MLOps Conference
On DataOps, productionizing ML models, and running experiments at scale.
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Pinned update
Birds of Feather (BOF) session on Observability for data and ML; SRE Conf CfP This update is for participants only
Machine Learning (ML) is at the helm of products. As products evolve with time, so is the necessity for ML to evolve. In 2010s, we saw DevOps culture take the forefront for engineering teams. 2020s will be all about MLOps.
MLOps stands for Machine Learning Operations. MLOps mainly focuses on workflows, thought processes and tools that are used in creating ML models, and their evolution over time. The workflows for ML at organizations are different as the problem space, maturity of teams and experience with ML tools are widely different.
MLOps relies on DataOps. DataOps is about Data operations, and helps define data and SLOs for data - how they are stored, managed and mutate over time - thereby providing the foundations for sound ML. The success and failure of ML models depends heavily on DataOps, where data is well-managed and brought into the system in a well thought out manner. ML and data processes have to evolve to provide insights into the reasons as to why certain models are not behaving as before.
Productionizing ML models is a challenge, but so is running experiments at scale. MLOps caters not only to scaling ML models in production, but also helps in providing guidelines and thought processes to support rapid prototyping and research for ML teams.
MLOps Conference 2021 edition
The 2021 edition is curated by Nischal HP, Director of Data at Scoutbee.
The conference covers the following themes:
- Machine Learning Operations
- Machine Learning in Production
- Privacy and Security in Machine Learning
- Tooling and frameworks in Machine Learning
- Economies of Machine Learning
Speakers from Doordash, Twilio, Scribble Data, Microsoft Research Labs India, Freshworks, Aampe, Myntra, Farfetch and other organizations will share their experiences and insights on the above topics.
Schedule: https://hasgeek.com/fifthelephant/mlops-conference/schedule
Who should participate in MLOps conference?
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require ML or building developer productivity products for ML.
- Product managers, who are seeking to learn about the process of building ML products.
- Directors, VPs and senior tech leadership who are building ML teams.
Contact information: Join The Fifth Elephant Telegram group on https://t.me/fifthel or follow @fifthel on Twitter. For inquiries, contact The Fifth Elephant on fifthelephant.editorial@hasgeek.com or call 7676332020.
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Supported by
Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more
Promoted
Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more
Accepting submissions till 14 Jul 2021, 11:00 PM
Not accepting submissions
ML workflows and processes are critical for enabling rapid prototyping and deployment of ML/AI models in any organization. This conference is a platform to present and discuss such workflows and patterns that drive data-driven organizations to operate at scale.
We are accepting experiential talks and written content on the following topics:
- ML development workflows.
- ML deployment frameworks.
- Data lineage.
- Model lineage.
- Model ethics/bias testing.
- A/B testing frameworks.
- Model governance.
- Explainability/interpretability of models in run-time.
- Impact of change in MLOps mindset in product organizations.
- DataOps workflows.
- DataOps frameworks.
- Alerting, monitoring and managing models in production.
- Growing and managing data teams.
- MLOps in research.
- Deployment and infrastructure for machine learning.
- ROI (Return on Investment) for MLOps.
Who should speak?
- MLOps engineers who build and maintain ML workflows and deploy ML models.
- Data engineers building production scale data pipelines, feature stores, model dashboards, and model maintenance.
- Tech leaders/engineers/scientists/product managers of companies who have built tools and products for ML productivity.
- Tech leaders/engineers/scientists/product managers of companies who have built tools, products, processes for Data Ops to support ML.
- Tech Leaders/engineers/scientists/product managers who have experience with products that failed to make a mark in the market due to ML failures.
- Investors who are investing in the space of ML productivity tools, frameworks and landscape.
- Privacy/ethics stakeholders involved in model governance and testing for ethics/bias.
Content can be submitted in the form of:
- 15 minute talks
- 30 minute talks
- 1,000 word written articles
All content will be peer-reviewed by practitioners from industry.
Make a submission
Accepting submissions till 14 Jul 2021, 11:00 PM
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upendra singh Privacy Attacks in Machine Learning Systems - Discover, Detect and DefendMy name is Upendra Singh. I work at Twilio as an Architect. As a part of this talk proposal I would like to shed some light on the new kind of attacks machine learning systems are facing nowadays - Privacy Attacks. During the talk we will explain and demonstrate how to discover, detect and defend Privacy related vulnerabilities in our machine learning models. Will also explain why it is so critic… more
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haridas n  Process of Open Source your ML ServicePic2Card is an Opensource ML service that helps to create AdaptiveCards from an Image. We have recently contributed this service to AdaptiveCards, an Opensource card authoring framework from Microsoft. more
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Venkata Pingali Past and Future of Feature StoresAudience Level: Intermediate Nature: Conceptual https://drive.google.com/file/d/1WpKUXeC0i93f72vC8yXtUR6unPEaB4ma/view?usp=sharing more
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Venkata Pingali Scribble Enrich - 2nd Generation Feature Engineering PlatformScribble Enrich - 2nd Generation Feature Engineering Platform more
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Vishal Gupta Jupyter to Jupiter : Scaling multi-tenant ML PipelinesA brief talk summarising the journey of an ML feature from a Jupyter Notebook to production. At Freshworks, given the diverse pool of customers using our products, each feature has dedicated models for each account, churning out millions of predictions every hour. This talk shall encompass the different tools and measures we’ve used to scale our ML products. Additionally, I’ll also be touching up… more
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Monojit Choudhury Massively Multilingual Neural Language Models: The new hope and fear for low resource languagesNatural Language processing is undergoing a paradigm shift right now; the models today are ever more powerful, capable and accurate. Thus, it looks like we are close to achieving the holy grail of AI. However, these assertions are true only for a handful of the world’s languages which have enough language data to train powerful and large models. What about the rest of the languages? In this talk,… more
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Milecia McGregor Tuning Hyperparameters with DVC ExperimentsWhen you start exploring multiple model architectures with different hyperparameter values, you need a way to quickly iterate. There are a lot of ways to handle this, but all of them require time and you might not be able to go back to a particular point to resume or restart training. more
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swaroopch Maintaining Machine Learning Model Accuracy Through MonitoringMachine Learning models begin to lose accuracy as soon as they are put into production. At DoorDash, we implemented a robust monitoring system to diagnose this issue and maintain the accuracy of our forecasts. more
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Schaun Wheeler Story-telling as a method for building production-ready machine-learning systemsThis presentation is about the essential but overlooked role storytelling plays in machine learning productionization. Although often assumed to be at best a tangential concern, machine learning systems only become successful through successfully building trust in the model, the data, and the system in which the model and data operate. That trust-building happens through storytelling. Most often,… more
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sachin nagargoje Fighting Fraudsters in Email Communication at Twilio using Machine LearningMy name is Sachin Nagargoje. I work at Twilio as a Staff Data Scientist. As a part of this talk proposal I would like to shed some light on the kind of attacks we are facing at Twilio nowadays and how we are tackling it via different innovative ways and Machine Learning techniques. I want to showcase what are the challenges we face, and how we do and what we do to catch such unwanted communicatio… more
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Pratik Bhavsar End2End Serverless Transformers On AWS Lambda For NLPTransformers are everywhere! But how to serve them? How do you leverage serverless to get scalability without any worries? Isn’t serverless used for light applications?. How to get the best latencies with your serverless? I will be sharing answers to these questions in my talk. more
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lavanya TS Fairness in ML: How do we build unbiased ML workflows?Biases often arise in automated workflows based on MachineLearning models due to erroneous assumptions made in the learning process. Examples of such biases involve societal biases such as gender bias, racial bias, age bias and so on. more
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Neha Gupta Automatic rollbacks for MLOps deployments in KubernetesWhile there are different tooling to automate deployments of ML models most of them require manually written rules for verifying deployments in production. more
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Nirant K ML Ops for StartupsContext: Early Startup While a plethora of MLOps work has been done at large data and model serving scale, this is an attempt to introduce you to different ops problems, tools and how to pick among them. In most cases, I’ll share an example to anchor the idea set further. more
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Sandilya Konduri Leveraging ML algorithms to deliver faster in an extremely chaotic environmentEcommerce in our country has come quite some distance and along with it has been successful in reshaping customer expectations and his/her perception of hygiene & delight. Speedy delivery now has become a base expectation of today’s customer. Add a certain set of categories , the need for quicker SLA just escalates higher. more
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Sajan Kedia Myntra Home Page Personalization: Optimizing Online Feature Store at ScaleThis talk is mainly about productionizing ML Model and Optimizing Online Feature Store at the scale of India’s Biggest Fashion E-Commerce. Myntra Home Page consists of widgets(targeted cards). Currently, their ranking is the same for all the users based on the business metrics(CTR, Revenue). To truly personalize the home page, we’ve deployed the ranking ML Model which talks to feature stores to g… more
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Rajeev Kumar Model Health Assurance at scale at LinkedInAt LinkedIn, AI models drive numerous use-cases - across ranking (eg: Feed items, jobs relevance), and classification (wg: content quality, account protection) use-cases - with hundreds of experiments performed every week. At this scale, it is also important to standardize and platformize how the said monitoring is also done so that we don’t reinvest on the same things, we can effect change acros… more
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Sai Sharan Tangeda Managed Feature Store: Improving data reusability & Providing a means for low latency real-time prediction at UdaanA brief talk on Managed Feature Store built on top of Open Source Feast. We will start with a brief walkthrough of the open source Feast feature store including the architecture and core capabilities. We would call out some of the challenges/limitations of the open source Feast feature store. We would then describe some of the enhancements which enables us to have a more robust, secure and scalab… more
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Mario Rozario ML Model Scaling with Teradata VantageMy name is Mario Rozario and I work at Teradata. As psrt of this talk I would be briefly covering our latest product Vantage and some of its inherent strengths. One of the biggest benefits of Vantage is the ability to now scale Machine Learning workloads based on our parallel technology. Now with Vantage, users will also be able to scale ML workloads that they build elsewhere and scale them here.… more
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Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Supported by
Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more
Promoted
Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more