Towards AI

FEBRUARY 18, 2025

Building a demo is one thing; scaling it to production is an entirely different beast. It has already inspired me to set new goals for 2025, and I hope it can do the same for other ML engineers. They also inspired a bunch of new potentials for ML engineers. Everything changed when Deepseek burst onto the scene a month ago.

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ML ML Data Quality Algorithm 100

AWS Machine Learning Blog

JUNE 3, 2024

Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. SageMaker Data Wrangler has also been integrated into SageMaker Canvas, reducing the time it takes to import, prepare, transform, featurize, and analyze data.

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AWS ML ML AI 117

ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation. Some of the issues make perfect sense as they relate to data quality, with common issues being bad/unclean data and data bias.

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Machine Learning Machine Learning Data Wrangling Data Science 94

Iguazio

MAY 29, 2024

In the application pipeline, teams can swap: Logging inputs + responses to various data sources (database, stream, file, etc.) Additional data sources (RAG, web search, etc.) Classical ML models and LLMs If using QLORA to fine-tune, teams can swap out domain specific fine tuned adapters while using the same base model (e.g.

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Data Pipeline AI AI ML 103

AWS Machine Learning Blog

APRIL 21, 2023

The last attribute, Churn , is the attribute that we want the ML model to predict. See the following code: # Configure the Data Quality Baseline Job # Configure the transient compute environment check_job_config = CheckJobConfig( role=role_arn, instance_count=1, instance_type="ml.c5.xlarge",

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Data Quality ML ML AWS 97

DagsHub

DECEMBER 15, 2024

At DagsHub, we're building a platform to simplify your ML workflows. Every project consists of data, experiments, and models. At DagsHub we manage all those, and focus on helping you build and improve the quality of unstructured datasets, so you get high-performing models. Let’s dive into some details.

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ODSC - Open Data Science

FEBRUARY 28, 2023

We couldn’t be more excited to announce our first group of partners for ODSC East 2023’s AI Expo and Demo Hall. These organizations are shaping the future of the AI and data science industries with their innovative products and services. Check them out below.

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Data Science Machine Learning Machine Learning AI 96

ODSC - Open Data Science

OCTOBER 25, 2024

At the AI Expo and Demo Hall as part of ODSC West next week, you’ll have the opportunity to meet one-on-one with representatives from industry-leading organizations like Plot.ly, Google, Snowflake, Microsoft, and plenty more. Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai

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AI AI Data Scientist Data Lakes 52

Alation

JULY 19, 2021

Artificial intelligence and machine learning (AI and ML) are removing some of the burden of manual metadata management, which has grown too cumbersome for people to manage alone. Data intelligence integrates intelligence derived from active metadata into categories like data quality, governance, and profiling.

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Data Governance Machine Learning Machine Learning Artificial Intelligence 105

ODSC - Open Data Science

OCTOBER 20, 2023

At the AI Expo and Demo Hall as part of ODSC West in a few weeks, you’ll have the opportunity to meet one-on-one with representatives from industry-leading organizations like Microsoft Azure, Hewlett Packard, Iguazio, neo4j, Tangent Works, Qwak, Cloudera, and others.

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AI AI Data Science Machine Learning 52

Snorkel AI

JANUARY 24, 2023

Unfortunately accessing data across various locations and file types and then operationalizing that data for AI usage has traditionally been a painfully manual, time-consuming, and costly process. Ahmad Khan, Head of AI/ML Strategy at Snowflake, discusses the challenges of operationalizing ML in a recent talk.

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AI AI ML ML 52

Snorkel AI

JANUARY 24, 2023

Unfortunately accessing data across various locations and file types and then operationalizing that data for AI usage has traditionally been a painfully manual, time-consuming, and costly process. Ahmad Khan, Head of AI/ML Strategy at Snowflake, discusses the challenges of operationalizing ML in a recent talk.

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AI AI ML ML 52

Snorkel AI

JANUARY 31, 2023

Building a machine learning (ML) pipeline can be a challenging and time-consuming endeavor. Inevitably concept and data drift over time cause degradation in a model’s performance. For an ML project to be successful, teams must build an end-to-end MLOps workflow that is scalable, auditable, and adaptable.

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AI AI ML ML 52

Snorkel AI

JANUARY 31, 2023

Building a machine learning (ML) pipeline can be a challenging and time-consuming endeavor. Inevitably concept and data drift over time cause degradation in a model’s performance. For an ML project to be successful, teams must build an end-to-end MLOps workflow that is scalable, auditable, and adaptable.

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AI AI ML ML 52

ODSC - Open Data Science

SEPTEMBER 20, 2023

Tuesday is the first day of the AI Expo and Demo Hall , where you can connect with our conference partners and check out the latest developments and research from leading tech companies. Finally, get ready for some All Hallows Eve fun with Halloween Data After Dark , featuring a costume contest, candy, and more. What’s next?

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Data Wrangling Data Science Machine Learning Machine Learning 52

Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Supercharge predictive modeling.

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AI AI ML ML 64

Snorkel AI

JUNE 2, 2023

Databricks customers can now access millions of rows of data seamlessly within the Snorkel Flow platform thanks to a new Databricks connector. Weeks later, on June 29, Snorkel AI Founding Engineer and Product Director Vincent Chen will present at “ Building AI-Powered Products with Foundation Models ” at the Databricks Data + AI Summit.

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SQL Machine Learning Machine Learning ML 52

Snorkel AI

JUNE 2, 2023

Databricks customers can now access millions of rows of data seamlessly within the Snorkel Flow platform thanks to a new Databricks connector. Weeks later, on June 29, Snorkel AI Founding Engineer and Product Director Vincent Chen will present at “ Building AI-Powered Products with Foundation Models ” at the Databricks Data + AI Summit.

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SQL Machine Learning Machine Learning ML 52

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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Machine Learning Machine Learning Data Scientist ML 69

IBM Journey to AI blog

JULY 27, 2023

Request a demo to see how watsonx can put AI to work There’s no AI, without IA AI is only as good as the data that informs it, and the need for the right data foundation has never been greater. A data lakehouse with multiple query engines and storage can allow engineers to share data in open formats.

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AI AI Data Scientist Data Quality 69

Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Book a demo today.

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AI AI ML ML 59

Pickl AI

NOVEMBER 3, 2023

It went from simple rule-based systems to advanced data-driven algorithms. Today, real-time trading choices are made by AI using the combined power of big data, machine learning (ML), and predictive analytics. Algorithms for ML: AI models employ ML to adjust and find correlations, gradually improving accuracy.

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AI AI Artificial Intelligence Artificial Intelligence 52

Snorkel AI

MARCH 14, 2023

Snorkel AI and Google Cloud have partnered to help organizations successfully transform raw, unstructured data into actionable AI-powered systems. Snorkel Flow easily deploys on Google Cloud infrastructure, ingests data from Google Cloud data sources, and integrates with Google Cloud’s AI and Data Cloud services.

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AI AI Data Scientist ML 52

Snorkel AI

MARCH 14, 2023

Snorkel AI and Google Cloud have partnered to help organizations successfully transform raw, unstructured data into actionable AI-powered systems. Snorkel Flow easily deploys on Google Cloud infrastructure, ingests data from Google Cloud data sources, and integrates with Google Cloud’s AI and Data Cloud services.

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AI AI Data Scientist ML 52

Precisely

JULY 18, 2024

Key Takeaways Data Fabric is a modern data architecture that facilitates seamless data access, sharing, and management across an organization. Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata.

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Data Lakes Data Warehouse Data Governance Machine Learning 52

Iguazio

FEBRUARY 8, 2024

They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps. Data Pipeline - Manages and processes various data sources.

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ML ML Data Scientist AI 52

O'Reilly Media

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% AI users are definitely facing these problems: 7% report that data quality has hindered further adoption, and 4% cite the difficulty of training a model on their data. Deploying and managing AI products isn’t simple.

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The MLOps Blog

NOVEMBER 14, 2023

This is where visualizations in ML come in. Graphical representations of structures and data flow within a deep learning model make its complexity easier to comprehend and enable insight into its decision-making process. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

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Deep Learning Deep Learning Data Scientist Machine Learning 111

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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AWS Database ETL AI 93

Iguazio

JANUARY 14, 2025

Data management - Ensuring data quality through data ingestion, transformation, cleansing, versioning, tagging, labeling, indexing, and more. Development - High quality model training, fine-tuning or prompt tuning, validation and deployment with CI/CD for ML.

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AI AI Deep Learning Deep Learning 52

Alation

SEPTEMBER 30, 2022

Our advanced search capability means that we can build the easiest-to-use and massively powerful interfaces for data stewards. Our ability to leverage ML for each allows us to make operations teams far more efficient. Our comprehensive connectors mean that we can build lineage much more efficiently. However, we can’t do it alone.

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Data Governance Azure SQL Data Quality 92

DataRobot Blog

DECEMBER 15, 2022

If your dataset is not in time order (time consistency is required for accurate Time Series projects), DataRobot can fix those gaps using the DataRobot Data Prep tool , a no-code tool that will get your data ready for Time Series forecasting. Prepare your data for Time Series Forecasting. Configuring an ML project.

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Exploratory Data Analysis AI AI Machine Learning 81

Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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Data Scientist Machine Learning Machine Learning AI 69

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming.

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Data Governance Data Scientist Data Quality Data Profiling 59

ODSC - Open Data Science

SEPTEMBER 17, 2024

Large Model Quality and Evaluation Anoop Sinha | Research Director, AI & Future Technologies | Google Large model development faces many challenges when it comes to ML quality and evaluation, including the coverage, scale, and wide use cases for what LLMs are used for. Check out a few of them below.

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Database Data Science ML ML 52

Iguazio

AUGUST 9, 2024

We also show a banking chatbot demo that includes fine-tuning a model and adding guardrails. Data Management - Ensuring data quality through data ingestion, transformation, cleansing, versioning, tagging, labeling, indexing, and more. Using the same data for model improvement. The four pipelines include: 1.

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AI AI Data Science Deep Learning 52

Iguazio

AUGUST 9, 2024

We also show a banking chatbot demo that includes fine-tuning a model and adding guardrails. Data Management - Ensuring data quality through data ingestion, transformation, cleansing, versioning, tagging, labeling, indexing, and more. Using the same data for model improvement. The four pipelines include: 1.

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AI AI Data Science Deep Learning 52