Many companies are attempting to speed up the delivery of their machine learning (ML) projects by creating platforms. While a few have succeeded, some have experienced significant failures, and most have ended up somewhere in the middle. This can happen when they address MLOps without first addressing their organizational structure and operating model. In this article, we will explore common pitfalls enterprises encounter when building ML platforms and provide solutions to help overcome these obstacles. We will tackle five common pitfalls enterprises face when getting their platform up and running and propose prescriptive solutions for each. To simplify the language, we will use the term "you" to refer to the team responsible for building and maintaining the platform.

Manasi Vartak is founder and CEO of Verta, a Palo Alto-based provider of solutions for Operational AI and ML Model Management. Technology research firm Gartner, Inc. has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. The reasons often cited for the high failure rate include poor scope definition, bad training data, organizational inertia, lack of process change, mission creep and insufficient experimentation. To this list, I would add another reason that I have seen many organizations struggle to achieve value from their AI projects. Companies often have invested heavily in building data science teams to create innovative ML models.

In a recently published article, Team topology for machine learning, I suggested that organizations in their Machine Learning (ML) journey should adopt a team topology consisting of four types of teams as illustrated in Figure 1. The team types are Stream-aligned ML, ML enabling, Data/Infrastructure Subsystem, and ML platform teams. To get an overview of these teams, please check out the article. In this article, we do a deep dive into ML platform teams. In particular, we explain the following points in more detail.

In a recently published article, Team topology for machine learning, I suggested that organizations in their Machine Learning (ML) journey should adopt a team topology consisting of four types of teams as illustrated in Figure 1. The team types are Stream-aligned ML, ML enabling, Data/Infrastructure Subsystem, and ML platform teams. To get an overview of these teams, please check out the article. In this article, we do a deep dive into ML platform teams. In particular, we explain the following points in more detail.

Nowadays, Machine Learning (ML) is all in rage worldwide. A lot of companies are adopting ML (or AI or Advanced Analytics or Data-Driven Decision Making) in their current business processes. In this organization, a lot of effort is going towards recruiting ML talents, forming teams, identifying the feature scope of the team. Like many tech organizations, these organizations are also producing monoliths applications, e.g., one platform that includes workflow orchestration, model management, feature management, ML application code, etc. When such an organization realizes that they have ten different teams with seven different architectures, they realize that it is neither scalable nor reasonable to be in such a situation.

Quora is an attractive website that appears like a duet of a program that uses artificial intelligence, data science, and machine learning. It's a simple concept where you can answer the questions asked by others on your favorite topic and vice versa. Lately, the usage of artificial intelligence and machine learning at Quora has grown a lot. It has not only gone deeper with bigger and better models for existing AI and machine learning applications but also has expanded the set of areas where AI and machine learning get used. So how does Quora use artificial intelligence and machine learning?

The beauty of this is that all of the above complexity is buried and can be maintained and updated by the Platform Team, whereas the consumers of the module don't need to worry about any of these things, and only need to be aware of high level concerns such as where does the code lives, what is the model name, and what environment should this run on. How and when the actual infrastructure is provisioned will depend on what kind of Terraform flow is implemented in your organisation. As with the model GitHub template repository, we have also created a slimmed down version of our Terraform module. It is available in our public GitHub profile as well, under the name terraform-aws-ml-model. With these two GirHub repositories, a fully working solution should be deployable to AWS out of the box.

KeepTruckin is on a mission to modernize the trucking industry. With the leading fleet management platform, we are bringing trucks online and fundamentally changing the way freight is moved on our roads. We see our hard work rewarded in tangible ways every day and we believe that intelligence is most powerful when paired with humility. We're motivated by the opportunity to impact and improve every facet of a trillion-dollar industry that touches everyone's lives. KeepTruckin is proud to be a Forbes Cloud 100 company, a 2020 Career-Launching Company by Wealthfront and named a Forbes Best Startup Employer 2020.

This was my second consecutive year attending the O'Reilly Strata Conference in New York City. This year, I was fortunate enough to be the winner of a free pass courtesy of KDnuggets. This is a conference that I have very much enjoyed attending. Fundamentally, it is focused on the intersection of business and data. The event covers a large range of tools, technologies, and techniques in the big data space. It also covers a number of important topics for data-driven businesses such as artificial intelligence, machine learning, data strategy, collaboration, reproducibility, emerging architecture, and building data teams.

This is a great interview with Abhik Banerjee. Abhik is a Staff Data Scientist at Kohl's Technology (at the Kohl's departmental stores) where he leads a team on machine learning and data mining projects. He also provides strategic direction around these areas to senior executives at Kohl's. Abhik has an interesting role because he has to marry online and physical store analytics to provide insights to Kohl's. Abhik received his MS in computer science from the University of Cincinnati in 2012. Welcome to another episode of Flyover Labs. Today we are lucky enough to have Abhik Banerjee with us. And Abhik is a Staff Data Scientist at Kohl's Department Stores where he works on machine learning and data mining projects.