ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

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AWS Machine Learning Blog

OCTOBER 2, 2024

This is often referred to as platform engineering and can be neatly summarized by the mantra “You (the developer) build and test, and we (the platform engineering team) do all the rest!” Amazon Bedrock is compatible with robust observability features to monitor and manage ML models and applications.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Pickl AI

AUGUST 28, 2024

Introduction In the rapidly evolving landscape of Machine Learning , Google Cloud’s Vertex AI stands out as a unified platform designed to streamline the entire Machine Learning (ML) workflow. This unified approach enables seamless collaboration among data scientists, data engineers, and ML engineers.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. Download Now.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

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ODSC - Open Data Science

JANUARY 10, 2024

Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. No-Code and Low-Code AI: A Practical Project-Driven Approach to ML Gwendolyn D.

Large Language Models 52

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

For production deployment, the no-code recipes enable easy assembly of the data ingestion pipeline to create a knowledge base and deployment of RAG or agentic chains. These solutions include two primary components: a data ingestion pipeline for building a knowledge base and a system for knowledge retrieval and summarization.

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

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