MLOps Conference

MLOps Conference

On DataOps, productionizing ML models, and running experiments at scale.

Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

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:

  1. Machine Learning Operations
  2. Machine Learning in Production
  3. Privacy and Security in Machine Learning
  4. Tooling and frameworks in Machine Learning
  5. 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?

  1. Data/MLOps engineers who want to learn about state-of-the-art tools and techniques.
  2. Data scientists who want a deeper understanding of model deployment/governance.
  3. Architects who are building ML workflows that scale.
  4. Tech founders who are building products that require ML or building developer productivity products for ML.
  5. Product managers, who are seeking to learn about the process of building ML products.
  6. 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

Vishal Gupta

@vizgupta

Jupyter to Jupiter : Scaling multi-tenant ML Pipelines

Submitted May 22, 2021

A 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 upon Apache Airflow, a workflow management platform and how we’ve used it to automate and parallelise various segments of our ML pipeline.

Outline

  • Introduction
  • Why Scale
    • To serve predictions (real-time & batch-wise)
    • To improve availability & adherence to SLA
    • To enable more customers to use ML models
    • To automate workflows
  • Challenges of a multi-tenant ML pipeline
    • Gathering data from different sources (in different regions)
    • Customizing feature engineering to handle diverse customer base… yet maintaining a unified pipeline
    • Training accurate models that also generalize well and replicating results across diverse customers
    • Building data and engineering pipelines to onboard customers with ease
    • Maintaining, monitoring and orchestrating models and workflows
  • Data Ingestion and preprocessing
    • Streamlining Data from different sources
    • Scaling & Optimising Data Pipelines
  • Model Training, Evaluation and Deployment
    • Scaling Offline ETL
    • Training and Evaluation models and metrics
    • Data and Model Versioning
    • Building training workflows
    • Model Customisation
  • Prediction, Back-filling and Interpretability
    • Scaling prediction pipelines
    • Logging and Monitoring
    • Product-specific insights
    • Testing
    • Drifts and Decay

Key Takaways :

  1. Building a multi-tenant ML pipeline to serve a diverse user base
  2. Tips, hacks and practises for scaling different parts of an ML pipeline
  3. Leveraging Airflow to accomplish the above
  4. Potential bottlenecks and issues one might face while solving the above

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Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

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