From Software Engineering to Production Machine Learning

Modern AI and Machine Learning roles require more than training a model or calling an API.

Engineers are increasingly expected to understand the complete lifecycle of AI systems—from data and model development to distributed training, LLM inference, deployment, observability, reliability, latency, and cost optimization.

Solely's AI & Machine Learning Engineer Program is designed around those real-world engineering responsibilities.

Throughout the program, you'll build production-oriented AI systems using technologies such as PyTorch, JAX, vLLM, Ray, Triton, TensorRT-LLM, Kubernetes, LangGraph, CUDA, and modern observability tools.

Artificial Intelligence and Machine Learning Skill Requirements

As the applications of artificial intelligence increase across all major industries, tech professionals with concentrated AI and machine learning experience are more in demand than ever before. The PwC 2024 Global AI Jobs Barometer reports that the demand for AI jobs is growing 3.5x faster than all other jobs globally. Specialization in this rapidly growing field may require different skills—depending on your unique professional experience and goals—but successful AI & machine learning professionals are typically proficient in many of the following concepts and technologies.

• Data science processes, data wrangling, data exploration, and hypothesis building and testing

• Applied data science with Python

• Machine learning models and accuracy metrics

• Deep learning concepts and their relevant applications and tools, such as Keras, Python, and TensorFlow

• Text-to-speech conversion with automated speech recognition

• Deep neural networks

• Conversational AI models, prompt engineering, and GenAI tools like ChatGPT, Microsoft BingAI, Google Gemini, Anthropic Claude, and GPT4all

Learn more about who our bootcamp is for and how to build or expand a lucrative data and AI career below.

What You’ll Learn in the SolelyBootcamp AI & Machine Learning Program

The part-time Artificial Intelligence and Machine Learning Bootcamp is divided into 5 units. Starting with programming fundamentals, you’ll quickly move into more advanced AI and machine learning concepts—In each of the following units, you will have the opportunity to work on a project based on a real-world scenario, helping you to apply your skills in practical applications..

• Pre-work

  • Get access to pre-work materials
    • Access to pre-work material to get you started. It must be completed before the first day of class and, then, join our SolelyBootcamp classroom!
    • AI/MLE Interview Preparation(Algorithm、Coding、ML Fundamentals、ML Design、LLM Design、Behavioral) *All of this content is included in the course.
• Programming & AI Engineering

  • Python3 & Advanced Data Handling
  • Advanced Linux, Git & Command-Line Workflows
  • NumPy & Pandas for Data Analysis
  • AI Training Frameworks(PyTorch, JAX, TensorFlow)
  • Distributed AI Training(PyTorch DDP, FSDP, DeepSpeed, Megatron-LM, Pipeline Parallelism, Tensor Parallelism)
  • AI Inference & Serving(vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve, FastAPI, gRPC)
  • GPU Programming & AI Runtime(CUDA, CANN, NCCL, Mixed Precision, BF16, FP8, CUDA Graphs)
  • LLM Inference Optimization(KV Cache, PagedAttention, Continuous Batching, Speculative Decoding)
  • Search & Retrieval Systems(Vector Search, ANN, Faiss)
  • Modern Data Storage(PostgreSQL, Redis, Neo4j, Feature Store)
• ML & Deep Learning Advanced

  • Traditional Machine Learning(Linear Models, Tree Models, Clustering, Dimensionality Reduction)
  • Deep Learning with PyTorch
  • Transformer Architecture & Modern LLMs
  • LLM Fine-tuning(LoRA, QLoRA, PEFT)
  • Reinforcement Learning & RLHF Fundamentals
  • Build End-to-End ML Pipelines(Data → Training → Evaluation → Inference → Deployment)
  • ML Platform Engineering(Model Registry, Feature Store, Experiment Tracking, Data Validation)
  • Production Model Evaluation(OpenAI Evals, LM Harness, A/B Testing, Human Evaluation)
  • Model Serving & Monitoring(TorchServe, FastAPI, Redis, Prometheus, Grafana)
  • Learn How Production MLE Teams Design, Deploy & Scale AI Systems
• Specialisation NLP & GenAI

  • Natural Language Processing
  • Word Embeddings
  • Transformer & Attention Mechanisms
  • Generative AI Fundamentals
  • LLMs(GPT, Claude, Llama)
  • Agent Engineering(LangGraph, MCP, Tool Calling, Multi-Agent Systems)
  • Prompt Engineering(Structured Outputs, Function Calling)
  • RAG Systems(Vector Database, Hybrid Search, Re-ranking)
  • Knowledge Graphs
  • AI Evaluation & Guardrails
  • AI Frameworks(LangChain, LangGraph, LlamaIndex, Pydantic AI, LangSmith)
  • Cloud AI Platforms(AWS SageMaker, Google Cloud Vertex AI)
• Deployment and MLOps Tools

  • API Development(FastAPI, gRPC)
  • Containerization(Docker)
  • Production Kubernetes(Kubernetes, Kubeflow)
  • Workflow Orchestration(Apache Airflow, Ray)
  • CI / CD Pipelines
  • ML Observability(Prometheus, Grafana, OpenTelemetry)
  • Production Monitoring(Model Drift, Data Drift, Performance Monitoring)
  • Scalable AI Infrastructure(Autoscaling, Load Balancing, Distributed Serving)
  • Developer Experience(TDD, GitHub PR Workflow, Code Reviews, Internal Tooling)

AI & Machine Learning Bootcamp Tools & Technologies

Throughout this program, you’ll learn to use a range of in-demand digital tools to build practical, hands-on AI and machine learning skills. Some of the tools you’ll develop proficiency in include:

AI & Machine Learning Bootcamp Projects

As a part of this program, you’ll have opportunities to apply your skills in industry-relevant projects to solve real-world challenges. These projects will help validate your proficiency with AI and machine learning tools and technologies, plus you can feature them in your professional portfolio to showcase to potential employers. Here are some examples (projects based on product content developed by senior AI/ML engineers at major companies like FAANG. For more details, you can participate in SolelyBootcamp 1-on-1 trial lesson):

• Google Consumer Decision Agent Platform

  • Core Competencies: Stateful system、Policy/Trade-off、Failure&rollback strategy、Offline evaluation
    Technical Highlights: Python/JVM/Go、LLM + Tool Calling、Rule Engine、Offline RL/Imitation Learning、Logging + Monitoring + Audit Trail、Constraint optimization、Causal analysis、Long-horizon planning
• NVIDIA Research — Instant NuRec

  • Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation
• OpenAI LLM Infra - OptimizingModel Serving for Low Latency

  • Core Competencies: Inference optimization + system performance scheduling、Simulate OpenAI production; build high-concurrency, low-latency inference architecture
    Technical highlights: vLLM + Ray Serve +Elastic Batching+ GPU Utilization Tracker
• Token-Aware LLM API Platform for Anthropic-style Infra

  • Company: OpenAl、Salesforce
    Technical highlights: KV-cache reuse, batch inference, and latency tracking、Canary rollout, auto-rollback, and multi-version prompt registry、Use RAG pipeline built with Azure, LangChain, and FAISS、Feedback-signal logging for future DPO / RLHF feedback integration
• AgentHub: Enterprise-Grade Multi-Agent Collaboration Platform

  • Company: Meta、NVIDIA
    Technical highlights: LangGraph v0.3 + CrewAI + AutoGen — dynamic role assignment (agents can change roles at runtime, e.g., Researcher ↔ Reviewer)、Context Fabric — vector DB + temporal store; compression via Graph-of-Thoughts + semantic sketch; cross-modal context linking (docs, images, task tables) to save tokens、Inference runtime — vLLM + FlashAttention-3 + speculative decoding; KV-cache multiplexing; hot-pluggable LoRA adapters; serving: Triton Inference Server + TensorRT-LLM、Observability & debugging — ExecutionGraph visualization; OpenTelemetry → ClickHouse; single-agent and end-to-end replay testing

Capstone Project

The capstone project is the culmination of all the previous skills taught in the bootcamp. You will choose a data analysis subject that interests you, build and fine-tune a model with AI techniques, and present your findings.

AI & Machine Learning Capstone Project Overview

Watch AI & machine learning instructor, Janet Flores, discusses a capstone project example that involves creating a recommender system.

AI & Machine Learning Bootcamp Student Project: Autonomous Driving Vehicles

Watch one capstone project example from an AI & Machine Learning bootcamp graduate that focuses on autonomous driving vehicle accidents and object detection.

Career Success Program for the AI & Machine Learning Bootcamp

Students will gain valuable insight into how to build a successful career in the field of AI and machine learning. Throughout the bootcamp experience, students may access live workshops, office hours, and asynchronous modules to help build a job search toolkit—which includes an optimized resume and LinkedIn profile.

Following successful completion of the program, bootcamp graduates can choose to opt into the SolelyBootcamp Career Success Program and access up to a year of 1:1 personalized career coaching and guidance in:

• Growing your professional network

• Resume and LinkedIn profile optimization

• Interview and assessment prep

• Salary negotiation workshops

• And much more!

You’ll also graduate from the program with a portfolio of work that demonstrates your ability to solve real business problems for real companies.

SolelyBootcamp Graduates Get Hired

SolelyBootcamp grads have landed jobs with some of the world's leading companies.