Category: artificial intelligence

The socially responsible revolution: how ESG can impact your business


This post is by Georgian Team from Georgian


In our changing world, environmental, social, and governance (ESG) management and investing are becoming increasingly important to both businesses and individuals within them. Investors are also beginning to consider how companies are integrating ESG into their strategies and operations. If incorporated properly, this means that ESG practices can be a value creation lever for your company.

In a recent CoLab Connect event, we chatted with Chase Wisnowski, vice president at Malk Partners, and Kelsey Jarrett, engagement manager at Malk Partners, about the growing importance of ESG in any business, and how it can elevate your company.

So… what is ESG?

ESG stands for environmental, social, and governance factors that are directly related to a company’s internal operations,” explains Chase. He adds that ESG can include things like evaluating diversity, equity and inclusion in your company, your employee engagement, the actual environmental footprint of your operations, as well as how you’re structuring your compliance program or business ethics program to align with best practices and mitigate the risks that your business faces, given the impact of your business operations.

Social responsibility, or the idea that a company should play a positive role in its community, is tightly woven together with ESG practices and can have a considerable impact on how your business is perceived and embraced. For example, “maybe [a company] is creating plant-based meats that are way less energy-intensive and better to the environment relative to traditional burgers,” says Chase, adding that this could have a (Read more...)

Form vs. Meaning


This post is by Om Malik from On my Om


There is a fundamental difference between form and meaning. Form is the physical structure of something, while meaning is the interpretation or concept that is attached to that form. For example, the form of a chair is its physical structure – four legs, a seat, and a back. The meaning of a chair is that it is something you can sit on.This distinction is important when considering whether or not an AI system can be trained to learn semantic meaning.

Scott Aaronson

How To Find the Best AI Opportunities in Your Business


This post is by Jessica Galang from Georgian


Building an AI product can seem daunting if you don’t know where to start. How do I attract the right people to build a working product or service? Do I have the right data? How do I know if using AI is the right way to solve my problems?

These are some of the questions answered at the latest CoLab Connect event on getting AI-ready. During a panel moderated by David Tingle, R&D Ops at Georgian, Georgian Head of Applied Research Parinaz Sobhani and DefenseStorm co-founder and chief product officer Edgardo Nazario explained how to over common challenges you’ll encounter when building an AI product. 

Prioritize AI opportunities

Let’s make sure that it’s not a solution looking for a problem, but rather we identify business problems that we see.

Edgardo Nazario

You can’t effectively apply AI to your business without knowing what problem you’re trying to solve. Similar to product development in any business, you’ll have to do the work — customer calls, discovery, evaluation of pain points — to figure out the right business problem to solve. 

In Georgian’s AI white paper, principles 1 and 2 explain this process of finding opportunities in more detail, which includes mapping your business processes, understanding your desired outcomes and prioritizing the opportunities based on value and effort. 

Sometimes, the highest value opportunities aren’t the best ones to pursue first if the effort is too high — you want to aim for something that can be delivered in the next six (Read more...)

Audit Your Data — The Right Way


This post is by Jessica Galang from Georgian


So you want to use machine learning to build better products and improve your user’s experience, but knowing where to start can be difficult. 

In our Principles of Applied AI whitepaper, we explain the importance of properly identifying opportunities for integrating AI into your product. To do that, you need to review the business processes that you enable for your customer and prioritize improving the ones that will deliver the most benefit for you and your customers (see page 7 of our whitepaper for more on this!).


After you shortlist a few opportunities with the highest impact, you need to start thinking about the data you need to build those AI-enabled systems. We asked our Head of Applied Research, Parinaz Sobhani, for her top tips on data audits and making sure you have the high-quality data needed to avoid a “garbage-in, garbage-out” scenario.

If you want to learn more about evaluating AI opportunities, data audits and get a chance to get some feedback from Parinaz, she’ll be leading the session at our CoLab Connect on March 15. Reach out to conor@georgian.io to reserve your spot! 

Start with ranking opportunities based on their values rather than availability of data

After you’ve identified and ranked opportunities based on customer value, you then need to think about the level of effort required to bring those opportunities to life.

“It starts with the opportunity identification, prioritization and product strategy rather than the data audit,” said Parinaz. Following that, “The second step might be, (Read more...)

Audit Your Data — The Right Way


This post is by Jessica Galang from Georgian


So you want to use machine learning to build better products and improve your user’s experience, but knowing where to start can be difficult. 

In our Principles of Applied AI whitepaper, we explain the importance of properly identifying opportunities for integrating AI into your product. To do that, you need to review the business processes that you enable for your customer and prioritize improving the ones that will deliver the most benefit for you and your customers (see page 7 of our whitepaper for more on this!).


After you shortlist a few opportunities with the highest impact, you need to start thinking about the data you need to build those AI-enabled systems. We asked our Head of Applied Research, Parinaz Sobhani, for her top tips on data audits and making sure you have the high-quality data needed to avoid a “garbage-in, garbage-out” scenario.

If you want to learn more about evaluating AI opportunities, data audits and get a chance to get some feedback from Parinaz, she’ll be leading the session at our CoLab Connect on March 15. Reach out to conor@georgian.io to reserve your spot! 

Start with ranking opportunities based on their values rather than availability of data

After you’ve identified and ranked opportunities based on customer value, you then need to think about the level of effort required to bring those opportunities to life.

“It starts with the opportunity identification, prioritization and product strategy rather than the data audit,” said Parinaz. Following that, “The second step might be, (Read more...)

Here’s where MLOps is accelerating enterprise AI adoption



A version of this blog appeared in TechCrunch: Here’s where MLOps is accelerating enterprise AI adoption

Why is MLOps important?

In early 2000s most business-critical software was hosted in privately-run data centers. Initially skeptical, enterprises moved their critical applications to the cloud. DevOps fueled this cloud adoption as it gave decision makers a sense of control over business-critical applications hosted outside their own data centers. Today, enterprises are in a similar phase of trial and acceptance of ML in their production environments and MLOps is acting as an accelerator. Similar to cloud-native startups, many startups today are ML-native and offer differentiated products to their customers. But a vast majority of large and mid-size enterprises are either in trial phase or just struggling to productionize functioning models. Here are some key challenges that MLOps can help with:

Cross-team ML collaboration is a must but tough to accomplish

A model may be as simple as a churn-prediction model, or as complex as the one determining Uber or Lyft pricing between San Jose and San Francisco. It is an incredibly complex task to create a model and enable teams to benefit from it. In addition to a large amount of labeled historic data to train the model, multiple teams need to coordinate to continuously monitor the models for performance degradation. There are three different core roles that are involved in ML modeling but each one has different motivations and incentives:

1. Data Engineers: these are trained engineers who excel at grabbing data from multiple (Read more...)

A Discussion on ML and AI Opportunities and Gaps at CoLab Day 2021


This post is by Georgian Team from Georgian


At our recent CoLab Day event, Parinaz Sobhani, Georgian’s Head of Applied Research, hosted a session with special guest Zahi Karam, Vice President of Data Science, Bluecore and two Georgian Applied Research Scientists — Azin Asgarian and Akshay Budhkar — to share their insights to help CoLab companies with their machine learning and artificial intelligence challenges and identify new opportunities.

Where to Start Your AI or ML Journey

We began the session by asking where a company should start their AI or ML journeys.

My advice is don’t start with data science.

For companies that aren’t AI-first, but want to incorporate AI and ML to improve their customer experience and use it for differentiation, one lesson from Bluecore is to not start with data science. In many cases, companies are able to build extreme value with just heuristics (rule-based technique) — no machine learning required. So once the business is ready to start building out a data science team, they have already created the data foundation and are able to build on top of that. 

In the beginning, the main priority is the data pipeline and capturing as much data as possible. Focus more on how you structure the data and how you collect it because you don’t yet know what data is going to be most useful. 

When you start building your first model, start with the simplest thing that you can package in a proof-of-concept with one or two clients before you invest in building out the full (Read more...)

Agile AI at Georgian — Part 5: A Roundup of our Favorite MLOps Tools


This post is by Jason Brenier from Georgian


Welcome back to Agile AI at Georgian, where I share lessons learned about how to adapt agile methodologies for AI products.

In previous installments, we’ve talked about finding your project’s North Star, motivating your team, mastering experimental design, and managing the data lifecycle. Today, we’re going to dive into the world of MLOps – an area where Georgian’s R&D team has recently been spending a lot of time. To write this post, I spoke with our engineers, data scientists, and product managers about their insights from working on Georgian’s internal AI products and collaborating with our portfolio companies.

So what is MLOps, anyway? As AI becomes more mature, teams are applying DevOps procedures and ideas to make the development and deployment of machine learning products more predictable and efficient. However, there are some key differences between traditional DevOps and MLOps.

As Diego Huang, Lead Engineer on Georgian’s engineering team puts it, “MLOps is still a fairly new field, and people are still figuring out the ‘best practices.’ There is not yet a cookie-cutter template that one can just follow like in the more mature field of DevOps. Therefore, we (engineers) need to understand how ML works and really think about what might break when the rubber hits the road.”

Choosing your MLOps Tools

Today, there’s a large and growing ecosystem of MLOps tools. Choosing the right ones can be a challenge. Here are some factors that we’ve found are important to consider:

A Guide To Scaling Your AI Product


This post is by Jessica Galang from Georgian


Artificial intelligence (AI) is revolutionizing business — but that doesn’t make it valuable for all organizations looking to generate value and drive growth. The challenge is knowing when and how to use AI wisely. 

Dr. Jerrold Jackson is VP and Head of Machine Learning and Data at Exos, a human performance and wellness company, where he is using AI to deliver personalization and optimize customer experience in real time. At Georgian’s ScaleTech Conference, he shared his insights on the fastest and most effective way to create value by adding AI capabilities to your products and services. 

Drawing on his experience designing and implementing data strategies at Exos and in several other fast-moving industries, Jerrold provided real-life examples of when and how to scale AI in your organization, plus tangible ways to evaluate success. Below, we round up some of the top takeaways from his talk, like the right way to evaluate AI opportunities and scaling your AI product. 

Is AI a good fit?

AI can help companies deliver tremendous value to their customers but it isn’t the right tool for everyone. So how do you evaluate whether AI is the right solution at the right time? Jerrold said the answer is often driven by data. 

AI is data-hungry and requires constant feeding, so it is best applied in industries where data is plentiful and accumulates quickly, such as fintech or adtech, where it can be used to train and refine machine-learning algorithms.  

“Many growth-stage companies don’t naturally have (Read more...)

Deploying GPT-Neo’s 1.3 Billion Parameter Language Model Using AWS SageMaker


This post is by Akshay Budhkar from Georgian


Language models are now capable of generating text that appears natural to humans. According to [Brown et al. 2020], humans only detect whether GPT-3 — one of the largest language models developed by OpenAI — generated short news articles accurately 52% of the time. Unfortunately, GPT-3 is not open-sourced (as of September 2021). EleutherAI has attempted to reproduce GPT-3 through GPT-Neo, a language model that has a similar architecture to GPT-3. At Georgian, we are very excited about the progress on large-scale language models and we compared GPT-3 and GPT-Neo in our previous blog post.

Many state-of-the-art NLP models are easily accessible through HuggingFace’s popular Transformers toolkit. Recently, HuggingFace and Amazon SageMaker reached a strategic partnership which, since GPT-Neo is available on HuggingFace, makes the deployment of GPT-Neo easier. In this blog post, we will walk through how we created a user interface for GPT-Neo 1.3B (containing 1.3 billion parameters) using Amazon SageMaker, and show how SageMaker’s auto-scaling helps us handle the dynamic traffic to our user interface.

You can see a demo of our user interface in the video above. Our user interface looks similar to the one that kiel.ai built. This is because we both used streamlit, a very convenient open-source framework for displaying data on web applications. A key difference between our UI and kiel.ai’s UI is that they deployed GPT-Neo using Docker, whereas we used Amazon SageMaker, which allows us to scale the number of compute instances hosting the GPT-Neo (Read more...)