As AI and machine learning (ML) expand into all industries, we explore how Codesmith’s data science programs, from the two-day ML unit to the AI Frontiers program, prepare engineers for these opportunities.
What are ML and AI?
ML, including AI, is a set of techniques that allows machines to learn with new and historical data to make predictions. Whether it’s a predictive model for online shopping, analyzing past purchases to predict potential future ones, or a bank identifying fraudulent transactions based on past data, these predictions turn numbers into words.
When predicting text in an LLM with a sufficiently large data source—with relationships between all those words sufficiently mapped—we’re not just looking at the relationship from one word to another word but to all text on the internet (known as the corpus), and so start to see ‘emergent abilities’.
This means predicting words and phrases that may include mathematical reasoning or even the ability to generate code.
Codesmith is involved with the full range of data science—from predictive analytics to generative AI—with residents going on to work as engineers alongside ML colleagues to full-time data scientists and ML engineers.
In this two-day ML unit, residents are familiarized with ML tools like K-means, classification, and regression algorithms, in addition to the ML language Python, and the vocabulary that will help them interface with ML teams at any company they may join.
The program’s instructor Jared Lewis describes it as “demystifying ML”, showing residents how to set up the environment to run scientific libraries like Jupyter Notebooks and practice problems on Python with linear algebra problems thrown in.
It’s designed to expose graduates who will first become software engineers, not ML engineers. Jared explains that the best way to get involved in ML is to join as a software engineer on teams adjacent to the ML team and slowly start taking on ML tasks.
However, many residents do go on to ML-adjacent roles such as Cat Cheng and Sagar Velagala at Netflix, and David DeStefano at EvoutionIQ.
The DSML Fellowship is a two-month program that prepares graduates for data science and machine learning roles. It’s aimed at academics from PhD, postdocs, and MA graduates looking to transition into the ML industry.
The fellowship’s lectures and units focus on industry best practices, how to model in a commercial context, and Full Stack Data Science —where residents conceive, construct, analyze, and deploy all the necessary aspects of a data product. While elective units include MLOps, mature code bases, open source work, client projects, and LLM generative AI projects.
Graduates of the fellowship, which spans from classical ML work to SOTA LLM development, have received offers from Thomson Reuters, Five Rings Capital, Tower Research Capital, and Guidehouse.
The team behind the DSML fellowship includes Gerard Torrats-Espinosa, a professor at Columbia University, Alex Zai, the author of “Deep Reinforcement Learning in Action” and deep learning engineer at Amazon and Uber, and Jonathan Bechtel, a data scientist who has worked with the Amber Capital, General Assembly, and Advent International.
Parallel, Codesmith’s custom ML training and application development for companies, brings in the most high-caliber talent in software engineering and ML from the Codesmith community.
The Team has provided training and AI-supported application development to clients ranging from the BBC to Unilever.
Given the growing opportunities in ML in the software engineering industry, Codesmith is bringing data engineering minors into our flagship Software Engineering Immersive this year.
The Data Infrastructure and ML Infrastructure add-ons will provide graduates with the ability to contribute to an organization's data management and ML infrastructure, as demand for these skills soars.
The AI Frontiers program is aimed at advanced alumni and non-alumni looking to learn the latest industry approaches and practical applications of frontier ML technology.
This program—covering AI foundations, ML & AI engineering, ML ops, Research & AI Frontiers will help prepare alumni to truly become architects of the future.
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