear about HPE’s approach to building an end-to-end ML stack, including Determined AI for model training and Pachyderm for data preparation. You’ll also see a live demo of how to use Determined AI for a transfer learning use case in the biomedical image domain. We’ll also touch on some key collaborations, including our partnerships with the AI Infrastructure Alliance, NVIDIA, and Aleph Alpha…more details
In-person | Ai X Talk | Intermediate
In an era of Generative AI and Large Language Models, using old school machine learning might sound quaint. The reality is, however, that hidden with your corporate data is a trove of information and hidden business patterns. Leveraging key techniques from Machine Learning can help businesses uncover hidden patterns that will have immediate impact on your bottom line and on your ROI…more details
In-person | Talk | NLP & LLMs | GenAI | Intermediate
There seems to be a new large ML model grabbing headlines every week. Whether it’s OpenAI’s big releases like GPT-3, Dalle-2 or Whisper, or one of the many open source projects generating state-of-the-art models, like Stable Diffusion, OpenFold or Craiyon, these models have found their way into the mainstream. Lukas will map the landscape for you and share how these teams use W&B to accelerate their work…more details
Virtual | Talk | LLMs | ML/AI Safety and Security | All Levels
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you..more details
In-person | Talk | Generative AI | NLP | Machine Learning | Research Frontiers (Research topics in ML, DL, Data Science etc) | All Levels
This talk will dive into the end-to-end process of and framework for building a generative AI application, leveraging a fun and engaging case study with open-source tooling (e.g., HuggingFace models, Python, PyTorch, Jupyter Notebooks). We will guide attendees through key stages from model selection and training to deployment, while also addressing fine-tuning versus prompt-engineering for specific tasks, ensuring the quality of output, and mitigating risks…more details
In-person | Talk | Beginner
Little is known about the history of Neural Networks. For instance, “”Artificial Intelligence.”” The history goes back to around 1946 when researchers noticed that the mathematics involved with linear algebra wherein materials were stretched that the neighboring atoms were affected, was best modeled with a branch of Math known as tensors. Tensors are used today to create neural networks. Neural networks are run on a powerful Graphics Processing Unit (GPU.) GPUs came about because of video games and entertainment. Thus we can say that video games laid the groundwork for AI…more details
In-person | Talk | LLMs | Machine Learning | Intermediate
In the fast-paced world of data science and AI, we will explore how large language models (LLMs) can elevate the development process of Apache Spark™ applications.
We’ll demonstrate how LLMs can simplify SQL query creation, data ingestion, and DataFrame transformations, leading to faster development and more precise code that’s easier to review and understand. We’ll also show how LLMs can assist in creating visualizations and clarifying data insights, making complex data easy to understand…more details
In-person | Talk | Data Analytics and Big data | Machine Learning | MLOps | Beginner – Intermediate
When choosing different architectures for your enterprise data environment, you always want to make sure how to manage choices in the CAP theorem. The CAP theorem states that you can not have consistency, availability, and partition-tolerance at the same time, but what if choosing for Kappa architecture makes it possible to have it all?…more details
In-person | Talk | All Levels
Attendees will learn how to apply NLP to financial data. Earnings data covers all industries in the US; attendees will be surprised to learn how much information is hiding in plain sight. Earnings calls are public information and I’m going to show you what you’re able to learn from them using ASR and NLP tools.
Speech recognition technology has come a long way in recent years, but there are still some larger market challenges when it comes to transcribing speech accurately. This comes into play especially for domain-specific terminology around certain industries. For example, in the renewable energy industry, transcription might include terms like “hydro DM,” “biomass conversion,” and “renewable portfolio standard,” that are outside normal conversation…more details
In-person | Talk | Beginner – Intermediate
Manufacturers of modern age are looking for data science solutions to help move their 3 key metrics, – Produce more, Be efficient and optimize resource utilization, and ship with highest possible quality. Given these 3 KPIs , we will understand how data science influences each of these and will deep dive over the key projects that have influenced these KPIs. We deep down on specific projects. Eg how we use recursive hypothesis testing to reduce the testing frequency, how we understand the physics behind a test and optimize, how we make mass production decisions based on design of experiments and small distribution analysis…more details
In-person | Talk | Deep Learning | Machine Learning | Intermediate
Keras, the popular deep learning library, is now becoming multi-backend with support for TensorFlow, JAX, and PyTorch. Available now, Keras Core allows developers to create models with all of the simple high-level components of Keras while interchanging between frameworks to take advantage of the benefits of each. Existing users of each framework can seamlessly integrate their code, including backend-specific code, with Keras. Additionally, Keras Core’s ops suite, which includes the NumPy API, allows you to develop custom components for use on any framework. For current users of tf.keras, Keras Core with the TensorFlow backend serves as a drop-in replacement, meaning that changing your imports is all that is necessary to start taking advantage of the framework-agnostic future…more details
In-person | Talk | LLMs | Machine Learning | Intermediate
In this presentation, we explore an approach that utilizes LLMs to guide feature engineering. By leveraging the contextual understanding within LLMs, we have developed a system for LLM-assisted feature engineering. Our research demonstrates the practical benefits of this synergy – from feature ideation to improved feature relevance to enhanced model interpretability and efficiency…more details
In-person | Keynote | Machine Learning | All Levels
We have seen amazing technical progress in AI applications in recent years. This talk considered the human side rather than the technical side: how can we gain confidence that our applications will be fair, just, truthful, beneficial, and well-stirred for their users, the other stakeholders, and society at large…more details
In-Person | Business Talk | All Levels
By harnessing the power of machine learning and AI, companies can unlock advanced capabilities and gain valuable insights that streamline sales processes and enhance sellers’ performance. This infusion eliminates the laborious task of manual information analysis across multiple disparate data sources, empowering sellers to dedicate their time and expertise to building meaningful customer relationships. In architecting AI-based solutions, it is of high importance to understand the users’ daily workflow and challenges, and not just surfacing the insights but surfacing the right information at the right place to simplify users’ workflow…more details
In-person | Ai X Talk | Generative AI
In this session you will learn about some early experiences, and best practices of deploying Generative AI solutions in enterprises…more details
In-person | Ai X Talk | Intermediate
Critical success factor for enterprise level AI adoption and sponsorship is the ability to rapidly deploy AI technologies, solve use cases quickly, and deliver iterative business value to stakeholders. In this session, we will discuss the challenges of AI adoption, share some pragmatic approaches that enterprises can adopt to accelerate their AI / ML initiatives and deliver quick and iterative business value. Examples of some approaches would include leveraging pre-integrated AI frameworks and automation techniques such as AutoML, along with ready-to-use industry-specific AI solutions. The faster and more incremental the delivery of business value through AI, the higher the likelihood of successful adoption and implementation of AI within enterprises…more details
In-person | Talk | Intermediate-Advanced
In today’s data-driven world, recommendation systems have become ubiquitous, driving user engagement and increasing revenue for businesses across various domains. This talk will take you on a journey from the raw data to vectorization techniques, ultimately culminating in the creation of a robust recommendation system. Whether you’re a seasoned data scientist or just starting your journey into recommendation systems, this presentation will provide valuable insights and practical takeaways for building powerful recommendation engines…more details
In-person | Talk | Machine Learning | Deep Learning | Intermediate
In this talk I will present a new, hybrid approach which combines the best aspects of both methods. The process begins with a careful observation of customer data and assessment of whether there are naturally formed clusters in the data. It continues with the selection of a clustering algorithm and the fine-tuning of a model to create clusters…more details
In-person | Talk | Machine Learning | Intermediate
Machine learning has undergone a profound transformation with open source. From a technology that can only do naive curve fitting to technology that could potentially end humanities dominion. In 2017, Ali Rahimi declared that Machine learning is the new alchemy and we’d like to go further and claim that Machine Learning is the new necromancy. A forgotten science with a passionately strong open source ethos that was eventually destroyed by the catholic church…more details
In-person | Talk | NLP & LLMs | Intermediate-Advanced
In this session, we will take a deep dive into a novel application of AI: training Large Language Models (LLM) on individual employee’s Slack messages. The first portion of our discussion is dedicated to the technical aspects of this process, where we will explain the steps involved in fine-tuning the LLM. We will demonstrate how such models, tailored to mimic specific individual’s textual styles, can serve as the foundation for applications in text generation and automated question answering systems.
Transitioning into the second part of our talk, we will spotlight the often-underemphasized side of AI deployment – the risks and ethical concerns. Using our project as a case study, we will expose you to the potential pitfalls we encountered and the proactive measures taken to manage these risks. Our aim is to underline the necessity of an encompassing risk management framework for AI…more details
In-person | Talk | MLOps | Intermediate
Ensuring proper data quality is critical in the effective implementation of data pipelines for ML, data science, geospatial analysis, or general analytics.
Most engineering teams address data quality and pipeline orchestration as two separate tasks. In this presentation, Sandy Ryza will explain the benefits of a model in which arbitrary checks are included in the data orchestration logic, resulting in better control and integration of data quality checks at various steps in the pipeline…more details
In-person | Talk | Generative AI | Machine Learning Safety and Security | Beginner-Intermediate
In today’s digital world, we have access to everything at our fingertips. We generate and consume data at a lightning speed. Data is growing at an exponential rate. But how safe is your data? Our data is always surrounded by advanced persistent threats (APTs). On the other hand, Data Science, Machine Learning & Artificial Intelligence (AI) with its sub-domains like Deep Learning and Large Language Models (LLMs) are making tremendous progress. In this talk, I will focus on the advanced technologies like LLMs and GPT models in the domain of cyber security. But some of these concepts are relatable to other industries as well…more details
In-person | Talk | Machine Learning | MLOps and Data Engineering | All Levels
The robotaxi industry has one of the most extreme use case for Machine Learning with very large image/LiDAR datasets and resource-hungry training jobs. I will recount my experience leading the ML Infrastructure team at Cruise, the #1 robotaxi company. From training on desktop GPUs to being entirely cloud-based, I will detail the challenges we faced, how we solved them. I will also detail what guarantees need to be built into any production-grade ML platform to ensure fast, robust, and safe ML development…more details
In-person | Talk | MLOps | Data Engineering & Big Data | All Levels
Over the past decade, the democratization of data science tooling, particularly through Python libraries like pandas and NumPy, has empowered practitioners of all levels to work with data efficiently. Yet, despite the popularity of these tools, they present challenges as practitioners look to scale their workflows to production. In this talk, we explore the limitations of these tools and pain points that data scientists encounter when working with data at scale. I will share how our open-source project Modin (10M+ downloads) addresses this issue by seamlessly scaling up your pandas code with just a single line of code change…more details
In-person | Talk | Deep Learning | MLOps and Data Engineering | Intermediate – Advanced
In this talk, we’ll provide an overview of the core ideas behind Saturn, how it works on a technical level to reduce runtimes & costs, and the process of using Saturn for large-model finetuning. We’ll demonstrate how Saturn can accelerate and optimize large-model workloads in just a few lines of code and describe some high-value real-world use cases we’ve already seen in industry & academia…more details
In-person | Talk | Machine Learning
Deploying advanced Machine Learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems, which has led to the development of MLOps. Large models make rigorous engineering and scalable architectures even more important, which in turn has led to the emergence of LMOps. Just the size of the models themselves, and the datasets used for training, require highly efficient infrastructure. More complex pipeline topologies which include transfer learning, fine tuning, instruction tuning, and evaluation along a collection of complex dimensions, require a high degree of flexibility for customization. Add to this the complex inference-time systems and requirements, and LMOps starts to look somewhat challenging to implement…more details
In-person | Talk | Deep Learning | NLP | Machine Learning | MLOps and Data Engineering | Beginner – Intermediate
We discuss a unified and user friendly approach to develop ML applications on MySQL HeatWave AutoML. We describe a simple to use MySQL API for HeatWave AutoML, its extension for different use cases and its ability to interface with third party applications. We also discuss how this facilitates the development of classification, regression, anomaly detection, forecasting and recommendation systems use cases, and present its comparison with other platforms that offer ML features for data bases. Lastly, we present how a user can fine tune the AutoML pipeline for their needs…more details
In-person | Half-Day Training | Machine Learning | Intermediate-Advanced
As machine learning models have become more present in our lives, there has been increasing attention on the reliability of these models. A major component of this is understanding the how uncertain the model is about its prediction. No model is exactly right 100% of the time, so we need methods and approaches by which we can quantify the level of uncertainty around a prediction.
Approaches to uncertainty quantification (UQ) vary, and depend on the type of problem. For classification problems, the primary approach is probability calibration: making sure that the model outputs corresponding to each class “behaves well” as a probability. For regression problems, there are several different approaches. One can configure models to output an interval, rather than a single point prediction, along with a “coverage” value that specifies the probability that the interval covers the true value. The framework of Conformal Prediction provides theoretical guarantees around such interval predictions. Or one can use methods that output an entire conditional density for y given X. This is called probabilistic regression, or conditional density estimation. Several parametric and non-parametric approaches exist for this problem including PrestoBoost, Coarsage, and NGBoost…more details
In-person | Half-Day Training | Machine Learning | All Levels
In this 90-minute hands-on lab, you’ll learn how to start your Graph journey with ArangoDB. We will walk you through getting a basic deployment up and running in minutes. We will then help you create a deployment in the ArangoGraph insights platform and learn how to load and query your data…more details
In-person | Workshop | Machine Learning | Beginner
Pandas can be tricky, and there is a lot of bad advice floating around. This tutorial will cut through some of the biggest issues I’ve seen with Pandas code after working with the library for a while and writing three books on it…more details
Virtual | Training | Generative AI | All Levels
This engaging workshop provides a hands-on journey into the world of Generative AI and Autonomous Agents, crucial building blocks towards achieving Artificial General Intelligence (AGI). Participants will delve into the transformative shift in AI, gaining insights into the revolutionary impact of Generative AI across various domains such as text, image, video, and 3D object generation, as well as data augmentation. The workshop will equip attendees with cutting-edge tools to substantially boost their AI capabilities and efficiency. A key feature of the session is a practical exercise in crafting an Autonomous AI Agent, a technology poised to work in synergy with us in the near future. This succinct yet thorough workshop is tailored for those eager to maintain a competitive edge in the swiftly advancing field of AI…more details
In-person | Training | MLOps and Data Engineering | Machine Learning | Deep Learning | All Levels
Our objective is to ensure that you are equipped with the essential knowledge and practical tools to proficiently manage your machine-learning models in a real-world production environment…more details
In-person | Workshop | Machine Learning | Deep Learning | NLP | ALL | Intermediate
In this hands-on, example driven workshop, we’ll introduce Mojo🔥 language features by starting with Python code and making minor changes to convert into high-performance Mojo🔥 code. Mojo provides full interoperability with the Python ecosystem, and we’ll show you how to integrate Mojo🔥 into your existing Python workflows. We’ll share the workshop material as a hosted tutorial with several Mojo🔥 scripts and Jupyter Notebooks which you can use as a starting point for your projects…more details
In-person | Workshop | Data Engineering & Big Data | Machine Learning | Generative AI | Data Visualization | Beginner
In this presentation, Alison will take you through the example of one independent researchers journey from no-code to knowledge graph master. She will walk you through the steps the researcher took, including how to format data stores, what data to collect, ways to leverage LLMs in the process and ultimately have a working knowledge graph in a dashboard with minimal coding experience…more details
Virtual | Workshop | Machine Learning | Intermediate
In this talk, we will cover the use of Generative models, such as LLMs and GANs, for the generation of smart synthetic data that can be leveraged to impute missing data. By using a generative model to impute missing data, we can generate new samples that are representative of the underlying data distribution, which can help to reduce the impact of missing data on our models. In addition, these models can be fine-tuned to specific datasets, allowing us to generate synthetic data that is tailored to our particular use case…more details
Virtual | Tutorial | GenAI | LLM | All Levels
The tutorial will consist of the following parts: (1) Introduction and overview of the generative AI landscape, (2) Technical and ethical challenges with generative AI, and (3) Solutions for alleviating the challenges with real-world use cases and case studies (including practical challenges and lessons learned in industry)…more details
Virtual | Workshop | Machine Learning | Intermediate
In this talk, we will explore and demystify th world of Causal AI for data science practitioners, with a focus on understand cause-and-effect relationships within data to drive optimal decisions. In this talk, we will focus on:
* from shapley to DAGs: the dangers of using post-hoc explainability methods as tools for decision making, and how tranditional ML isn’t suited in situations where want to perform interventions on the system.
* discovering causality: how do we figure out what is causal and what isn’t, with a brief introduction to methods of structure learning and causal discovery
* optimal decision making: by understanding causality, we now can accurately estimate the impact we can make on our system – how to use this knowledge to derive the best possible actions to make?…more details
Virtual | Workshop | NLP & LLMs | Intermediate
The workshop will be hands-on and composed of 4 modules, targeting data scientists, machine learning experts, software engineers, and product managers. Prior exposure to LLMs is not necessary. At the end of the workshops, participants will have gained insights on how to effectively make the best of open-source LLMs and learned the necessary steps to bridge the gap between prototype and production-ready applications…more details
In-person | Tutorial | Generative AI | Machine Learning | Beginner – Intermediate
The tutorial will cover the existing research on the capabilities of LLMs versus small traditional ML models. If an LLM is the best solution, the tutorial covers several techniques, including evaluation suites like the EleutherAI Harness, head-to-head competition approaches, and using LLMs for evaluating other LLMs. The tutorial will also touch on subtle factors that affect evaluation, including role of prompts, tokenization, and requirements for factual accuracy. Finally, a discussion of model bias and ethics will be integrated into the working examples…more details
In-person | Workshop | Deep Learning | All Levels
Facial recognition systems are everywhere. Of course, it’s where you would expect it, such as airports, border crossings, and government offices. However, it’s also in some public surveillance cameras, all over social media, embedded in smart home solutions, and even in your phone. Have you ever wondered how facial recognition systems work? In this hands-on session, we will build a facial recognition system from scratch using open-source technologies and publically available pre-trained models…more details