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Resources

Welcome to Algorithmia's resources page. Navigate to our publications and videos to learn more about us and how we think about enterprise machine learning.

REPORT

5 latest trends in enterprise machine learning

Discover five key trends in enterprise AI/ML that have emerged throughout 2021—and what you need to plan for success in 2022 and beyond.

Read the report

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REPORT

The framework for ML governance

Get a comprehensive guide for effectively implementing machine learning governance at every stage of the lifecycle in your enterprise.

Read the report

WHITEPAPER

7 steps to effective AI governance

Learn why you need a new framework to manage your AI governance—and how to put it into place at your organization.

Read the whitepaper

CASE STUDY

CEMEX: Cementing ML success with AI governance

Hear how CEMEX uses Algorithmia to secure and govern global ML applications.

CASE STUDY

Merck

Learn how Merck is using Algorithmia’s MLOps platform to accelerate vaccine research and discovery.

CASE STUDY

Deloitte: Building a best-in-class customer experience platform

Learn how Deloitte uses Algorithmia to improve customer experience with best-in-class MLOps.

CASE STUDY

Happy Money

Learn how Happy Money dramatically sped up ML model iteration time with help from Algorithmia.

Case study

EY

Learn how Algorithmia is able to support EY in delivering a scalable ML service that allows EY's clients to deploy models quickly and securely, ensuring continuity of business with zero downtime.

Case study

Toyota insurance branch MS&AD

Toyota's insurance group increased their models in production by 75 percent when they used Algorithmia as their deployment platform.

Case Study

Tevec

Tevec is a supply chain recommendation platform that uses machine learning to forecast demand and suggest optimized replenishment/fulfillment order for logistics chains. See how Algorithmia enables Tevec's goals.

Case Study

United Nations

Algorithmia partnered with the UN to build a repository of algorithms that are available to every member state to aid in smarter social, health, environmental, and economic decision making.

DEMO

Approval workflows

Learn how to manage approval workflows in Algorithmia, including options for integrating with external Git systems.

Demo

Versioning

Learn how to handle model versioning in Algorithmia, including publishing model updates and performing rollbacks.

Demo

Compliance

Learn how to perform compliance- and risk-based audits of model activity in Algorithmia.

Demo

Pipelining

Learn how to develop model pipelines in Algorithmia, including models that use different languages, frameworks, and versions.

Demo

Jupyter Notebook integration

Create, update, and publish algorithms using the Algorithmia Python client, and deploy models directly from an existing Jupyter Notebook workflow.

WEBINAR

Cementing ML success with AI governance

Hear how CEMEX uses Algorithmia to secure and govern global ML applications.

TALK

Building a best-in-class customer experience platform: The Hux journey

Learn how Deloitte uses Algorithmia to improve customer experience with best-in-class MLOps.

TALK

Merck accelerates vaccine discovery with Algorithmia

Learn how Merck is using Algorithmia’s MLOps platform to accelerate vaccine research and discovery.

TALK

Bridging the gap: From model development to machine learning management

Discover top strategies for taking ML projects from development to management in this industry-expert panel hosted by Algorithmia CEO Diego Oppenheimer.

WEBINAR

7 steps to effective AI governance

Join Algorithmia and AI Powered Banking for a webinar about the 7 steps to put an effective governance strategy in place.

16 March 2021

TALK

Take your ML models from training to production

Watch this session from TWIMLcon 2021 to learn how to deploy your ML models into scaled production with Algorithmia.

WEBINAR

2021 enterprise trends in machine learning

Discover the top 10 trends in enterprise machine learning for 2021—and our tips for companies that want to succeed with AI/ML in the coming year.

10 December 2020

WEBINAR

Algorithmia Insights demo

Learn how to get started with Algorithmia Insights for ML model performance monitoring.

17 November 2020

Webinar

Machine learning out-of-the-lab and into production

Join industry analyst Michael Azoff and Algorithmia CEO Diego Oppenheimer as they explore the disruption and collaboration between data science and AI engineering on the road to ML production.

15 October 2020

Webinar

Eight must-haves for MLOps success and when to use them

Join Mike Gualtieri, Forrester VP, and Diego Oppenheimer, CEO of Algorithmia, as they discuss the eight critical areas of machine learning operations.

17 September 2020

Webinar Q&A

Q&A featuring Forrester

Read our Q&A from the Eight must-haves for MLOps success webinar featuring Mike Gualtieri, Forrester VP.

Webinar

MLOps: Planning for the new normal

Join Kahini Shah, Investor at Gradient Ventures, and Diego Oppenheimer, CEO of Algorithmia, as they discuss the impacts that the global pandemic is having on AI/ML.

TALK

Happy Money: A comprehensive guide to MLOps

Watch this recording from Ai4 to hear Chris Courtney, VP of Science at Happy Money, discuss planning for the new normal with MLOps.

Webinar

How EY prevents financial crimes using AI/ML

Join Carl Case, Principal at EY, and Diego Oppenheimer, CEO of Algorithmia, to hear how Algorithmia supports EY in delivering a scalable ML service that allows EY's clients to deploy models quickly and securely.

10 September 2020

Webinar

A comprehensive guide to MLOps

Join Patchen Noelke, Senior Director of Product Marketing at Algorithmia, as he presents a framework for understanding enterprise machine learning in the next normal.

16 July 2020

Webinar

2020 enterprise trends in the new normal

Learn how large enterprises are changing their ML initiatives in reaction to the pandemic and how the industry is trending for the rest of 2020 and beyond.

12 November 2020

Webinar

Advanced actions on Algorithmia: Governance and security with GitHub Actions

Join James Sutton, Senior Customer Engineering Architect, for an instructional webinar on using GitHub Actions to enable continuous integration for machine learning.

25 June 2020

Webinar

Building versus buying: how to achieve ML operations and management

Join Sam Charrington, TWIML AI podcast host, and Kenny Daniel, Algorithmia CTO, as they discuss what goes into building an MLOps platform and how to evaluate off-the-shelf ML management solutions.

9 June 2020

Webinar

10 measures and KPIs for ML success

In this webinar, you'll learn how to gain leverage and mitigate risks in the enterprise machine learning journey.

Webinar

5 missteps of ML every operations manager can avoid

Many companies view machine learning as integral to digital transformation and a key competitive advantage. But common missteps can happen along the way. In this webinar, you'll learn how to course correct.

Webinar

2020 state of enterprise machine learning

Join Adam Wenchel, CEO of ArthurAI, and Diego Oppenheimer, CEO of Algorithmia, as they discuss the current and future state of enterprise machine learning.

20 February 2020

TALK

Rapid prototyping ML microservices using Agile

Learn how data scientists and engineers can work with Agile principles on the Algorithmia platform to build, test, and implement models quickly.

TALK

DevOps for machine learning and other half-truths: Processes and tools for the ML lifecycle

To build and deploy enterprise-ready models that generate real value, businesses need to standardize on a new stack and a new, ML-focused lifecycle. Learn why.

TALK

From R&D to ROI: Realize value by operationalizing machine learning

Algorithmia CEO Diego Oppenheimer speaks at ODSC West on the importance of productionizing machine learning models.

TALK

The new SDLC: CI/CD in the age of machine learning

Learn how you can adapt your infrastructure, operations, staffing, and training to meet the challenges of the new software development lifecycle (SDLC) without throwing away everything that already works.

26 September 2019

REPORT

5 latest trends in enterprise machine learning

Discover five key trends in enterprise AI/ML that have emerged throughout 2021—and what you need to plan for success in 2022 and beyond.

REPORT

The framework for ML governance

Get a comprehensive guide for effectively implementing machine learning governance at every stage of the lifecycle in your enterprise.

WHITEPAPER

7 steps to effective AI governance

Learn why you need a new framework to manage your AI governance—and how to put it into place at your organization.

REPORT

2021 enterprise trends in machine learning

Discover the 10 machine learning trends that enterprises should be paying attention to if they want to succeed with AI/ML in 2021.

WHITEPAPER

MLOps maturity: A roadmap for success

Guide your company to machine learning maturity with our roadmap of the key steps to productionize machine learning.

REPORT

Kisaco leadership chart on ML lifecycle solutions 2020-2021

Algorithmia participated in a rigorous independent analyst review of six machine learning lifecycle enterprise solutions and was recognized as Market Leader.

WHITEPAPER

Building versus buying an ML management platform

To extract value as soon as possible from AI and maintain a competitive advantage in your industry, purchasing an off-the-shelf platform that fits into your existing workflow is the best answer. Let us show you why.

Chart book

2020 enterprise trends in the new normal

We surveyed AI/ML business leaders at large enterprises on how they are changing their ML initiatives in reaction to the pandemic.

WHITEPAPER

Measures and indicators for machine learning maturity

Learn how an enterprise organization can achieve ML success and how it should measure and maintain an ongoing machine learning program.

WHITEPAPER

Solving enterprise machine learning's five main challenges

Learn about the five common challenges that occur on the road from R&D to ROI for machine learning—and what you can do to overcome them.

WHITEPAPER

ML infrastructure part 1: Seven challenges of ML for DevOps

In the first of our five-part series, learn about the seven most common challenges of ML for DevOps, and what you can do to overcome them.

WHITEPAPER

ML infrastructure part 2: Model deployment

In the second of our five-part series, discover the best practices for ensuring that efforts to deploy machine learning models are successful.

WHITEPAPER

Machine learning infrastructure part 3: Connectivity

Machine learning projects start to generate value only after workflows connect with data and related management systems. Learn more in the third of our five-part series.

WHITEPAPER

ML infrastructure part 4: Serving and scaling

In the fourth of our five-part series, learn how to overcome serving and scaling challenges up front.

WHITEPAPER

ML infrastructure part 5: Management and governance

In the last of our five-part series, learn about the critical importance of governance for machine learning—and how to approach it.

WHITEPAPER

Deploying machine learning with serverless microservices

Microservices are an emerging software architecture that’s gaining traction with large enterprises and also smaller, early-stage companies. Learn how they can be used for your machine learning initiatives.

WHITEPAPER

Pipelining machine learning models together

Learn about pipelining, the art of splitting up your machine learning workflows into reusable, modular parts in order to build more powerful software over time.

WHITEPAPER

Productionizing machine learning models with containers

Learn how the paradigm of containerization can work for your data science teams as they build out a plan for productionizing machine learning.

Report

2020 state of enterprise machine learning

We surveyed more than 740 enterprise business leaders in the fall of 2019 to understand how companies of all sizes are developing their data science and machine learning capabilities into 2020.

Report

2018 state of enterprise machine learning

In the fall of 2018, we conducted a survey of more than 500 senior decision-makers across companies of all sizes in various industries to understand how machine learning is developing in the marketplace.